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An experimental study of animating-based facial image manipulation in online class environments.

In Scientific reports ; h5-index 158.0

Recent advances in artificial intelligence technology have significantly improved facial image manipulation, which is known as Deepfake. Facial image manipulation synthesizes or replaces a region of the face in an image with that of another face. The techniques for facial image manipulation are classified into four categories: (1) entire face synthesis, (2) identity swap, (3) attribute manipulation, and (4) expression swap. Out of them, we focus on expression swap because it effectively manipulates only the expression of the face in the images or videos without creating or replacing the entire face, having advantages for the real-time application. In this study, we propose an evaluation framework of the expression swap models targeting the real-time online class environments. For this, we define three kinds of scenarios according to the portion of the face in the entire image considering actual online class situations: (1) attendance check (Scenario 1), (2) presentation (Scenario 2), and (3) examination (Scenario 3). Considering the manipulation on the online class environments, the framework receives a single source image and a target video and generates the video that manipulates a face of the target video to that in the source image. To this end, we select two models that satisfy the conditions required by the framework: (1) first order model and (2) GANimation. We implement these models in the framework and evaluate their performance for the defined scenarios. Through the quantitative and qualitative evaluation, we observe distinguishing properties of the used two models. Specifically, both models show acceptable results in Scenario 1, where the face occupies a large portion of the image. However, their performances are significantly degraded in Scenarios 2 and 3, where the face occupies less portion of the image; the first order model causes relatively less loss of image quality than GANimation in the result of the quantitative evaluation. In contrast, GANimation has the advantages of representing facial expression changes compared to the first order model. Finally, we devise an architecture for applying the expression swap model to the online video conferencing application in real-time. In particular, by applying the expression swap model to widely used online meeting platforms such as Zoom, Google Meet, and Microsoft Teams, we demonstrate its feasibility for real-time online classes.

Park Jeong-Ha, Lim Chae-Yun, Kwon Hyuk-Yoon

2023-Mar-22

Dermatology

Role of the Microbiome in Immunotherapy of Melanoma.

In Cancer journal (Sudbury, Mass.)

Novel immunotherapeutics for advanced melanoma have drastically changed survival rates and management strategies in recent years. Immune checkpoint inhibitors have emerged as efficacious agents for some patients but have not been proven to be as beneficial in other patient cohorts. Recent investigation into this observation has implicated the gut microbiome as a potential immunomodulator in regulating patient response to therapy. Numerous studies have provided evidence for this link. Bacterial colonization patterns have been associated with therapeutic outcomes, under the notion that favorable commensal organisms improve host immune response. This review aims to report the most recent and pertinent findings related to the relationship between gut microbial communities and melanoma therapy efficacy. This article also highlights the emerging frontier of artificial intelligence in its application regarding patient microbial composition evaluation, predictive models for therapy response, and recommendations for the future of probiotics and dietary interventions to optimize melanoma survival and outcomes.

Jiminez Victoria, Yusuf Nabiha

Radiology

Transformer Performance for Chemical Reactions: Analysis of Different Predictive and Evaluation Scenarios.

In Journal of chemical information and modeling

The prediction of chemical reaction pathways has been accelerated by the development of novel machine learning architectures based on the deep learning paradigm. In this context, deep neural networks initially designed for language translation have been used to accurately predict a wide range of chemical reactions. Among models suited for the task of language translation, the recently introduced molecular transformer reached impressive performance in terms of forward-synthesis and retrosynthesis predictions. In this study, we first present an analysis of the performance of transformer models for product, reactant, and reagent prediction tasks under different scenarios of data availability and data augmentation. We find that the impact of data augmentation depends on the prediction task and on the metric used to evaluate the model performance. Second, we probe the contribution of different combinations of input formats, tokenization schemes, and embedding strategies to model performance. We find that less stable input settings generally lead to better performance. Lastly, we validate the superiority of round-trip accuracy over simpler evaluation metrics, such as top-k accuracy, using a committee of human experts and show a strong agreement for predictions that pass the round-trip test. This demonstrates the usefulness of more elaborate metrics in complex predictive scenarios and highlights the limitations of direct comparisons to a predefined database, which may include a limited number of chemical reaction pathways.

Jaume-Santero Fernando, Bornet Alban, Valery Alain, Naderi Nona, Vicente Alvarez David, Proios Dimitrios, Yazdani Anthony, Bournez Colin, Fessard Thomas, Teodoro Douglas

2023-Mar-23

Pathology

Learning to predict RNA sequence expressions from whole slide images with applications for search and classification.

In Communications biology

Deep learning methods are widely applied in digital pathology to address clinical challenges such as prognosis and diagnosis. As one of the most recent applications, deep models have also been used to extract molecular features from whole slide images. Although molecular tests carry rich information, they are often expensive, time-consuming, and require additional tissue to sample. In this paper, we propose tRNAsformer, an attention-based topology that can learn both to predict the bulk RNA-seq from an image and represent the whole slide image of a glass slide simultaneously. The tRNAsformer uses multiple instance learning to solve a weakly supervised problem while the pixel-level annotation is not available for an image. We conducted several experiments and achieved better performance and faster convergence in comparison to the state-of-the-art algorithms. The proposed tRNAsformer can assist as a computational pathology tool to facilitate a new generation of search and classification methods by combining the tissue morphology and the molecular fingerprint of the biopsy samples.

Alsaafin Areej, Safarpoor Amir, Sikaroudi Milad, Hipp Jason D, Tizhoosh H R

2023-Mar-22

Pathology

A systematic evaluation of deep learning methods for the prediction of drug synergy in cancer.

In PLoS computational biology

One of the main obstacles to the successful treatment of cancer is the phenomenon of drug resistance. A common strategy to overcome resistance is the use of combination therapies. However, the space of possibilities is huge and efficient search strategies are required. Machine Learning (ML) can be a useful tool for the discovery of novel, clinically relevant anti-cancer drug combinations. In particular, deep learning (DL) has become a popular choice for modeling drug combination effects. Here, we set out to examine the impact of different methodological choices on the performance of multimodal DL-based drug synergy prediction methods, including the use of different input data types, preprocessing steps and model architectures. Focusing on the NCI ALMANAC dataset, we found that feature selection based on prior biological knowledge has a positive impact-limiting gene expression data to cancer or drug response-specific genes improved performance. Drug features appeared to be more predictive of drug response, with a 41% increase in coefficient of determination (R2) and 26% increase in Spearman correlation relative to a baseline model that used only cell line and drug identifiers. Molecular fingerprint-based drug representations performed slightly better than learned representations-ECFP4 fingerprints increased R2 by 5.3% and Spearman correlation by 2.8% w.r.t the best learned representations. In general, fully connected feature-encoding subnetworks outperformed other architectures. DL outperformed other ML methods by more than 35% (R2) and 14% (Spearman). Additionally, an ensemble combining the top DL and ML models improved performance by about 6.5% (R2) and 4% (Spearman). Using a state-of-the-art interpretability method, we showed that DL models can learn to associate drug and cell line features with drug response in a biologically meaningful way. The strategies explored in this study will help to improve the development of computational methods for the rational design of effective drug combinations for cancer therapy.

Baptista Delora, Ferreira Pedro G, Rocha Miguel

2023-Mar-23

Pathology

The value of artificial intelligence in the diagnosis of lung cancer: A systematic review and meta-analysis.

In PloS one ; h5-index 176.0

Lung cancer is a common malignant tumor disease with high clinical disability and death rates. Currently, lung cancer diagnosis mainly relies on manual pathology section analysis, but the low efficiency and subjective nature of manual film reading can lead to certain misdiagnoses and omissions. With the continuous development of science and technology, artificial intelligence (AI) has been gradually applied to imaging diagnosis. Although there are reports on AI-assisted lung cancer diagnosis, there are still problems such as small sample size and untimely data updates. Therefore, in this study, a large amount of recent data was included, and meta-analysis was used to evaluate the value of AI for lung cancer diagnosis. With the help of STATA16.0, the value of AI-assisted lung cancer diagnosis was assessed by specificity, sensitivity, negative likelihood ratio, positive likelihood ratio, diagnostic ratio, and plotting the working characteristic curves of subjects. Meta-regression and subgroup analysis were used to investigate the value of AI-assisted lung cancer diagnosis. The results of the meta-analysis showed that the combined sensitivity of the AI-aided diagnosis system for lung cancer diagnosis was 0.87 [95% CI (0.82, 0.90)], specificity was 0.87 [95% CI (0.82, 0.91)] (CI stands for confidence interval.), the missed diagnosis rate was 13%, the misdiagnosis rate was 13%, the positive likelihood ratio was 6.5 [95% CI (4.6, 9.3)], the negative likelihood ratio was 0.15 [95% CI (0.11, 0.21)], a diagnostic ratio of 43 [95% CI (24, 76)] and a sum of area under the combined subject operating characteristic (SROC) curve of 0.93 [95% CI (0.91, 0.95)]. Based on the results, the AI-assisted diagnostic system for CT (Computerized Tomography), imaging has considerable diagnostic accuracy for lung cancer diagnosis, which is of significant value for lung cancer diagnosis and has greater feasibility of realizing the extension application in the field of clinical diagnosis.

Liu Mingsi, Wu Jinghui, Wang Nian, Zhang Xianqin, Bai Yujiao, Guo Jinlin, Zhang Lin, Liu Shulin, Tao Ke

2023

General

A network-based analysis detects cocaine-induced changes in social interactions in Drosophila melanogaster.

In PloS one ; h5-index 176.0

Addiction is a multifactorial biological and behavioral disorder that is studied using animal models, based on simple behavioral responses in isolated individuals. A couple of decades ago it was shown that Drosophila melanogaster can serve as a model organism for behaviors related to alcohol, nicotine and cocaine (COC) addiction. Scoring of COC-induced behaviors in a large group of flies has been technologically challenging, so we have applied a local, middle and global level of network-based analyses to study social interaction networks (SINs) among a group of 30 untreated males compared to those that have been orally administered with 0.50 mg/mL of COC for 24 hours. In this study, we have confirmed the previously described increase in locomotion upon COC feeding. We have isolated new network-based measures associated with COC, and influenced by group on the individual behavior. COC fed flies showed a longer duration of interactions on the local level, and formed larger, more densely populated and compact, communities at the middle level. Untreated flies have a higher number of interactions with other flies in a group at the local level, and at the middle level, these interactions led to the formation of separated communities. Although the network density at the global level is higher in COC fed flies, at the middle level the modularity is higher in untreated flies. One COC specific behavior that we have isolated was an increase in the proportion of individuals that do not interact with the rest of the group, considered as the individual difference in COC induced behavior and/or consequence of group influence on individual behavior. Our approach can be expanded on different classes of drugs with the same acute response as COC to determine drug specific network-based measures and could serve as a tool to determinate genetic and environmental factors that influence both drug addiction and social interaction.

Petrović Milan, Meštrović Ana, Andretić Waldowski Rozi, Filošević Vujnović Ana

2023

Radiology

Domain-guided data augmentation for deep learning on medical imaging.

In PloS one ; h5-index 176.0

While domain-specific data augmentation can be useful in training neural networks for medical imaging tasks, such techniques have not been widely used to date. Our objective was to test whether domain-specific data augmentation is useful for medical imaging using a well-benchmarked task: view classification on fetal ultrasound FETAL-125 and OB-125 datasets. We found that using a context-preserving cut-paste strategy, we could create valid training data as measured by performance of the resulting trained model on the benchmark test dataset. When used in an online fashion, models trained on this hybrid data performed similarly to those trained using traditional data augmentation (FETAL-125 F-score 85.33 ± 0.24 vs 86.89 ± 0.60, p-value 0.014; OB-125 F-score 74.60 ± 0.11 vs 72.43 ± 0.62, p-value 0.004). Furthermore, the ability to perform augmentations during training time, as well as the ability to apply chosen augmentations equally across data classes, are important considerations in designing a bespoke data augmentation. Finally, we provide open-source code to facilitate running bespoke data augmentations in an online fashion. Taken together, this work expands the ability to design and apply domain-guided data augmentations for medical imaging tasks.

Athalye Chinmayee, Arnaout Rima

2023

General

Programming 3D curved mesosurfaces using microlattice designs.

In Science (New York, N.Y.)

Cellular microstructures form naturally in many living organisms (e.g., flowers and leaves) to provide vital functions in synthesis, transport of nutrients, and regulation of growth. Although heterogeneous cellular microstructures are believed to play pivotal roles in their three-dimensional (3D) shape formation, programming 3D curved mesosurfaces with cellular designs remains elusive in man-made systems. We report a rational microlattice design that allows transformation of 2D films into programmable 3D curved mesosurfaces through mechanically guided assembly. Analytical modeling and a machine learning-based computational approach serve as the basis for shape programming and determine the heterogeneous 2D microlattice patterns required for target 3D curved surfaces. About 30 geometries are presented, including both regular and biological mesosurfaces. Demonstrations include a conformable cardiac electronic device, a stingray-like dual mode actuator, and a 3D electronic cell scaffold.

Cheng Xu, Fan Zhichao, Yao Shenglian, Jin Tianqi, Lv Zengyao, Lan Yu, Bo Renheng, Chen Yitong, Zhang Fan, Shen Zhangming, Wan Huanhuan, Huang Yonggang, Zhang Yihui

2023-Mar-24

Radiology

Current and Advanced Applications of Gadoxetic Acid-enhanced MRI in Hepatobiliary Disorders.

In Radiographics : a review publication of the Radiological Society of North America, Inc

Gadoxetic acid is an MRI contrast agent that has specific applications in the study of hepatobiliary disease. After being distributed in the vascular and extravascular spaces during the dynamic phase, gadoxetic acid is progressively taken up by hepatocytes and excreted to the bile ducts during the hepatobiliary phase. The information derived from the enhancement characteristics during dynamic and hepatobiliary phases is particularly relevant in the detection and characterization of focal liver lesions and in the evaluation of the structure and function of the liver and biliary system. The use of new MRI sequences and advanced imaging techniques (eg, relaxometry, multiparametric imaging, and analysis of heterogeneity), the introduction of artificial intelligence, and the development of biomarkers and radiomic and radiogenomic tools based on gadoxetic acid-enhanced MRI findings will play an important role in the future in assessing liver function, chronic liver disease, and focal liver lesions; in studying biliary pathologic conditions; and in predicting treatment responses and prognosis. © RSNA, 2023 Quiz questions for this article are available in the supplemental material.

Baleato-González Sandra, Vilanova Joan C, Luna Antonio, Menéndez de Llano Rafael, Laguna-Reyes Juan Pablo, Machado-Pereira Diogo M, Bermúdez-Naveira Anaberta, Osorio-Vázquez Iria, Alcalá-Mata Lidia, García-Figueiras Roberto

2023-Apr

General

Computational Approaches for Peroxisomal Protein Localization.

In Methods in molecular biology (Clifton, N.J.)

Computational approaches are practical when investigating putative peroxisomal proteins and for sub-peroxisomal protein localization in unknown protein sequences. Nowadays, advancements in computational methods and Machine Learning (ML) can be used to hasten the discovery of novel peroxisomal proteins and can be combined with more established computational methodologies. Here, we explain and list some of the most used tools and methodologies for novel peroxisomal protein detection and localization.

Anteghini Marco, Martins Dos Santos Vitor A P

2023

Cellular compartments, Machine learning, Peroxisome targeting signal, Sub-organelle localization, Subcellular localization

Oncology

The Application of Artificial Intelligence to Investigate Long-Term Outcomes and Assess Optimal Margin Width in Hepatectomy for Intrahepatic Cholangiocarcinoma.

In Annals of surgical oncology ; h5-index 71.0

BACKGROUND : Intrahepatic cholangiocarcinoma (ICC) is associated with poor long-term outcomes, and limited evidence exists on optimal resection margin width. This study used artificial intelligence to investigate long-term outcomes and optimal margin width in hepatectomy for ICC.

METHODS : The study enrolled patients who underwent curative-intent resection for ICC between 1990 and 2020. The optimal survival tree (OST) was used to investigate overall (OS) and recurrence-free survival (RFS). An optimal policy tree (OPT) assigned treatment recommendations based on random forest (RF) counterfactual survival probabilities associated with each possible margin width between 0 and 20 mm.

RESULTS : Among 600 patients, the median resection margin was 4 mm (interquartile range [IQR], 2-10). Overall, 379 (63.2 %) patients experienced recurrence with a 5-year RFS of 28.3 % and a 5-year OS of 38.7 %. The OST identified five subgroups of patients with different OS rates based on tumor size, a carbohydrate antigen 19-9 [CA19-9] level higher than 200 U/mL, nodal status, margin width, and age (area under the curve [AUC]: training, 0.81; testing, 0.69). The patients with tumors smaller than 4.8 cm and a margin width of 2.5 mm or greater had a relative increase in 5-year OS of 37 % compared with the entire cohort. The OST for RFS estimated a 46 % improvement in the 5-year RFS for the patients younger than 60 years who had small (<4.8 cm) well- or moderately differentiated tumors without microvascular invasion. The OPT suggested five optimal margin widths to maximize the 5-year OS for the subgroups of patients based on age, tumor size, extent of hepatectomy, and CA19-9 levels.

CONCLUSIONS : Artificial intelligence OST identified subgroups within ICC relative to long-term outcomes. Although tumor biology dictated prognosis, the OPT suggested that different margin widths based on patient and disease characteristics may optimize ICC long-term survival.

Alaimo Laura, Moazzam Zorays, Endo Yutaka, Lima Henrique A, Butey Swatika P, Ruzzenente Andrea, Guglielmi Alfredo, Aldrighetti Luca, Weiss Matthew, Bauer Todd W, Alexandrescu Sorin, Poultsides George A, Maithel Shishir K, Marques Hugo P, Martel Guillaume, Pulitano Carlo, Shen Feng, Cauchy François, Koerkamp Bas Groot, Endo Itaru, Kitago Minoru, Kim Alex, Ejaz Aslam, Beane Joal, Cloyd Jordan, Pawlik Timothy M

2023-Mar-23

General

AI Moral Enhancement: Upgrading the Socio-Technical System of Moral Engagement.

In Science and engineering ethics

Several proposals for moral enhancement would use AI to augment (auxiliary enhancement) or even supplant (exhaustive enhancement) human moral reasoning or judgment. Exhaustive enhancement proposals conceive AI as some self-contained oracle whose superiority to our own moral abilities is manifest in its ability to reliably deliver the 'right' answers to all our moral problems. We think this is a mistaken way to frame the project, as it presumes that we already know many things that we are still in the process of working out, and reflecting on this fact reveals challenges even for auxiliary proposals that eschew the oracular approach. We argue there is nonetheless a substantial role that 'AI mentors' could play in our moral education and training. Expanding on the idea of an AI Socratic Interlocutor, we propose a modular system of multiple AI interlocutors with their own distinct points of view reflecting their training in a diversity of concrete wisdom traditions. This approach minimizes any risk of moral disengagement, while the existence of multiple modules from a diversity of traditions ensures pluralism is preserved. We conclude with reflections on how all this relates to the broader notion of moral transcendence implicated in the project of AI moral enhancement, contending it is precisely the whole concrete socio-technical system of moral engagement that we need to model if we are to pursue moral enhancement.

Volkman Richard, Gabriels Katleen

2023-Mar-23

AI socratic interlocutor, Artificial intelligence, Artificial moral agent, Moral enhancement

General

Predicting epileptic seizures based on EEG signals using spatial depth features of a 3D-2D hybrid CNN.

In Medical & biological engineering & computing ; h5-index 32.0

Epilepsy is a recurrent chronic brain disease that affects nearly 75 million people around the world. Therefore, the ability to reliably predict epileptic seizures would be instrumental for implementing interventions to reduce brain injury and improve patients' quality of life. In addition to classical machine learning algorithms and feature engineering methods, the use of electroencephalography (EEG) to predict seizures has gradually become a mainstream trend. Here, we propose a patient-specific method to predict epileptic seizures based on EEG data acquired using spatial depth features of a three-dimensional-two-dimensional hybrid convolutional neural network (3D-2D HyCNN) model. This method facilitates the acquisition of abundant and reliable deep features from multi-channel EEG signals. We first developed a reliable data preprocessing method to reconstruct time-series EEG signals into 3D feature images. Then, the 3D-2D HyCNN model was used to extract correlation features between multiple channels of EEG signals, which are automatically exploited by the network to improve seizure prediction. The method achieved accuracy of 98.43% and 93.11%, sensitivity of 98.58% and 90.98%, and specificity of 96.86% and 92.39% on the CHB-MIT Scalp EEG dataset and the American Epilepsy Society Epilepsy Prediction Challenge dataset, respectively. The results revealed that the new algorithm is reliable. Graphical Abstract A new patient-specific epilepsy prediction approach.

Qi Nan, Piao Yan, Yu Peng, Tan Baolin

2023-Mar-23

3D-2D hybrid CNN, EEG, Epilepsy, Seizure prediction

Surgery

Effectiveness of high-flow nasal cannulae compared with noninvasive positive-pressure ventilation in preventing reintubation in patients receiving prolonged mechanical ventilation.

In Scientific reports ; h5-index 158.0

Many intensive care unit patients who undergo endotracheal extubation experience extubation failure and require reintubation. Because of the high mortality rate associated with reintubation, postextubation respiratory management is crucial, especially for high-risk populations. We conducted the present study to compare the effectiveness of oxygen therapy administered using high-flow nasal cannulae (HFNC) and noninvasive positive pressure ventilation (NIPPV) in preventing reintubation among patients receiving prolonged mechanical ventilation (PMV). This single-center, prospective, unblinded randomized controlled trial was at the respiratory care center (RCC). Participants were randomized to an HFNC group or an NIPPV group (20 patients in each) and received noninvasive respiratory support (NRS) administered using their assigned method. The primary outcome was reintubation within7 days after extubation. None of the patients in the NIPPV group required reintubation, whereas 5 (25%) of the patients in the HFNC group required reintubation (P = 0.047). The 90-day mortality rates of the NIPPV and HFNC groups (four patients [20%] vs. two patients [10%], respectively) did not differ significantly. No significant differences in length of RCC stay, length of hospital stay, time to liberation from NRS, and ventilator-free days at 28-day were identified. The time to event outcome analysis also revealed that the risk of reintubation in the HFNC group was higher than that in the NIPPV group (P = 0.018). Although HFNC is becoming increasingly common as a form of postextubation NRS, HFNC may not be as effective as NIPPV in preventing reintubation among patients who have been receiving PMV for at least 2 weeks. Additional studies evaluating HFNC as an alternative to NIPPV for patients receiving PMV are warranted.ClinicalTrial.gov ID: NCT04564859; IRB number: 20160901R.Trial registration: ClinicalTrial.gov ( https://clinicaltrials.gov/ct2/show/NCT04564859 ).

Tseng Chi-Wei, Chao Ke-Yun, Wu Hsiu-Li, Lin Chen-Chun, Hsu Han-Shui

2023-Mar-22

General

Machine Ethics: Do Androids Dream of Being Good People?

In Science and engineering ethics

Is ethics a computable function? Can machines learn ethics like humans do? If teaching consists in no more than programming, training, indoctrinating… and if ethics is merely following a code of conduct, then yes, we can teach ethics to algorithmic machines. But if ethics is not merely about following a code of conduct or about imitating the behavior of others, then an approach based on computing outcomes, and on the reduction of ethics to the compilation and application of a set of rules, either a priori or learned, misses the point. Our intention is not to solve the technical problem of machine ethics, but to learn something about human ethics, and its rationality, by reflecting on the ethics that can and should be implemented in machines. Any machine ethics implementation will have to face a number of fundamental or conceptual problems, which in the end refer to philosophical questions, such as: what is a human being (or more generally, what is a worthy being); what is human intentional acting; and how are intentional actions and their consequences morally evaluated. We are convinced that a proper understanding of ethical issues in AI can teach us something valuable about ourselves, and what it means to lead a free and responsible ethical life, that is, being good people beyond merely "following a moral code". In the end we believe that rationality must be seen to involve more than just computing, and that value rationality is beyond numbers. Such an understanding is a required step to recovering a renewed rationality of ethics, one that is urgently needed in our highly technified society.

Génova Gonzalo, Moreno Valentín, González M Rosario

2023-Mar-23

Artificial intelligence, Computability, Intentional action, Machine ethics, Moral codes of conduct, Rationality of ethics

General

A device-independent method for the colorimetric quantification on microfluidic sensors using a color adaptation algorithm.

In Mikrochimica acta

A general and adaptable method is proposed to reliably extract quantitative information from smartphone images of microfluidic sensors. By analyzing and processing the color information of selected standard substances, the influence of light conditions, device differences, and human factors could be significantly reduced. Machine learning and multivariate fitting methods were proved to be effective for chroma correction, and a key element was the training of sample size and the fitting form, respectively. A custom APP was developed and validated using a high-sensitivity chromium ion quantification paper chip. The average chroma deviations under different conditions were reduced by more than 75% in RGB color space, and the concentration test error was reduced by more than half compared with the commonly used method. The proposed approach could be a beneficial supplement to existing and potential colorimetry-based detection methods.

Feng Junjie, Jiang Huiyun, Jin Yan, Rong Shenghui, Wang Shiqiang, Wang Haozhi, Wang Lin, Xu Wei, Sun Bing

2023-Mar-23

Colorimetry, Image processing, Microfluidic sensors, Paper-based analytical devices, Smartphone

Public Health

Understanding Public Attitudes and Willingness to Share Commercial Data for Health Research: Survey Study in the United Kingdom.

In JMIR public health and surveillance

BACKGROUND : Health research using commercial data is increasing. The evidence on public acceptability and sociodemographic characteristics of individuals willing to share commercial data for health research is scarce.

OBJECTIVE : This survey study investigates the willingness to share commercial data for health research in the United Kingdom with 3 different organizations (government, private, and academic institutions), 5 different data types (internet, shopping, wearable devices, smartphones, and social media), and 10 different invitation methods to recruit participants for research studies with a focus on sociodemographic characteristics and psychological predictors.

METHODS : We conducted a web-based survey using quota sampling based on age distribution in the United Kingdom in July 2020 (N=1534). Chi-squared tests tested differences by sociodemographic characteristics, and adjusted ordered logistic regressions tested associations with trust, perceived importance of privacy, worry about data misuse and perceived risks, and perceived benefits of data sharing. The results are shown as percentages, adjusted odds ratios, and 95% CIs.

RESULTS : Overall, 61.1% (937/1534) of participants were willing to share their data with the government and 61% (936/1534) of participants were willing to share their data with academic research institutions compared with 43.1% (661/1534) who were willing to share their data with private organizations. The willingness to share varied between specific types of data-51.8% (794/1534) for loyalty cards, 35.2% (540/1534) for internet search history, 32% (491/1534) for smartphone data, 31.8% (488/1534) for wearable device data, and 30.4% (467/1534) for social media data. Increasing age was consistently and negatively associated with all the outcomes. Trust was positively associated with willingness to share commercial data, whereas worry about data misuse and the perceived importance of privacy were negatively associated with willingness to share commercial data. The perceived risk of sharing data was positively associated with willingness to share when the participants considered all the specific data types but not with the organizations. The participants favored postal research invitations over digital research invitations.

CONCLUSIONS : This UK-based survey study shows that willingness to share commercial data for health research varies; however, researchers should focus on effectively communicating their data practices to minimize concerns about data misuse and improve public trust in data science. The results of this study can be further used as a guide to consider methods to improve recruitment strategies in health-related research and to improve response rates and participant retention.

Hirst Yasemin, Stoffel Sandro T, Brewer Hannah R, Timotijevic Lada, Raats Monique M, Flanagan James M

2023-Mar-23

acceptability, commercial data, data, data donation, data sharing, digital, health, loyalty cards, mobile phone, participant recruitment, public, sociodemographic factors

General

Smartphone-Tracked Digital Markers of Momentary Subjective Stress in College Students: Idiographic Machine Learning Analysis.

In JMIR mHealth and uHealth

BACKGROUND : Stress is an important predictor of mental health problems such as burnout and depression. Acute stress is considered adaptive, whereas chronic stress is viewed as detrimental to well-being. To aid in the early detection of chronic stress, machine learning models are increasingly trained to learn the quantitative relation from digital footprints to self-reported stress. Prior studies have investigated general principles in population-wide studies, but the extent to which the findings apply to individuals is understudied.

OBJECTIVE : We aimed to explore to what extent machine learning models can leverage features of smartphone app use log data to recognize momentary subjective stress in individuals, which of these features are most important for predicting stress and represent potential digital markers of stress, the nature of the relations between these digital markers and stress, and the degree to which these relations differ across people.

METHODS : Student participants (N=224) self-reported momentary subjective stress 5 times per day up to 60 days in total (44,381 observations); in parallel, dedicated smartphone software continuously logged their smartphone app use. We extracted features from the log data (eg, time spent on app categories such as messenger apps and proxies for sleep duration and onset) and trained machine learning models to predict momentary subjective stress from these features using 2 approaches: modeling general relations at the group level (nomothetic approach) and modeling relations for each person separately (idiographic approach). To identify potential digital markers of momentary subjective stress, we applied explainable artificial intelligence methodology (ie, Shapley additive explanations). We evaluated model accuracy on a person-to-person basis in out-of-sample observations.

RESULTS : We identified prolonged use of messenger and social network site apps and proxies for sleep duration and onset as the most important features across modeling approaches (nomothetic vs idiographic). The relations of these digital markers with momentary subjective stress differed from person to person, as did model accuracy. Sleep proxies, messenger, and social network use were heterogeneously related to stress (ie, negative in some and positive or zero in others). Model predictions correlated positively and statistically significantly with self-reported stress in most individuals (median person-specific correlation=0.15-0.19 for nomothetic models and median person-specific correlation=0.00-0.09 for idiographic models).

CONCLUSIONS : Our findings indicate that smartphone log data can be used for identifying digital markers of stress and also show that the relation between specific digital markers and stress differs from person to person. These findings warrant follow-up studies in other populations (eg, professionals and clinical populations) and pave the way for similar research using physiological measures of stress.

Aalbers George, Hendrickson Andrew T, Vanden Abeele Mariek Mp, Keijsers Loes

2023-Mar-23

digital biomarker, digital phenotype, machine learning, mobile health, mobile phone, personalized models

General

Acoustic Analysis of Speech for Screening for Suicide Risk: Machine Learning Classifiers for Between- and Within-Person Evaluation of Suicidality.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Assessing a patient's suicide risk is challenging for health professionals because it depends on voluntary disclosure by the patient and often has limited resources. The application of novel machine learning approaches to determine suicide risk has clinical utility.

OBJECTIVE : This study aimed to investigate cross-sectional and longitudinal approaches to assess suicidality based on acoustic voice features of psychiatric patients using artificial intelligence.

METHODS : We collected 348 voice recordings during clinical interviews of 104 patients diagnosed with mood disorders at baseline and 2, 4, 8, and 12 months after recruitment. Suicidality was assessed using the Beck Scale for Suicidal Ideation and suicidal behavior using the Columbia Suicide Severity Rating Scale. The acoustic features of the voice, including temporal, formal, and spectral features, were extracted from the recordings. A between-person classification model that examines the vocal characteristics of individuals cross sectionally to detect individuals at high risk for suicide and a within-person classification model that detects considerable worsening of suicidality based on changes in acoustic features within an individual were developed and compared. Internal validation was performed using 10-fold cross validation of audio data from baseline to 2-month and external validation was performed using data from 2 to 4 months.

RESULTS : A combined set of 12 acoustic features and 3 demographic variables (age, sex, and past suicide attempts) were included in the single-layer artificial neural network for the between-person classification model. Furthermore, 13 acoustic features were included in the extreme gradient boosting machine learning algorithm for the within-person model. The between-person classifier was able to detect high suicidality with 69% accuracy (sensitivity 74%, specificity 62%, area under the receiver operating characteristic curve 0.62), whereas the within-person model was able to predict worsening suicidality over 2 months with 79% accuracy (sensitivity 68%, specificity 84%, area under receiver operating characteristic curve 0.67). The second model showed 62% accuracy in predicting increased suicidality in external sets.

CONCLUSIONS : Within-person analysis using changes in acoustic features within an individual is a promising approach to detect increased suicidality. Automated analysis of voice can be used to support the real-time assessment of suicide risk in primary care or telemedicine.

Min Sooyeon, Shin Daun, Rhee Sang Jin, Park C Hyung Keun, Yang Jeong Hun, Song Yoojin, Kim Min Ji, Kim Kyungdo, Cho Won Ik, Kwon Oh Chul, Ahn Yong Min, Lee Hyunju

2023-Mar-23

artificial intelligence, digital health tool, mood disorder, prediction, screening, suicide, voice analysis

General

Modular Soft Robot with Origami Skin for Versatile Applications.

In Soft robotics

Recent advances in soft robotics demonstrate the requirement of modular actuation to enable the rapid replacement of actuators for maintenance and functionality extension. There remain challenges to designing soft actuators capable of different motions with a consistent appearance for simplifying fabrication and modular connection. Origami structures reshaping along with their unique creases became a powerful tool to provide compact constraint layers for soft pneumatic actuators. Inspired by Waterbomb and Kresling origami, this article presents three types of vacuum-driven soft actuators with a cubic shape and different origami skins, featuring contraction, bending, and twisting-contraction combined motions, respectively. In addition, these modular actuators with diversified motion patterns can be directly fabricated by molding silicone shell and constraint layers together. Actuators with different geometrical parameters are characterized to optimize the structure and maximize output properties after establishing a theoretical model to predict the deformation. Owing to the shape consistency, our actuators can be further modularized to achieve modular actuation via mortise and tenon-based structures, promoting the possibility and efficiency of module connection for versatile tasks. Eventually, several types of modular soft robots are created to achieve fragile object manipulation and locomotion in various environments to show their potential applications.

Jin Tao, Wang Tianhong, Xiong Quan, Tian Yingzhong, Li Long, Zhang Quan, Yeow Chen-Hua

2023-Mar-23

modular actuation, modular soft robot, origami, soft pneumatic actuators

Radiology

Multidelay MR Arterial Spin Labeling Perfusion Map for the Prediction of Cerebral Hyperperfusion After Carotid Endarterectomy.

In Journal of magnetic resonance imaging : JMRI

BACKGROUND : Multidelay arterial spin labeling (ASL) generates time-resolved perfusion maps, which may provide sufficient and accurate hemodynamic information in carotid stenosis.

PURPOSE : To use imaging markers derived from multidelay ASL magnetic resonance imaging (MRI) and to determine the optimal strategy for predicting cerebral hyperperfusion after carotid endarterectomy (CEA).

STUDY TYPE : Prospective observational cohort.

SUBJECTS : A total of 79 patients who underwent CEA for carotid stenosis.

FIELD STRENGTH/SEQUENCE : A 3.0 T/pseudo-continuous ASL with three postlabeling delays of 1.0, 1.57, and 2.46 seconds using fast-spin echo readout.

ASSESSMENT : Cerebral perfusion pressure, antegrade, and collateral flow were scored on a four-grade ordinal scale based on preoperative multidelay ASL perfusion maps. Simultaneously, quantitative hemodynamic parameters including cerebral blood flow (CBF), arterial transit time (ATT), relative CBF (rCBF) and relative ATT (rATT; ipsilateral/contralateral values) were calculated. On the CBF ratio map obtained through dividing postoperative by preoperative CBF map, regions of interest were placed covering ipsilateral middle cerebral artery territory. Three neuroradiologists conducted this procedure. Cerebral hyperperfusion was defined as a CBF ratio >2.

STATISTICAL TESTS : Weighted κ values, independent sample t test, chi-square test, Mann-Whitney U-test, multivariable logistic regression analysis, receiver-operating characteristic curve analysis, and Delong test. Significance level was P < 0.05.

RESULTS : Cerebral hyperperfusion was observed in 15 (19%) patients. Higher blood pressure (odd ratio [OR] = 1.08) and carotid near-occlusion (NO; OR = 7.31) were clinical risk factors for postoperative hyperperfusion. Poor ASL perfusion score (OR = 37.33), decreased CBF (OR = 0.74), prolonged ATT (OR = 1.02), lower rCBF (OR = 0.91), and higher rATT (OR = 1.12) were independent imaging predictors of hyperperfusion. ASL perfusion score exhibited the highest specificity (95.3%), while CBF exhibited the highest sensitivity (93.3%) for the prediction of hyperperfusion. When combined with ASL perfusion score, CBF and ATT, the predictive ability was significantly higher than using blood pressure and NO alone (AUC: 0.98 vs. 0.78).

DATA CONCLUSIONS : Multidelay ASL can accurately predict cerebral hyperperfusion after CEA with high sensitivity and specificity.

EVIDENCE LEVEL : 2 TECHNICAL EFFICACY: Stage 5.

Fan Xiaoyuan, Lai Zhichao, Lin Tianye, Li Kang, Hou Bo, You Hui, Wei Juan, Qu Jianxun, Liu Bao, Zuo Zhentao, Feng Feng

2023-Mar-23

arterial spin labeling, carotid endarterectomy, carotid stenosis, cerebral hyperperfusion syndrome

General

EBi-LSTM: an enhanced bi-directional LSTM for time-series data classification by heuristic development of optimal feature integration in brain computer interface.

In Computer methods in biomechanics and biomedical engineering

Generally, time series data is referred to as the sequential representation of data that observes from different applications. Therefore, such expertise can use Electroencephalography (EEG) signals to fetch data regarding brain neural activities in brain-computer interface (BCI) systems. Due to massive and myriads data, the signals are appealed in a non-stationary format that ends with a poor quality resolution. To overcome this existing issue, a new framework of enhanced deep learning methods is proposed. The source signals are collected and undergo feature extraction in four ways. Hence, the features are concatenated to enhance the performance. Subsequently, the concatenated features are given to probability ratio-based Reptile Search Algorithm (PR-RSA) to select the optimal features. Finally, the classification is conducted using Enhanced Bi-directional Long Short-Term Memory (EBi-LSTM), where the hyperparameters are optimized by PR-RSA. Throughout the result analysis, it is confirmed that the offered model obtains elevated classification accuracy, and thus tends to increase the performance.

Saraswat Mala, Dubey Anil Kumar

2023-Mar-23

Time-series data classification, brain–computer interface, electroencephalography signal, enhanced bi-directional long short-term memory, ensemble feature extraction, probability ratio-based reptile search algorithm

Pathology

Whole Slide Images in Artificial Intelligence Applications in Digital Pathology: Challenges and Pitfalls.

In Turk patoloji dergisi

The use of digitized data in pathology research is rapidly increasing. The whole slide image (WSI) is an indispensable part of the visual examination of slides in digital pathology and artificial intelligence applications; therefore, the acquisition of WSI with the highest quality is essential. Unlike the conventional routine of pathology, the digital conversion of tissue slides and the differences in its use pose difficulties for pathologists. We categorized these challenges into three groups: before, during, and after the WSI acquisition. The problems before WSI acquisition are usually related to the quality of the glass slide and reflect all existing problems in the analytical process in pathology laboratories. WSI acquisition problems are dependent on the device used to produce the final image file. They may be related to the parts of the device that create an optical image or the hardware and software that enable digitization. Post-WSI acquisition issues are related to the final image file itself, which is the final form of this data, or the software and hardware that will use this file. Because of the digital nature of the data, most of the difficulties are related to the capabilities of the hardware or software. Being aware of the challenges and pitfalls of using digital pathology and AI will make pathologists' integration to the new technologies easier in their daily practice or research.

Basak Kayhan, Ozyoruk Kutsev Bengisu, Demir Derya

2023-Mar-23

Dermatology

A call for implementing augmented intelligence in pediatric dermatology.

In Pediatric dermatology

Augmented intelligence (AI), the combination of artificial based intelligence with human intelligence from a practitioner, has become an increased focus of clinical interest in the field of dermatology. Technological advancements have led to the development of deep-learning based models to accurately diagnose complex dermatological diseases such as melanoma in adult datasets. Models for pediatric dermatology remain scarce, but recent studies have shown applications in the diagnoses of facial infantile hemangiomas and X-linked hypohidrotic ectodermal dysplasia; however, we see unmet needs in other complex clinical scenarios and rare diseases, such as diagnosing squamous cell carcinoma in patients with epidermolysis bullosa. Given the still limited number of pediatric dermatologists, especially in rural areas, AI has the potential to help overcome health disparities by helping primary care physicians treat or triage patients.

Issa Christopher J, Reimer-Taschenbrecker Antonia, Paller Amy S

2023-Mar-23

artificial intelligence, convolutional neural networks, deep learning, health disparities, pediatric dermatology

General

A Long-term Consistent Artificial Intelligence and Remote Sensing-based Soil Moisture Dataset.

In Scientific data

The Consistent Artificial Intelligence (AI)-based Soil Moisture (CASM) dataset is a global, consistent, and long-term, remote sensing soil moisture (SM) dataset created using machine learning. It is based on the NASA Soil Moisture Active Passive (SMAP) satellite mission SM data and is aimed at extrapolating SMAP-like quality SM back in time using previous satellite microwave platforms. CASM represents SM in the top soil layer, and it is defined on a global 25 km EASE-2 grid and for 2002-2020 with a 3-day temporal resolution. The seasonal cycle is removed for the neural network training to ensure its skill is targeted at predicting SM extremes. CASM comparison to 367 global in-situ SM monitoring sites shows a SMAP-like median correlation of 0.66. Additionally, the SM product uncertainty was assessed, and both aleatoric and epistemic uncertainties were estimated and included in the dataset. CASM dataset can be used to study a wide range of hydrological, carbon cycle, and energy processes since only a consistent long-term dataset allows assessing changes in water availability and water stress.

Skulovich Olya, Gentine Pierre

2023-Mar-22

Surgery

Breast Tumor Classification using Short-ResNet with Pixel-based Tumor Probability Map in Ultrasound Images.

In Ultrasonic imaging

Breast cancer is the most common form of cancer and is still the second leading cause of death for women in the world. Early detection and treatment of breast cancer can reduce mortality rates. Breast ultrasound is always used to detect and diagnose breast cancer. The accurate breast segmentation and diagnosis as benign or malignant is still a challenging task in the ultrasound image. In this paper, we proposed a classification model as short-ResNet with DC-UNet to solve the segmentation and diagnosis challenge to find the tumor and classify benign or malignant with breast ultrasonic images. The proposed model has a dice coefficient of 83% for segmentation and achieves an accuracy of 90% for classification with breast tumors. In the experiment, we have compared with segmentation task and classification result in different datasets to prove that the proposed model is more general and demonstrates better results. The deep learning model using short-ResNet to classify tumor whether benign or malignant, that combine DC-UNet of segmentation task to assist in improving the classification results.

Wang You-Wei, Kuo Tsung-Ter, Chou Yi-Hong, Su Yu, Huang Shing-Hwa, Chen Chii-Jen

2023-Mar-23

breast cancer, convolutional neural network, deep learning, tumor classification, ultrasound

General

Boundary-oriented Network for Automatic Breast Tumor Segmentation in Ultrasound Images.

In Ultrasonic imaging

Breast cancer is considered as the most prevalent cancer. Using ultrasound images is a momentous clinical diagnosis method to locate breast tumors. However, accurate segmentation of breast tumors remains an open problem due to ultrasound artifacts, low contrast, and complicated tumor shapes in ultrasound images. To address this issue, we proposed a boundary-oriented network (BO-Net) for boosting breast tumor segmentation in ultrasound images. The BO-Net boosts tumor segmentation performance from two perspectives. Firstly, a boundary-oriented module (BOM) was designed to capture the weak boundaries of breast tumors by learning additional breast tumor boundary maps. Second, we focus on enhanced feature extraction, which takes advantage of the Atrous Spatial Pyramid Pooling (ASPP) module and Squeeze-and-Excitation (SE) block to obtain multi-scale and efficient feature information. We evaluate our network on two public datasets: Dataset B and BUSI. For the Dataset B, our network achieves 0.8685 in Dice, 0.7846 in Jaccard, 0.8604 in Precision, 0.9078 in Recall, and 0.9928 in Specificity. For the BUSI dataset, our network achieves 0.7954 in Dice, 0.7033 in Jaccard, 0.8275 in Precision, 0.8251 in Recall, and 0.9814 in Specificity. Experimental results show that BO-Net outperforms the state-of-the-art segmentation methods for breast tumor segmentation in ultrasound images. It demonstrates that focusing on boundary and feature enhancement creates more efficient and robust breast tumor segmentation.

Zhang Mengmeng, Huang Aibin, Yang Debiao, Xu Rui

2023-Mar-23

U-Net, breast tumors, deep learning, image segmentation, ultrasound images

General

ANPELA: Significantly Enhanced Quantification Tool for Cytometry-Based Single-Cell Proteomics.

In Advanced science (Weinheim, Baden-Wurttemberg, Germany)

ANPELA is widely used for quantifying traditional bulk proteomic data. Recently, there is a clear shift from bulk proteomics to the single-cell ones (SCP), for which powerful cytometry techniques demonstrate the fantastic capacity of capturing cellular heterogeneity that is completely overlooked by traditional bulk profiling. However, the in-depth and high-quality quantification of SCP data is still challenging and severely affected by the large numbers of quantification workflows and extreme performance dependence on the studied datasets. In other words, the proper selection of well-performing workflow(s) for any studied dataset is elusory, and it is urgently needed to have a significantly enhanced and accelerated tool to address this issue. However, no such tool is developed yet. Herein, ANPELA is therefore updated to its 2.0 version (https://idrblab.org/anpela/), which is unique in providing the most comprehensive set of quantification alternatives (>1000 workflows) among all existing tools, enabling systematic performance evaluation from multiple perspectives based on machine learning, and identifying the optimal workflow(s) using overall performance ranking together with the parallel computation. Extensive validation on different benchmark datasets and representative application scenarios suggest the great application potential of ANPELA in current SCP research for gaining more accurate and reliable biological insights.

Zhang Ying, Sun Huaicheng, Lian Xichen, Tang Jing, Zhu Feng

2023-Mar-22

cell population identification, comprehensive assessment, parallel computing, protein quantification, single-cell proteomics, trajectory inference

Surgery

Identification of copper metabolism-related subtypes and establishment of the prognostic model in ovarian cancer.

In Frontiers in endocrinology ; h5-index 55.0

BACKGROUND : Ovarian cancer (OC) is one of the most common and most malignant gynecological malignancies in gynecology. On the other hand, dysregulation of copper metabolism (CM) is closely associated with tumourigenesis and progression. Here, we investigated the impact of genes associated with copper metabolism (CMRGs) on the prognosis of OC, discovered various CM clusters, and built a risk model to evaluate patient prognosis, immunological features, and therapy response.

METHODS : 15 CMRGs affecting the prognosis of OC patients were identified in The Cancer Genome Atlas (TCGA). Consensus Clustering was used to identify two CM clusters. lasso-cox methods were used to establish the copper metabolism-related gene prognostic signature (CMRGPS) based on differentially expressed genes in the two clusters. The GSE63885 cohort was used as an external validation cohort. Expression of CM risk score-associated genes was verified by single-cell sequencing and quantitative real-time PCR (qRT-PCR). Nomograms were used to visually depict the clinical value of CMRGPS. Differences in clinical traits, immune cell infiltration, and tumor mutational load (TMB) between risk groups were also extensively examined. Tumour Immune Dysfunction and Rejection (TIDE) and Immune Phenotype Score (IPS) were used to validate whether CMRGPS could predict response to immunotherapy in OC patients.

RESULTS : In the TCGA and GSE63885 cohorts, we identified two CM clusters that differed significantly in terms of overall survival (OS) and tumor microenvironment. We then created a CMRGPS containing 11 genes to predict overall survival and confirmed its reliable predictive power for OC patients. The expression of CM risk score-related genes was validated by qRT-PCR. Patients with OC were divided into low-risk (LR) and high-risk (HR) groups based on the median CM risk score, with better survival in the LR group. The 5-year AUC value reached 0.74. Enrichment analysis showed that the LR group was associated with tumor immune-related pathways. The results of TIDE and IPS showed a better response to immunotherapy in the LR group.

CONCLUSION : Our study, therefore, provides a valuable tool to further guide clinical management and tailor the treatment of patients with OC, offering new insights into individualized treatment.

Zhao Songyun, Zhang Xin, Gao Feng, Chi Hao, Zhang Jinhao, Xia Zhijia, Cheng Chao, Liu Jinhui

2023

OC, Tumor microenvironment, copper metabolism, immunotherapy, machine learning, risk score signature

General

Modeling structure-activity relationships with machine learning to identify GSK3-targeted small molecules as potential COVID-19 therapeutics.

In Frontiers in endocrinology ; h5-index 55.0

Coronaviruses induce severe upper respiratory tract infections, which can spread to the lungs. The nucleocapsid protein (N protein) plays an important role in genome replication, transcription, and virion assembly in SARS-CoV-2, the virus causing COVID-19, and in other coronaviruses. Glycogen synthase kinase 3 (GSK3) activation phosphorylates the viral N protein. To combat COVID-19 and future coronavirus outbreaks, interference with the dependence of N protein on GSK3 may be a viable strategy. Toward this end, this study aimed to construct robust machine learning models to identify GSK3 inhibitors from Food and Drug Administration-approved and investigational drug libraries using the quantitative structure-activity relationship approach. A non-redundant dataset consisting of 495 and 3070 compounds for GSK3α and GSK3β, respectively, was acquired from the ChEMBL database. Twelve sets of molecular descriptors were used to define these inhibitors, and machine learning algorithms were selected using the LazyPredict package. Histogram-based gradient boosting and light gradient boosting machine algorithms were used to develop predictive models that were evaluated based on the root mean square error and R-squared value. Finally, the top two drugs (selinexor and ruboxistaurin) were selected for molecular dynamics simulation based on the highest predicted activity (negative log of the half-maximal inhibitory concentration, pIC₅₀ value) to further investigate the structural stability of the protein-ligand complexes. This artificial intelligence-based virtual high-throughput screening approach is an effective strategy for accelerating drug discovery and finding novel pharmacological targets while reducing the cost and time.

Pirzada Rameez Hassan, Ahmad Bilal, Qayyum Naila, Choi Sangdun

2023

GSK3, QSAR, coronaviruses, machine learning, molecular descriptors

Pathology

A pathomic approach for tumor-infiltrating lymphocytes classification on breast cancer digital pathology images.

In Heliyon

BACKGROUND AND OBJECTIVES : The detection of tumor-infiltrating lymphocytes (TILs) could aid in the development of objective measures of the infiltration grade and can support decision-making in breast cancer (BC). However, manual quantification of TILs in BC histopathological whole slide images (WSI) is currently based on a visual assessment, thus resulting not standardized, not reproducible, and time-consuming for pathologists. In this work, a novel pathomic approach, aimed to apply high-throughput image feature extraction techniques to analyze the microscopic patterns in WSI, is proposed. In fact, pathomic features provide additional information concerning the underlying biological processes compared to the WSI visual interpretation, thus providing more easily interpretable and explainable results than the most frequently investigated Deep Learning based methods in the literature.

METHODS : A dataset containing 1037 regions of interest with tissue compartments and TILs annotated on 195 TNBC and HER2+ BC hematoxylin and eosin (H&E)-stained WSI was used. After segmenting nuclei within tumor-associated stroma using a watershed-based approach, 71 pathomic features were extracted from each nucleus and reduced using a Spearman's correlation filter followed by a nonparametric Wilcoxon rank-sum test and least absolute shrinkage and selection operator. The relevant features were used to classify each candidate nucleus as either TILs or non-TILs using 5 multivariable machine learning classification models trained using 5-fold cross-validation (1) without resampling, (2) with the synthetic minority over-sampling technique and (3) with downsampling. The prediction performance of the models was assessed using ROC curves.

RESULTS : 21 features were selected, with most of them related to the well-known TILs properties of having regular shape, clearer margins, high peak intensity, more homogeneous enhancement and different textural pattern than other cells. The best performance was obtained by Random-Forest with ROC AUC of 0.86, regardless of resampling technique.

CONCLUSIONS : The presented approach holds promise for the classification of TILs in BC H&E-stained WSI and could provide support to pathologists for a reliable, rapid and interpretable clinical assessment of TILs in BC.

Verdicchio Mario, Brancato Valentina, Cavaliere Carlo, Isgrò Francesco, Salvatore Marco, Aiello Marco

2023-Mar

Breast cancer, Digital pathology, Machine learning, Pathomics, Tumor infiltrating lymphocytes

General

Does managerial short-termism always matter in a firm's corporate social responsibility performance? Evidence from China.

In Heliyon

Using data on Chinese A-share listed firms from 2008 to 2017, we explore how corporate social responsibility (CSR) performance is affected by managerial short-termism and what factors influence the association between the two. First, by employing text analysis in conjunction with machine learning, we construct a new managerial short-termism indicator. Using panel fixed models, we find that managerial short-termism has an adverse impact on CSR performance, and the results are consistent in a series of robustness checks. The heterogeneous test results show that the negative effect is significant only for firms with lower internal corporate governance, for firms in less competitive industries, for firms with less analyst attention, and for state-owned enterprises (SOEs). Additionally, a better institutional environment weakens the negative impact of managerial short-termism on CSR performance. The findings shed light on policy implications for emerging countries.

Xu Xiaohui, Yang Jun

2023-Mar

CSR performance, Managerial short-termism, Random forest regression

General

Techniques of power system static security assessment and improvement: A literature survey.

In Heliyon

The secure operation of a power system depends on the available security evaluation tools and improvement techniques to tackle the disturbances or contingencies. The main objective of the survey presented in this paper is to provide a comprehensive review to the researchers, academicians, and utility engineers on the available techniques of static security assessment and improvement in modern power systems. Various performance indices are used to express the severity of limit violations from security margins typically in transmission line loading and buses voltage magnitude under a given disturbance or contingency. The accuracy and speed of computation considering uncertainties in renewable energy generation and load demand scenarios are the fundamental requirements of any security assessment tool. Conventional power flow and machine learning approaches are explored and compared for static security assessment. Although, conventional AC power flow provides accurate result, it is computationally demanding and slow process to assess the security of a power system with uncertainties and changing future operating scenarios considering simultaneous component failures. Several machine learning techniques have been studied to make fast and sufficiently accurate assessment. The application of FACTS devices to improve static security of a power system has been reviewed. To ensure the effectiveness of FACTS devices, various sensitivity and optimization approaches have been suggested for proper placement and sizing. The increasing complexity and uncertainty in power systems due to increased penetration of renewable energy resources and the introduction of new type of loads such as electric vehicles and heating loads suggests the development and application of more robust and portable security assessment tools such as deep learning algorithms and fast responding flexible security improvement mechanisms like FACTS devices.

Hailu Engidaw Abel, Nyakoe George Nyauma, Muriithi Christopher Maina

2023-Mar

FACTS device, Machine learning, Optimal allocation, Performance index, Power system uncertainties, Static security assessment, Static security improvement

General

Scientometric and multidimensional contents analysis of PM2.5 concentration prediction.

In Heliyon

The foundation for the environmental department to take suitable measures and make a significant contribution towards improving air quality is the precise and dependable prediction of PM_2.5 concentration. It is essential to review the development process and hotspots of PM_2.5 concentration prediction studies over the past 20 years (2000-2021) comprehensively and quantitatively. This study used detailed bibliometric methods and CiteSpace software to visually analyze the PM_2.5 pollution level. The outcomes found that the prediction research phases of PM_2.5 can be broadly divided into three phases and enter the rapid growth phase after 2017. Five categories of keywords are clustered, and the forecasting data and forecasting methods are typical cluster representatives. Then, the construction and processing of PM_2.5 concentration prediction datasets, the prediction methods and technical processes, and the determination of the prediction spatial-temporal scales are the main content of the analysis. In the future, it is necessary to concentrate on multi-source data fusion for PM_2.5 concentration prediction at multiple spatial-temporal scales and focus on technology integration and innovative applications in forecasting models, especially the optimal use of deep machine learning methods to improve prediction accuracy and practical application conversion.

Gong Jintao, Ding Lei, Lu Yingyu, Qiong Zhang Yun Li

2023-Mar

Contents analysis, Forecasting methods, Forecasting scale, PM2.5 concentration prediction, Scientometric

General

Optimal sizing of residential photovoltaic and battery system connected to the power grid based on the cost of energy and peak load.

In Heliyon

The use of renewable energy is necessary to achieve the goals of sustainable development, and sooner or later all countries are forced to plan and make policies for the use of this equipment. Considering the growing trend of smart systems and the ability of these systems to control and use renewable resources, it is necessary to investigate how to control and optimally use these resources in smart systems. Considering the geographical conditions and significant solar energy radiation in Iran, the most suitable option for using renewable energy in residential buildings is solar energy. Among the types of solar energy used around the world, photovoltaic panels are used more due to their wide range, being cheaper than other sources of electric power from solar energy and more durable than other sources. In order to reduce widespread losses and reduce the cost of transmission and distribution, increase efficiency, the possibility of the presence of private sector investors and increase the security and stability of the power grid, distributed production of electrical energy at consumption locations using small-scale units is the most cost-effective way to use home solar panels. Also, the production of energy from wind turbines can be done in the areas where anemometer data determine it to be suitable. The combination of solar energy and wind energy can effectively reduce the need for batteries, but studies show that this combination is only economically viable when it is used on a large scale and with high powers, which requires a lot of investment. Large initial capital is one of the biggest problems of distributed production systems, so the use of artificial intelligence methods for accurate capacity determination of renewable energy production systems becomes doubly important. The economic results show that the least cost of electricity and net price cost are 0.44 $ per kWh and 15.0 million $ respectively, when the converter size was gradually changed, with a renewable fraction of 46.7%.

Vahabi Khah Mohammad, Zahedi Rahim, Eskandarpanah Reza, Mirzaei Amir Mohammad, Farahani Omid Noudeh, Malek Iman, Rezaei Nima

2023-Mar

PV battery System, Renewable energy, Sizing, Solar energy

Radiology

Interoperable slide microscopy viewer and annotation tool for imaging data science and computational pathology.

In Nature communications ; h5-index 260.0

The exchange of large and complex slide microscopy imaging data in biomedical research and pathology practice is impeded by a lack of data standardization and interoperability, which is detrimental to the reproducibility of scientific findings and clinical integration of technological innovations. We introduce Slim, an open-source, web-based slide microscopy viewer that implements the internationally accepted Digital Imaging and Communications in Medicine (DICOM) standard to achieve interoperability with a multitude of existing medical imaging systems. We showcase the capabilities of Slim as the slide microscopy viewer of the NCI Imaging Data Commons and demonstrate how the viewer enables interactive visualization of traditional brightfield microscopy and highly-multiplexed immunofluorescence microscopy images from The Cancer Genome Atlas and Human Tissue Atlas Network, respectively, using standard DICOMweb services. We further show how Slim enables the collection of standardized image annotations for the development or validation of machine learning models and the visual interpretation of model inference results in the form of segmentation masks, spatial heat maps, or image-derived measurements.

Gorman Chris, Punzo Davide, Octaviano Igor, Pieper Steven, Longabaugh William J R, Clunie David A, Kikinis Ron, Fedorov Andrey Y, Herrmann Markus D

2023-Mar-22

Oncology

Immune gene patterns and characterization of the tumor immune microenvironment associated with cancer immunotherapy efficacy.

In Heliyon

Although immunotherapy has revolutionized cancer management, most patients do not derive benefits from it. Aiming to explore an appropriate strategy for immunotherapy efficacy prediction, we collected 6251 patients' transcriptome data from multicohort population and analyzed the data using a machine learning algorithm. In this study, we found that patients from three immune gene clusters had different overall survival when treated with immunotherapy (P < 0.001), and that these clusters had differential states of hypoxia scores and metabolism functions. The immune gene score showed good immunotherapy efficacy prediction (AUC was 0.737 at 20 months), which was well validated. The immune gene score, tumor mutation burden, and long non-coding RNA score were further combined to build a tumor immune microenvironment signature, which correlated more strongly with overall survival (AUC, 0.814 at 20 months) than when using a single variable. Thus, we recommend using the characterization of the tumor immune microenvironment associated with immunotherapy efficacy via a multi-omics analysis of cancer.

Lin Lili, Zhang Wenda, Chen Yongjian, Ren Wei, Zhao Jianli, Ouyang Wenhao, He Zifan, Su Weifeng, Yao Herui, Yu Yunfang

2023-Mar

AUC, Area under the curve, CIs, Confidence intervals, CTL, Cytotoxic T-lymphocyte infiltration, Cancer, GEO, Gene Expression Omnibus, GO, Gene Ontology, GSEA, Gene set enrichment analysis, GSVA, Gene set variation analysis, HLAs, Human leukocyte antigens, HRs, Hazard ratios, Immunotherapy, KEGG, Kyoto Encyclopedia of Genes and Genomes, LASSO, Penalized logistic least absolute shrinkage and selector operation, Machine learning, NSCLC, Non-small cell lung cancer, OS, Overall survival, PCA, Principal componentanalysis, PD-L1, Programmed death ligand-1, PFS, Profession-free survival, RNA-seq, Transcriptome RNA sequencing, ROC, receiver operating characteristic curves, TCGA, The Cancer Genome Atlas, TMB, Tumor mutation burden, TME, Tumor immunemicroenvironment, Tumor immune microenvironment, WGCNA, Weighted gene co-expression network analysis, lncRNA, Long non-coding RNA

General

Assessing the influence of industry 4.0 technologies on occupational health and safety.

In Heliyon

The aim of this article is to know the impact that the different Industry 4.0 technologies have on occupational health and safety risks, with special attention to the new emerging risks generated. To achieve this objective, an analysis of the literature was carried out. It allowed us to design a survey that was answered by 130 managers and/or technicians of pioneering companies in the development of Industry 4.0 technologies. Next, 32 of these projects were selected and a multiple case study was conducted through 37 in-depth interviews. Moreover, other source of information were analysed (project reports, technical reports, websites..). The findings highlight that the analysed technologies (Additive Manufacturing, Artificial Intelligence, Artificial Vision, Big Data and/or Advanced Analytics, Cybersecurity, Internet of Things, Robotics and Virtual and Augmented Reality) help to reduce occupational health and safety risks (physical and mechanical). However, its impact depends on the type of technology and the method of application. Influences in new emerging risks (mainly psychosocial and mechanical) have been detected in all technologies except in Internet of Things. In addition, additive manufacturing, artificial intelligence, machine vision, the internet of things, robotics and virtual and augmented reality help to reduce ergonomic risks and artificial intelligence, big data and cybersecurity psychosocial risks. The results obtained have implications for policy makers, managers, consultants and those in charge of managing occupational health and safety risks in industrial companies.

Arana-Landín Germán, Laskurain-Iturbe Iker, Iturrate Mikel, Landeta-Manzano Beñat

2023-Mar

ISO 45001, Industry 4.0 technologies, Occupational health and safety, Occupational risks

Radiology

Amplitude of low-frequency fluctuation-based regional radiomics similarity network: Biomarker for Parkinson's disease.

In Heliyon

Parkinson's disease (PD) is a highly heterogeneous disorder that is difficult to diagnose. Therefore, reliable biomarkers are needed. We implemented a method constructing a regional radiomics similarity network (R2SN) based on the amplitude of low-frequency fluctuation (ALFF). We classified patients with PD and healthy individuals by using a machine learning approach in accordance with the R2SN connectome. The ALFF-based R2SN exhibited great reproducibility with different brain atlases and datasets. Great classification performances were achieved both in primary (AUC = 0.85 ± 0.02 and accuracy = 0.81 ± 0.03) and independent external validation (AUC = 0.77 and accuracy = 0.70) datasets. The discriminative R2SN edges correlated with the clinical evaluations of patients with PD. The nodes of discriminative R2SN edges were primarily located in the default mode, sensorimotor, executive control, visual and frontoparietal network, cerebellum and striatum. These findings demonstrate that ALFF-based R2SN is a robust potential neuroimaging biomarker for PD and could provide new insights into connectome reorganization in PD.

Shi Dafa, Ren Zhendong, Zhang Haoran, Wang Guangsong, Guo Qiu, Wang Siyuan, Ding Jie, Yao Xiang, Li Yanfei, Ren Ke

2023-Mar

Amplitude of low-frequency fluctuation (ALFF), Biomarker, Machine learning, Network, Parkinson’s disease

Internal Medicine

Deep learning-assisted diagnosis of chronic atrophic gastritis in endoscopy.

In Frontiers in oncology

BACKGROUND : Chronic atrophic gastritis (CAG) is a precancerous condition. It is not easy to detect CAG in endoscopy. Improving the detection rate of CAG under endoscopy is essential to reduce or interrupt the occurrence of gastric cancer. This study aimed to construct a deep learning (DL) model for CAG recognition based on endoscopic images to improve the CAG detection rate during endoscopy.

METHODS : We collected 10,961 endoscopic images and 118 video clips from 4,050 patients. For model training and testing, we divided them into two groups based on the pathological results: CAG and chronic non-atrophic gastritis (CNAG). We compared the performance of four state-of-the-art (SOTA) DL networks for CAG recognition and selected one of them for further improvement. The improved network was called GAM-EfficientNet. Finally, we compared GAM-EfficientNet with three endoscopists and analyzed the decision basis of the network in the form of heatmaps.

RESULTS : After fine-tuning and transfer learning, the sensitivity, specificity, and accuracy of GAM-EfficientNet reached 93%, 94%, and 93.5% in the external test set and 96.23%, 89.23%, and 92.37% in the video test set, respectively, which were higher than those of the three endoscopists.

CONCLUSIONS : The CAG recognition model based on deep learning has high sensitivity and accuracy, and its performance is higher than that of endoscopists.

Shi Yanting, Wei Ning, Wang Kunhong, Wu Jingjing, Tao Tao, Li Na, Lv Bing

2023

chronic atrophic gastritis (CAG), deep learning - artificial intelligence, endoscopy, gastric cancer, transfer learning

General

A versatile and fast-sampling rate wearable analog data logger.

In MethodsX

We propose a wearable, versatile, and open-source data logger that harvests the capacities of a low-cost microcontroller and enables fast-sampling recording of Analog signals into a microSD card. We describe here the circuit design and an exhaustive list of instructions to build a small, lightweight, and fast sampling rate data logger (up to 5 kHz for simultaneous recording of 3 channels and up to 40 kHz when using a single channel). We provide data analysis instructions, including publicly available scripts to facilitate its replication and customization. As a straightforward proof-of-concept, we tested our device embedded with a three-axial Analog accelerometer and were able to record triple axis acceleration of body movements in high resolution. A Fourier transform followed by a principal component analysis discriminated accurately between body motions of two participants and two types of movement recorded (walking VS running). Our wearable and fast-sampling rate data logger overcomes limits that we identified in previous studies, by being low-cost, capable of fast sampling rate, and easily replicated. Moreover, it can be customized to fit with a wide variety of applications in biomedical research by substituting the three-axial Analog accelerometer with virtually any type of Analog sensors or devices that output Analog signals. •We present a method to build and use a low-cost, fast-sampling rate and wearable Analog data logger, where having an engineering background is not required.•The data logger we present can collect Analog signals from 3 channels simultaneously at 5kHz and up to 40 kHz when using a single channel.•We demonstrate that our data logger can record data from a triple axis Analog accelerometer at 5 kHz, however, signals from virtually any Analog sensor or device that outputs Analog signals can be collected.

Bouchekioua Youcef, Matsui Hiroshi, Watanabe Shigeru

2023

Accelerometer, Analog, Data logger, Microcontroller, Movement disorders, Sensor, Versatile and Fast-Sampling Rate Wearable Analog Data Logger

General

Search, identification, and curation of cell and gene therapy product regulations using augmented intelligent systems.

In Frontiers in medicine

BACKGROUND : Manually keeping up-to-date with regulations such as directives, guidance, laws, and ordinances related to cell and gene therapy is a labor-intensive process. We used machine learning (ML) algorithms to create an augmented intelligent system to optimize systematic screening of global regulations to improve efficiency and reduce overall labor and missed regulations.

METHODS : Combining Boolean logic and artificial intelligence (i.e., augmented intelligence) for the search process, ML algorithms were used to identify and suggest relevant cell and gene therapy regulations. Suggested regulations were delivered to a landing page for further subject matter expert (SME) tagging of words/phrases to provide system relevance on functional words. Ongoing learning from the repository regulations continued to increase system reliability and performance. The automated ability to train and retrain the system allows for continued refinement and improvement of system accuracy. Automated daily searches for applicable regulations in global databases provide ongoing opportunities to update the repository.

RESULTS : Compared to manual searching, which required 3-4 SMEs to review ~115 regulations, the current system performance, with continuous system learning, requires 1 full-time equivalent to process approximately 9,000 regulations/day. Currently, system performance has 86% overall accuracy, a recommend recall of 87%, and a reject recall of 84%. A conservative search strategy is intentionally used to permit SMEs to assess low-recommended regulations in order to prevent missing any applicable regulations.

CONCLUSION : Compared to manual searches, our custom automated search system greatly improves the management of cell and gene therapy regulations and is efficient, cost effective, and accurate.

Schaut William, Shrivastav Akash, Ramakrishnan Srikanth, Bowden Robert

2023

CAR-T, augmented intelligence, automated systematic search, machine learning, regulations, regulatory documents

General

Optimizing MRI-based brain tumor classification and detection using AI: A comparative analysis of neural networks, transfer learning, data augmentation, and the cross-transformer network.

In European journal of radiology open

Early detection and diagnosis of brain tumors are crucial to taking adequate preventive measures, as with most cancers. On the other hand, artificial intelligence (AI) has grown exponentially, even in such complex environments as medicine. Here it's proposed a framework to explore state-of-the-art deep learning architectures for brain tumor classification and detection. An own development called Cross-Transformer is also included, which consists of three scalar products that combine self-care model keys, queries, and values. Initially, we focused on the classification of three types of tumors: glioma, meningioma, and pituitary. With the Figshare brain tumor dataset was trained the InceptionResNetV2, InceptionV3, DenseNet121, Xception, ResNet50V2, VGG19, and EfficientNetB7 networks. Over 97 % of classifications were accurate in this experiment, which provided a network's performance overview. Subsequently, we focused on tumor detection using the Brain MRI Images for Brain Tumor Detection and The Cancer Genome Atlas Low-Grade Glioma database. The development encompasses learning transfer, data augmentation, as well as image acquisition sequences; T1-weighted images (T1WI), T1-weighted post-gadolinium (T1-Gd), and Fluid-Attenuated Inversion Recovery (FLAIR). Based on the results, using learning transfer and data augmentation increased accuracy by up to 6 %, with a p-value below the significance level of 0.05. As well, the FLAIR sequence was the most efficient for detection. As an alternative, our proposed model proved to be the most effective in terms of training time, using approximately half the time of the second fastest network.

Anaya-Isaza Andrés, Mera-Jiménez Leonel, Verdugo-Alejo Lucía, Sarasti Luis

2023

Artificial intelligence, Cancer detection, Machine learning, Magnetic resonance imaging, Transformers, Tumors

Surgery

ChatGPT - Reshaping medical education and clinical management.

In Pakistan journal of medical sciences

Artificial Intelligence is no more the talk of the fiction read in novels or seen in movies. It has been making inroads slowly and gradually in medical education and clinical management of patients apart from all other walks of life. Recently, chatbots particularly ChatGPT, were developed and trained, using a huge amount of textual data from the internet. This has made a significant impact on our approach in medical science. Though there are benefits of this new technology, a lot of caution is required for its use.

Khan Rehan Ahmed, Jawaid Masood, Khan Aymen Rehan, Sajjad Madiha

2023

Artificial Intelligence, ChatGPT, Education, NLP, Open AI, clinical management, medical education

Oncology

Systematic assessment of prognostic molecular features across cancers.

In Cell genomics

Precision oncology promises accurate prediction of disease trajectories by utilizing molecular features of tumors. We present a systematic analysis of the prognostic potential of diverse molecular features across large cancer cohorts. We find that the mRNA expression of biologically coherent sets of genes (modules) is substantially more predictive of patient survival than single-locus genomic and transcriptomic aberrations. Extending our analysis beyond existing curated gene modules, we find a large novel class of highly prognostic DNA/RNA cis-regulatory modules associated with dynamic gene expression within cancers. Remarkably, in more than 82% of cancers, modules substantially improve survival stratification compared with conventional clinical factors and prominent genomic aberrations. The prognostic potential of cancer modules generalizes to external cohorts better than conventionally used single-gene features. Finally, a machine-learning framework demonstrates the combined predictive power of multiple modules, yielding prognostic models that perform substantially better than existing histopathological and clinical factors in common use.

Santhanam Balaji, Oikonomou Panos, Tavazoie Saeed

2023-Mar-08

cancer genomics, cancer regulatory networks, precision oncology, prognostic cancer biomarkers

General

A longan yield estimation approach based on UAV images and deep learning.

In Frontiers in plant science

Longan yield estimation is an important practice before longan harvests. Statistical longan yield data can provide an important reference for market pricing and improving harvest efficiency and can directly determine the economic benefits of longan orchards. At present, the statistical work concerning longan yields requires high labor costs. Aiming at the task of longan yield estimation, combined with deep learning and regression analysis technology, this study proposed a method to calculate longan yield in complex natural environment. First, a UAV was used to collect video images of a longan canopy at the mature stage. Second, the CF-YD model and SF-YD model were constructed to identify Cluster_Fruits and Single_Fruits, respectively, realizing the task of automatically identifying the number of targets directly from images. Finally, according to the sample data collected from real orchards, a regression analysis was carried out on the target quantity detected by the model and the real target quantity, and estimation models were constructed for determining the Cluster_Fruits on a single longan tree and the Single_Fruits on a single Cluster_Fruit. Then, an error analysis was conducted on the data obtained from the manual counting process and the estimation model, and the average error rate regarding the number of Cluster_Fruits was 2.66%, while the average error rate regarding the number of Single_Fruits was 2.99%. The results show that the method proposed in this paper is effective at estimating longan yields and can provide guidance for improving the efficiency of longan fruit harvests.

Li Denghui, Sun Xiaoxuan, Jia Yuhang, Yao Zhongwei, Lin Peiyi, Chen Yingyi, Zhou Haobo, Zhou Zhengqi, Wu Kaixuan, Shi Linlin, Li Jun

2023

UAV image, convolutional neural network, image analysis, regression analysis, yield estimation

Surgery

An intracochlear electrocochleography dataset - from raw data to objective analysis using deep learning.

In Scientific data

Electrocochleography (ECochG) measures electrophysiological inner ear potentials in response to acoustic stimulation. These potentials reflect the state of the inner ear and provide important information about its residual function. For cochlear implant (CI) recipients, we can measure ECochG signals directly within the cochlea using the implant electrode. We are able to perform these recordings during and at any point after implantation. However, the analysis and interpretation of ECochG signals are not trivial. To assist the scientific community, we provide our intracochlear ECochG data set, which consists of 4,924 signals recorded from 46 ears with a cochlear implant. We collected data either immediately after electrode insertion or postoperatively in subjects with residual acoustic hearing. This data descriptor aims to provide the research community access to our comprehensive electrophysiological data set and algorithms. It includes all steps from raw data acquisition to signal processing and objective analysis using Deep Learning. In addition, we collected subject demographic data, hearing thresholds, subjective loudness levels, impedance telemetry, radiographic findings, and classification of ECochG signals.

Schuerch Klaus, Wimmer Wilhelm, Dalbert Adrian, Rummel Christian, Caversaccio Marco, Mantokoudis Georgios, Gawliczek Tom, Weder Stefan

2023-Mar-22

General

Modeling structure-activity relationships with machine learning to identify GSK3-targeted small molecules as potential COVID-19 therapeutics.

In Frontiers in endocrinology ; h5-index 55.0

Coronaviruses induce severe upper respiratory tract infections, which can spread to the lungs. The nucleocapsid protein (N protein) plays an important role in genome replication, transcription, and virion assembly in SARS-CoV-2, the virus causing COVID-19, and in other coronaviruses. Glycogen synthase kinase 3 (GSK3) activation phosphorylates the viral N protein. To combat COVID-19 and future coronavirus outbreaks, interference with the dependence of N protein on GSK3 may be a viable strategy. Toward this end, this study aimed to construct robust machine learning models to identify GSK3 inhibitors from Food and Drug Administration-approved and investigational drug libraries using the quantitative structure-activity relationship approach. A non-redundant dataset consisting of 495 and 3070 compounds for GSK3α and GSK3β, respectively, was acquired from the ChEMBL database. Twelve sets of molecular descriptors were used to define these inhibitors, and machine learning algorithms were selected using the LazyPredict package. Histogram-based gradient boosting and light gradient boosting machine algorithms were used to develop predictive models that were evaluated based on the root mean square error and R-squared value. Finally, the top two drugs (selinexor and ruboxistaurin) were selected for molecular dynamics simulation based on the highest predicted activity (negative log of the half-maximal inhibitory concentration, pIC₅₀ value) to further investigate the structural stability of the protein-ligand complexes. This artificial intelligence-based virtual high-throughput screening approach is an effective strategy for accelerating drug discovery and finding novel pharmacological targets while reducing the cost and time.

Pirzada Rameez Hassan, Ahmad Bilal, Qayyum Naila, Choi Sangdun

2023

GSK3, QSAR, coronaviruses, machine learning, molecular descriptors

General

IP4GS: Bringing genomic selection analysis to breeders.

In Frontiers in plant science

Genomic selection (GS), a strategy to use genotypes to predict phenotypes via statistical or machine learning models, has become a routine practice in plant breeding programs. GS can speed up the genetic gain by reducing phenotyping costs and/or shortening the breeding cycles. GS analysis is complicated involving data clean up and formatting, training and test population analysis, model selection and evaluation, and parameter optimization. In addition, GS analysis also requires some programming skills and knowledge of statistical modeling. Thus, we need a more practical GS tools for breeders. To alleviate this difficulty, we developed the web-based platform IP4GS (https://ngdc.cncb.ac.cn/ip4gs/), which offers a user-friendly interface to perform GS analysis simply through point-and-click actions. IP4GS currently includes seven commonly used models, eleven evaluation metrics, and visualization modules, offering great convenience for plant breeders with limited bioinformatics knowledge to apply GS analysis.

Li Tong, Jiang Shan, Fu Ran, Wang Xiangfeng, Cheng Qian, Jiang Shuqin

2023

R shiny, bioinformatics, genomic selection, genotype-to-phenotype prediction, web-based platform

General

Assessing farmland suitability for agricultural machinery in land consolidation schemes in hilly terrain in China: A machine learning approach.

In Frontiers in plant science

Identifying available farmland suitable for agricultural machinery is the most promising way of optimizing agricultural production and increasing agricultural mechanization. Farmland consolidation suitable for agricultural machinery (FCAM) is implemented as an effective tool for increasing sustainable production and mechanized agriculture. By using the machine learning approach, this study assesses the suitability of farmland for agricultural machinery in land consolidation schemes based on four parameters, i.e., natural resource endowment, accessibility of agricultural machinery, socioeconomic level, and ecological limitations. And based on "suitability" and "potential improvement in farmland productivity", we classified land into four zones: the priority consolidation zone, the moderate consolidation zone, the comprehensive consolidation zone, and the reserve consolidation zone. The results showed that most of the farmland (76.41%) was either basically or moderately suitable for FCAM. Although slope was often an indicator that land was suitable for agricultural machinery, other factors, such as the inferior accessibility of tractor roads, continuous depopulation, and ecological fragility, contributed greatly to reducing the overall suitability of land for FCAM. Moreover, it was estimated that the potential productivity of farmland would be increased by 720.8 kg/ha if FCAM were implemented. Four zones constituted a useful basis for determining the implementation sequence and differentiating strategies for FCAM schemes. Consequently, this zoning has been an effective solution for implementing FCAM schemes. However, the successful implementation of FCAM schemes, and the achievement a modern and sustainable agriculture system, will require some additional strategies, such as strengthening farmland ecosystem protection and promoting R&D into agricultural machinery suitable for hilly terrain, as well as more financial support.

Yang Heng, Ma Wenqiu, Liu Tongxin, Li Wenqing

2023

farmland consolidation suitable for agricultural machinery, hilly terrain, machine learning approach (MLA), potential of farmland productivity, suitability assessment, zoning

Surgery

Assessment of shape-based features ability to predict the ascending aortic aneurysm growth.

In Frontiers in physiology

The current guidelines for the ascending aortic aneurysm (AsAA) treatment recommend surgery mainly according to the maximum diameter assessment. This criterion has already proven to be often inefficient in identifying patients at high risk of aneurysm growth and rupture. In this study, we propose a method to compute a set of local shape features that, in addition to the maximum diameter D, are intended to improve the classification performances for the ascending aortic aneurysm growth risk assessment. Apart from D, these are the ratio DCR between D and the length of the ascending aorta centerline, the ratio EILR between the length of the external and the internal lines and the tortuosity T. 50 patients with two 3D acquisitions at least 6 months apart were segmented and the growth rate (GR) with the shape features related to the first exam computed. The correlation between them has been investigated. After, the dataset was divided into two classes according to the growth rate value. We used six different classifiers with input data exclusively from the first exam to predict the class to which each patient belonged. A first classification was performed using only D and a second with all the shape features together. The performances have been evaluated by computing accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUROC) and positive (negative) likelihood ratio LHR+ (LHR-). A positive correlation was observed between growth rate and DCR (r = 0.511, p = 1.3e-4) and between GR and EILR (r = 0.472, p = 2.7e-4). Overall, the classifiers based on the four metrics outperformed the same ones based only on D. Among the diameter-based classifiers, k-nearest neighbours (KNN) reported the best accuracy (86%), sensitivity (55.6%), AUROC (0.74), LHR+ (7.62) and LHR- (0.48). Concerning the classifiers based on the four shape features, we obtained the best accuracy (94%), sensitivity (66.7%), specificity (100%), AUROC (0.94), LHR+ (+∞) and LHR- (0.33) with support vector machine (SVM). This demonstrates how automatic shape features detection combined with risk classification criteria could be crucial in planning the follow-up of patients with ascending aortic aneurysm and in predicting the possible dangerous progression of the disease.

Geronzi Leonardo, Haigron Pascal, Martinez Antonio, Yan Kexin, Rochette Michel, Bel-Brunon Aline, Porterie Jean, Lin Siyu, Marin-Castrillon Diana Marcela, Lalande Alain, Bouchot Olivier, Daniel Morgan, Escrig Pierre, Tomasi Jacques, Valentini Pier Paolo, Biancolini Marco Evangelos

2023

aorta, ascending aorta aneurysm, biomechanical features, cardiovascular diseases, classification, growth rate, machine learning, risk assessment

General

A lightweight fetal distress-assisted diagnosis model based on a cross-channel interactive attention mechanism.

In Frontiers in physiology

Fetal distress is a symptom of fetal intrauterine hypoxia, which is seriously harmful to both the fetus and the pregnant woman. The current primary clinical tool for the assessment of fetal distress is Cardiotocography (CTG). Due to subjective variability, physicians often interpret CTG results inconsistently, hence the need to develop an auxiliary diagnostic system for fetal distress. Although the deep learning-based fetal distress-assisted diagnosis model has a high classification accuracy, the model not only has a large number of parameters but also requires a large number of computational resources, which is difficult to deploy to practical end-use scenarios. Therefore, this paper proposes a lightweight fetal distress-assisted diagnosis network, LW-FHRNet, based on a cross-channel interactive attention mechanism. The wavelet packet decomposition technique is used to convert the one-dimensional fetal heart rate (FHR) signal into a two-dimensional wavelet packet coefficient matrix map as the network input layer to fully obtain the feature information of the FHR signal. With ShuffleNet-v2 as the core, a local cross-channel interactive attention mechanism is introduced to enhance the model's ability to extract features and achieve effective fusion of multichannel features without dimensionality reduction. In this paper, the publicly available database CTU-UHB is used for the network performance evaluation. LW-FHRNet achieves 95.24% accuracy, which meets or exceeds the classification results of deep learning-based models. Additionally, the number of model parameters is reduced many times compared with the deep learning model, and the size of the model parameters is only 0.33 M. The results show that the lightweight model proposed in this paper can effectively aid in fetal distress diagnosis.

Deng Yanjun, Zhang Yefei, Zhou Zhixin, Zhang Xianfei, Jiao Pengfei, Zhao Zhidong

2023

attention mechanism, fetal distress, fetal heart rate, lightweight model, wavelet packet coefficient

General

Analysis of the therapeutic interaction provided by a humanoid robot serving stroke survivors as a therapeutic assistant for arm rehabilitation.

In Frontiers in robotics and AI

Objective: To characterize a socially active humanoid robot's therapeutic interaction as a therapeutic assistant when providing arm rehabilitation (i.e., arm basis training (ABT) for moderate-to-severe arm paresis or arm ability training (AAT) for mild arm paresis) to stroke survivors when using the digital therapeutic system Evidence-Based Robot-Assistant in Neurorehabilitation (E-BRAiN) and to compare it to human therapists' interaction. Methods: Participants and therapy: Seventeen stroke survivors receiving arm rehabilitation (i.e., ABT [n = 9] or AAT [n = 8]) using E-BRAiN over a course of nine sessions and twenty-one other stroke survivors receiving arm rehabilitation sessions (i.e., ABT [n = 6] or AAT [n = 15]) in a conventional 1:1 therapist-patient setting. Analysis of therapeutic interaction: Therapy sessions were videotaped, and all therapeutic interactions (information provision, feedback, and bond-related interaction) were documented offline both in terms of their frequency of occurrence and time used for the respective type of interaction using the instrument THER-I-ACT. Statistical analyses: The therapeutic interaction of the humanoid robot, supervising staff/therapists, and helpers on day 1 is reported as mean across subjects for each type of therapy (i.e., ABT and AAT) as descriptive statistics. Effects of time (day 1 vs. day 9) on the humanoid robot interaction were analyzed by repeated-measures analysis of variance (rmANOVA) together with the between-subject factor type of therapy (ABT vs. AAT). The between-subject effect of the agent (humanoid robot vs. human therapist; day 1) was analyzed together with the factor therapy (ABT vs. AAT) by ANOVA. Main results and interpretation: The overall pattern of the therapeutic interaction by the humanoid robot was comprehensive and varied considerably with the type of therapy (as clinically indicated and intended), largely comparable to human therapists' interaction, and adapted according to needs for interaction over time. Even substantially long robot-assisted therapy sessions seemed acceptable to stroke survivors and promoted engaged patients' training behavior. Conclusion: Humanoid robot interaction as implemented in the digital system E-BRAiN matches the human therapeutic interaction and its modification across therapies well and promotes engaged training behavior by patients. These characteristics support its clinical use as a therapeutic assistant and, hence, its application to support specific and intensive restorative training for stroke survivors.

Platz Thomas, Pedersen Ann Louise, Deutsch Philipp, Umlauft Alexandru-Nicolae, Bader Sebastian

2023

arm, artificial intelligence, interaction, robot, social, stroke, training

General

Deep Survival Analysis With Clinical Variables for COVID-19.

In IEEE journal of translational engineering in health and medicine

OBJECTIVE : Millions of people have been affected by coronavirus disease 2019 (COVID-19), which has caused millions of deaths around the world. Artificial intelligence (AI) plays an increasing role in all areas of patient care, including prognostics. This paper proposes a novel predictive model based on one dimensional convolutional neural networks (1D CNN) to use clinical variables in predicting the survival outcome of COVID-19 patients.

METHODS AND PROCEDURES : We have considered two scenarios for survival analysis, 1) uni-variate analysis using the Log-rank test and Kaplan-Meier estimator and 2) combining all clinical variables ([Formula: see text]=44) for predicting the short-term from long-term survival. We considered the random forest (RF) model as a baseline model, comparing to our proposed 1D CNN in predicting survival groups.

RESULTS : Our experiments using the univariate analysis show that nine clinical variables are significantly associated with the survival outcome with corrected p < 0.05. Our approach of 1D CNN shows a significant improvement in performance metrics compared to the RF and the state-of-the-art techniques (i.e., 1D CNN) in predicting the survival group of patients with COVID-19.

CONCLUSION : Our model has been tested using clinical variables, where the performance is found promising. The 1D CNN model could be a useful tool for detecting the risk of mortality and developing treatment plans in a timely manner.

CLINICAL IMPACT : The findings indicate that using both Heparin and Exnox for treatment is typically the most useful factor in predicting a patient's chances of survival from COVID-19. Moreover, our predictive model shows that the combination of AI and clinical data can be applied to point-of-care services through fast-learning healthcare systems.

Chaddad Ahmad, Hassan Lama, Katib Yousef, Bouridane Ahmed

2023

CNN, COVID-19, clinical variables

General

Deep learning-based fusion of widefield diffuse optical tomography and micro-CT structural priors for accurate 3D reconstructions.

In Biomedical optics express

Widefield illumination and detection strategies leveraging structured light have enabled fast and robust probing of tissue properties over large surface areas and volumes. However, when applied to diffuse optical tomography (DOT) applications, they still require a time-consuming and expert-centric solving of an ill-posed inverse problem. Deep learning (DL) models have been recently proposed to facilitate this challenging step. Herein, we expand on a previously reported deep neural network (DNN) -based architecture (modified AUTOMAP - ModAM) for accurate and fast reconstructions of the absorption coefficient in 3D DOT based on a structured light illumination and detection scheme. Furthermore, we evaluate the improved performances when incorporating a micro-CT structural prior in the DNN-based workflow, named Z-AUTOMAP. This Z-AUTOMAP significantly improves the widefield imaging process's spatial resolution, especially in the transverse direction. The reported DL-based strategies are validated both in silico and in experimental phantom studies using spectral micro-CT priors. Overall, this is the first successful demonstration of micro-CT and DOT fusion using deep learning, greatly enhancing the prospect of rapid data-integration strategies, often demanded in challenging pre-clinical scenarios.

Nizam Navid Ibtehaj, Ochoa Marien, Smith Jason T, Intes Xavier

2023-Mar-01

General

Automated analysis framework for in vivo cardiac ablation therapy monitoring with optical coherence tomography.

In Biomedical optics express

Radiofrequency ablation (RFA) is a minimally invasive procedure that is commonly used for the treatment of atrial fibrillation. However, it is associated with a significant risk of arrhythmia recurrence and complications owing to the lack of direct visualization of cardiac substrates and real-time feedback on ablation lesion transmurality. Within this manuscript, we present an automated deep learning framework for in vivo intracardiac optical coherence tomography (OCT) analysis of swine left atria. Our model can accurately identify cardiac substrates, monitor catheter-tissue contact stability, and assess lesion transmurality on both OCT intensity and polarization-sensitive OCT data. To the best of our knowledge, we have developed the first automatic framework for in vivo cardiac OCT analysis, which holds promise for real-time monitoring and guidance of cardiac RFA therapy..

Huang Ziyi, Zhao Xiaowei, Ziv Ohad, Laurita Kenneth R, Rollins Andrew M, Hendon Christine P

2023-Mar-01

General

Sentinel lymph node mapping in patients with breast cancer using a photoacoustic/ultrasound dual-modality imaging system with carbon nanoparticles as the contrast agent: a pilot study.

In Biomedical optics express

Assessing the metastatic status of axillary lymph nodes is a common clinical practice in the staging of early breast cancers. Yet sentinel lymph nodes (SLNs) are the regional lymph nodes believed to be the first stop along the lymphatic drainage path of the metastasizing cancer cells. Compared to axillary lymph node dissection, sentinel lymph node biopsy (SLNB) helps reduce morbidity and side effects. Current SLNB methods, however, still have suboptimum properties, such as restrictions due to nuclide accessibility and a relatively low therapeutic efficacy when only a single contrast agent is used. To overcome these limitations, researchers have been motivated to develop a non-radioactive SLN mapping method to replace or supplement radionuclide mapping. We proposed and demonstrated a clinical procedure using a dual-modality photoacoustic (PA)/ultrasound (US) imaging system to locate the SLNs to offer surgical guidance. In our work, the high contrast of PA imaging and its specificity to SLNs were based on the accumulation of carbon nanoparticles (CNPs) in the SLNs. A machine-learning model was also trained and validated to distinguish stained SLNs based on single-wavelength PA images. In the pilot study, we imaged 11 patients in vivo, and the specimens from 13 patients were studied ex vivo. PA/US imaging identified stained SLNs in vivo without a single false positive (23 SLNs), yielding 100% specificity and 52.6% sensitivity based on the current PA imaging system. Our machine-learning model can automatically detect SLNs in real time. In the new procedure, single-wavelength PA/US imaging uses CNPs as the contrast agent. The new system can, with that contrast agent, noninvasively image SLNs with high specificity in real time based on the unique features of the SLNs in the PA images. Ultimately, we aim to use our systems and approach to substitute or supplement nuclide tracers for a non-radioactive, less invasive SLN mapping method in SLNB for the axillary staging of breast cancer.

Gu Liujie, Deng Handi, Bai Yizhou, Gao Jianpan, Wang Xuewei, Yue Tong, Luo Bin, Ma Cheng

2023-Mar-01

Pathology

Computational tools for exploring peptide-membrane interactions in gram-positive bacteria.

In Computational and structural biotechnology journal

The vital cellular functions in Gram-positive bacteria are controlled by signaling molecules known as quorum sensing peptides (QSPs), considered promising therapeutic interventions for bacterial infections. In the bacterial system QSPs bind to membrane-coupled receptors, which then auto-phosphorylate and activate intracellular response regulators. These response regulators induce target gene expression in bacteria. One of the most reliable trends in drug discovery research for virulence-associated molecular targets is the use of peptide drugs or new functionalities. In this perspective, computational methods act as auxiliary aids for biologists, where methodologies based on machine learning and in silico analysis are developed as suitable tools for target peptide identification. Therefore, the development of quick and reliable computational resources to identify or predict these QSPs along with their receptors and inhibitors is receiving considerable attention. The databases such as Quorumpeps and Quorum Sensing of Human Gut Microbes (QSHGM) provide a detailed overview of the structures and functions of QSPs. The tools and algorithms such as QSPpred, QSPred-FL, iQSP, EnsembleQS and PEPred-Suite have been used for the generic prediction of QSPs and feature representation. The availability of compiled key resources for utilizing peptide features based on amino acid composition, positional preferences, and motifs as well as structural and physicochemical properties, including biofilm inhibitory peptides, can aid in elucidating the QSP and membrane receptor interactions in infectious Gram-positive pathogens. Herein, we present a comprehensive survey of diverse computational approaches that are suitable for detecting QSPs and QS interference molecules. This review highlights the utility of these methods for developing potential biomarkers against infectious Gram-positive pathogens.

Kumar Shreya, Balaya Rex Devasahayam Arokia, Kanekar Saptami, Raju Rajesh, Prasad Thottethodi Subrahmanya Keshava, Kandasamy Richard K

2023

3-HBA, 3–Hydroxybenzoic Acid, AAC, Amino Acid Composition, ABC, ATP-binding cassette, ACD, Available Chemicals Database, AIP, Autoinducing Peptide, AMP, Anti-Microbial Peptide, ATP, Adenosine Triphosphate, Agr, Accessory gene regulator, BFE, Binding Free Energy, BIP Inhibitors, BIP, Biofilm Inhibitory Peptides, BLAST, Basic Local Alignment Search Tool, BNB, Bernoulli Naïve-Bayes, CADD, Computer-Aided Drug Design, CSP, Competence Stimulating Peptide, CTD, Composition-Transition-Distribution, D, Aspartate, DCH, 3,3′-(3,4-dichlorobenzylidene)-bis-(4-hydroxycoumarin), DT, Decision Tree, FDA, Food and Drug Administration, GBM, Gradient Boosting Machine, GDC, g-gap Dipeptide, GNB, Gaussian NB, Gram-positive bacteria, H, Histidine, H-Kinase, Histidine Kinase, H-phosphotransferase, Histidine Phosphotransferase, HAM, Hamamelitannin, HGM, Human Gut Microbiota, HNP, Human Neutrophil Peptide, IT, Information Theory Features, In silico approaches, KNN, K-Nearest Neighbors, MCC, Mathew Co-relation Coefficient, MD, Molecular Dynamics, MDR, Multiple Drug Resistance, ML, Machine Learning, MRSA, Methicillin Resistant S. aureus, MSL, Multiple Sequence Alignment, OMR, Omargliptin, OVP, Overlapping Property Features, PCP, Physicochemical Properties, PDB, Protein Data Bank, PPIs, Protein-Protein Interactions, PSM, Phenol-Soluble Modulin, PTM, Post Translational Modification, QS, Quorum Sensing, QSCN, QS communication network, QSHGM, Quorum Sensing of Human Gut Microbes, QSI, QS Inhibitors, QSIM, QS Interference Molecules, QSP inhibitors, QSP predictors, QSP, QS Peptides, QSPR, Quantitative Structure Property Relationship, Quorum sensing peptides, RAP, RNAIII-activating protein, RF, Random Forest, RIP, RNAIII-inhibiting peptide, ROC, Receiver Operating Characteristic, SAR, Structure-Activity Relationship, SFS, Sequential Forward Search, SIT, Sitagliptin, SVM, Support Vector Machine, TCS, Two-Component Sensory, TRAP, Target of RAP, TRG, Trelagliptin, WHO, World Health Organization, mRMR, minimum Redundancy and Maximum Relevance

General

BrainGENIE: The Brain Gene Expression and Network Imputation Engine.

In Translational psychiatry ; h5-index 60.0

In vivo experimental analysis of human brain tissue poses substantial challenges and ethical concerns. To address this problem, we developed a computational method called the Brain Gene Expression and Network-Imputation Engine (BrainGENIE) that leverages peripheral-blood transcriptomes to predict brain tissue-specific gene-expression levels. Paired blood-brain transcriptomic data collected by the Genotype-Tissue Expression (GTEx) Project was used to train BrainGENIE models to predict gene-expression levels in ten distinct brain regions using whole-blood gene-expression profiles. The performance of BrainGENIE was compared to PrediXcan, a popular method for imputing gene expression levels from genotypes. BrainGENIE significantly predicted brain tissue-specific expression levels for 2947-11,816 genes (false-discovery rate-adjusted p < 0.05), including many transcripts that cannot be predicted significantly by a transcriptome-imputation method such as PrediXcan. BrainGENIE recapitulated measured diagnosis-related gene-expression changes in the brain for autism, bipolar disorder, and schizophrenia better than direct correlations from blood and predictions from PrediXcan. We developed a convenient software toolset for deploying BrainGENIE, and provide recommendations for how best to implement models. BrainGENIE complements and, in some ways, outperforms existing transcriptome-imputation tools, providing biologically meaningful predictions and opening new research avenues.

Hess Jonathan L, Quinn Thomas P, Zhang Chunling, Hearn Gentry C, Chen Samuel, Kong Sek Won, Cairns Murray, Tsuang Ming T, Faraone Stephen V, Glatt Stephen J

2023-Mar-22

General

Deep Survival Analysis With Clinical Variables for COVID-19.

In IEEE journal of translational engineering in health and medicine

OBJECTIVE : Millions of people have been affected by coronavirus disease 2019 (COVID-19), which has caused millions of deaths around the world. Artificial intelligence (AI) plays an increasing role in all areas of patient care, including prognostics. This paper proposes a novel predictive model based on one dimensional convolutional neural networks (1D CNN) to use clinical variables in predicting the survival outcome of COVID-19 patients.

METHODS AND PROCEDURES : We have considered two scenarios for survival analysis, 1) uni-variate analysis using the Log-rank test and Kaplan-Meier estimator and 2) combining all clinical variables ([Formula: see text]=44) for predicting the short-term from long-term survival. We considered the random forest (RF) model as a baseline model, comparing to our proposed 1D CNN in predicting survival groups.

RESULTS : Our experiments using the univariate analysis show that nine clinical variables are significantly associated with the survival outcome with corrected p < 0.05. Our approach of 1D CNN shows a significant improvement in performance metrics compared to the RF and the state-of-the-art techniques (i.e., 1D CNN) in predicting the survival group of patients with COVID-19.

CONCLUSION : Our model has been tested using clinical variables, where the performance is found promising. The 1D CNN model could be a useful tool for detecting the risk of mortality and developing treatment plans in a timely manner.

CLINICAL IMPACT : The findings indicate that using both Heparin and Exnox for treatment is typically the most useful factor in predicting a patient's chances of survival from COVID-19. Moreover, our predictive model shows that the combination of AI and clinical data can be applied to point-of-care services through fast-learning healthcare systems.

Chaddad Ahmad, Hassan Lama, Katib Yousef, Bouridane Ahmed

2023

CNN, COVID-19, clinical variables

Radiology

Deep-learning-based ensemble method for fully automated detection of renal masses on magnetic resonance images.

In Journal of medical imaging (Bellingham, Wash.)

PURPOSE : Accurate detection of small renal masses (SRM) is a fundamental step for automated classification of benign and malignant or indolent and aggressive renal tumors. Magnetic resonance image (MRI) may outperform computed tomography (CT) for SRM subtype differentiation due to improved tissue characterization, but is less explored compared to CT. The objective of this study is to autonomously detect SRM on contrast-enhanced magnetic resonance images (CE-MRI).

APPROACH : In this paper, we described a novel, fully automated methodology for accurate detection and localization of SRM on CE-MRI. We first determine the kidney boundaries using a U-Net convolutional neural network. We then search for SRM within the localized kidney regions using a mixture-of-experts ensemble model based on the U-Net architecture. Our dataset contained CE-MRI scans of 118 patients with different solid kidney tumor subtypes including renal cell carcinomas, oncocytomas, and fat-poor renal angiomyolipoma. We evaluated the proposed model on the entire CE-MRI dataset using 5-fold cross validation.

RESULTS : The developed algorithm reported a Dice similarity coefficient of 91.20 ± 5.41 % (mean ± standard deviation) for kidney segmentation from 118 volumes consisting of 25,025 slices. Our proposed ensemble model for SRM detection yielded a recall and precision of 86.2% and 83.3% on the entire CE-MRI dataset, respectively.

CONCLUSIONS : We described a deep-learning-based method for fully automated SRM detection using CE-MR images, which has not been studied previously. The results are clinically important as SRM localization is a pre-step for fully automated diagnosis of SRM subtypes.

Anush Agarwal, Rohini Gaikar, Nicola Schieda, WalaaEldin Elfaal Mohamed, Eranga Ukwatta

2023-Mar

U-Net, computer aided detection, deep learning, magnetic resonance imaging, renal masses

General

Intelligent antepartum fetal monitoring via deep learning and fusion of cardiotocographic signals and clinical data.

In Health information science and systems

PURPOSE : Cardiotocography (CTG), which measures uterine contraction (UC) and fetal heart rate (FHR), is a crucial tool for assessing fetal health during pregnancy. However, traditional computerized cardiotocography (cCTG) approaches have non-negligible calibration errors in feature extraction and heavily rely on the expertise and prior experience to define diagnostic features from CTG or FHR signals. Although previous works have studied deep learning methods for extracting CTG or FHR features, these methods still neglect the clinical information of pregnant women.

METHODS : In this paper, we proposed a multimodal deep learning architecture (MMDLA) for intelligent antepartum fetal monitoring that is capable of performing automatic CTG feature extraction, fusion with clinical data and classification. The multimodal feature fusion was achieved by concatenating high-level CTG features, which were extracted from preprocessed CTG signals via a convolution neural network (CNN) with six convolution layers and five fully connected layers, and the clinical data of pregnant women. Eventually, light gradient boosting machine (LGBM) was implemented as fetal status assessment classifier. The effectiveness of MMDLA was evaluated using a dataset of 16,355 cases, each of which includes FHR signal, UC signal and pertinent clinical data like maternal age and gestational age.

RESULTS : With an accuracy of 90.77% and an area under the curve (AUC) value of 0.9201, the multimodal features performed admirably. The data imbalance issue was also effectively resolved by the LGBM classifier, with a normal-F1 value of 0.9376 and an abnormal-F1 value of 0.8223.

CONCLUSION : In summary, the proposed MMDLA is conducive to the realization of intelligent antepartum fetal monitoring.

Cao Zhen, Wang Guoqiang, Xu Ling, Li Chaowei, Hao Yuexing, Chen Qinqun, Li Xia, Liu Guiqing, Wei Hang

2023-Dec

Antepartum fetal monitoring, Cardiotocographic signal, Clinical data, Convolutional neural network, Multimodal feature fusion

Pathology

USC-ENet: a high-efficiency model for the diagnosis of liver tumors combining B-mode ultrasound and clinical data.

In Health information science and systems

PURPOSE : Ultrasound image acquisition has the advantages of being low cost, rapid, and non-invasive, and it does not produce radiation. Currently, ultrasound is widely used in the diagnosis of liver tumors. However, owing to the complex presentation and diverse features of benign and malignant liver tumors, accurate diagnosis of liver tumors using ultrasound is difficult even for experienced radiologists. In recent years, artificial intelligence-assisted diagnosis has proven to provide effective support to radiologists. However, there is room for further improvement in the existing ultrasound artificial intelligence diagnostic model of liver tumor. First, the image diagnostic model may not fully consider relevant clinical data in the decision-making process. Second, owing to the difficulty in collecting biopsy pathology and physician-labeled ultrasound data of liver tumors, training datasets are usually small, and commonly used large neural networks tend to overfit on small datasets, which seriously affects the generalization of the model.

METHODS : In this study, we propose a deep learning-assisted diagnosis model called USC-ENet, which integrates B-mode ultrasound features of liver tumors and clinical data of patients, and we design a small neural network specifically for small-scale medical images combined with an attention mechanism.

RESULTS AND CONCLUSION : Real data from 542 patients with liver tumors (N = 2168 images) are used during model training and validation. Experiments show that USC-ENet can achieve a good classification effect (area under the curve = 0.956, sensitivity = 0.915, and specificity = 0.880) after small-scale data training, and it has certain interpretability, showing good potential for clinical adoption. In conclusion, our model provides not only a reliable second opinion for radiologists but also a reference for junior radiologists who lack clinical experience.

Zhao Tingting, Zeng Zhiyong, Li Tong, Tao Wenjing, Yu Xing, Feng Tao, Bu Rui

2023-Dec

B-mode ultrasound, EfficientNet, Feature fusion, Liver tumor

Public Health

COVID-19Base v3: Update of the knowledgebase for drugs and biomedical entities linked to COVID-19.

In Frontiers in public health

COVID-19 has taken a huge toll on our lives over the last 3 years. Global initiatives put forward by all stakeholders are still in place to combat this pandemic and help us learn lessons for future ones. While the vaccine rollout was not able to curb the spread of the disease for all strains, the research community is still trying to develop effective therapeutics for COVID-19. Although Paxlovid and remdesivir have been approved by the FDA against COVID-19, they are not free of side effects. Therefore, the search for a therapeutic solution with high efficacy continues in the research community. To support this effort, in this latest version (v3) of COVID-19Base, we have summarized the biomedical entities linked to COVID-19 that have been highlighted in the scientific literature after the vaccine rollout. Eight different topic-specific dictionaries, i.e., gene, miRNA, lncRNA, PDB entries, disease, alternative medicines registered under clinical trials, drugs, and the side effects of drugs, were used to build this knowledgebase. We have introduced a BLSTM-based deep-learning model to predict the drug-disease associations that outperforms the existing model for the same purpose proposed in the earlier version of COVID-19Base. For the very first time, we have incorporated disease-gene, disease-miRNA, disease-lncRNA, and drug-PDB associations covering the largest number of biomedical entities related to COVID-19. We have provided examples of and insights into different biomedical entities covered in COVID-19Base to support the research community by incorporating all of these entities under a single platform to provide evidence-based support from the literature. COVID-19Base v3 can be accessed from: https://covidbase-v3.vercel.app/. The GitHub repository for the source code and data dictionaries is available to the community from: https://github.com/91Abdullah/covidbasev3.0.

Basit Syed Abdullah, Qureshi Rizwan, Musleh Saleh, Guler Reto, Rahman M Sohel, Biswas Kabir H, Alam Tanvir

2023

CORD-19, COVID-19, SARS-CoV-2, deep learning, machine learning

General

Structures, band gaps, and formation energies of highly stable phases of inorganic ABX3 halides: A = Li, Na, K, Rb, Cs, Tl; B = Be, Mg, Ca, Ge, Sr, Sn, Pb; and X = F, Cl, Br, I.

In RSC advances

Recently, halide perovskites have attracted a substantial attention. Although the focus was mostly on hybrid ones with organic polyatomic cations and with inadequate stability, there is a sizable inorganic halide space that is not well explored and may be more stable than hybrid perovskites. In this work, a robust automated framework is used to calculate the essential properties of the highly stable phases of 168 inorganic halide perovskites. The considered space of ABX₃ compounds consists of A = Li, Na, K, Rb, Cs, Tl, B = Be, Mg, Ca, Ge, Sr, Sn, Pb, and X = F, Cl, Br, I. The targeted properties are the structure, the formation energy to assess stability, and the energy gap for potential applicability. The calculations are carried out using the density functional theory (DFT) integrated with the precision library of Standard Solid-State Pseudopotentials (SSSP) for structure relaxation and PseudoDojo for energy gap calculation. Furthermore, we adopted a very sufficient and robust random sampling to identify the highly stable phases. The results illustrated that only 118 of the possible 168 compounds are formidable and have reliable results. The remaining 50 compounds are either not formidable or suffer from computational inconsistencies.

Alqahtani Saad M, Alsayoud Abduljabar Q, Alharbi Fahhad H

2023-Mar-14

General

Speed-resolved perfusion imaging using multi-exposure laser speckle contrast imaging and machine learning.

In Journal of biomedical optics

SIGNIFICANCE : Laser speckle contrast imaging (LSCI) gives a relative measure of microcirculatory perfusion. However, due to the limited information in single-exposure LSCI, models are inaccurate for skin tissue due to complex effects from e.g. static and dynamic scatterers, multiple Doppler shifts, and the speed-distribution of blood. It has been demonstrated how to account for these effects in laser Doppler flowmetry (LDF) using inverse Monte Carlo (MC) algorithms. This allows for a speed-resolved perfusion measure in absolute units %RBC × mm/s, improving the physiological interpretation of the data. Until now, this has been limited to a single-point LDF technique but recent advances in multi-exposure LSCI (MELSCI) enable the analysis in an imaging modality.

AIM : To present a method for speed-resolved perfusion imaging in absolute units %RBC × mm/s, computed from multi-exposure speckle contrast images.

APPROACH : An artificial neural network (ANN) was trained on a large simulated dataset of multi-exposure contrast values and corresponding speed-resolved perfusion. The dataset was generated using MC simulations of photon transport in randomized skin models covering a wide range of physiologically relevant geometrical and optical tissue properties. The ANN was evaluated on in vivo data sets captured during an occlusion provocation.

RESULTS : Speed-resolved perfusion was estimated in the three speed intervals 0 to 1    mm / s , 1 to 10    mm / s , and > 10    mm / s , with relative errors 9.8%, 12%, and 19%, respectively. The perfusion had a linear response to changes in both blood tissue fraction and blood flow speed and was less affected by tissue properties compared with single-exposure LSCI. The image quality was subjectively higher compared with LSCI, revealing previously unseen macro- and microvascular structures.

CONCLUSIONS : The ANN, trained on modeled data, calculates speed-resolved perfusion in absolute units from multi-exposure speckle contrast. This method facilitates the physiological interpretation of measurements using MELSCI and may increase the clinical impact of the technique.

Hultman Martin, Larsson Marcus, Strömberg Tomas, Fredriksson Ingemar

2023-Mar

artificial neural networks, blood flow, microcirculation, multi-exposure laser speckle contrast imaging

Ophthalmology

TP53-mediated clonal hematopoiesis confers increased risk for incident atherosclerotic disease.

In Nature cardiovascular research

Somatic mutations in blood indicative of clonal hematopoiesis of indeterminate potential (CHIP) are associated with an increased risk of hematologic malignancy, coronary artery disease, and all-cause mortality. Here we analyze the relation between CHIP status and incident peripheral artery disease (PAD) and atherosclerosis, using whole-exome sequencing and clinical data from the UK Biobank and Mass General Brigham Biobank. CHIP associated with incident PAD and atherosclerotic disease across multiple beds, with increased risk among individuals with CHIP driven by mutation in DNA Damage Repair (DDR) genes such as TP53 and PPM1D. To model the effects of DDR-induced CHIP on atherosclerosis, we used a competitive bone marrow transplantation strategy, and generated atherosclerosis-prone Ldlr-/- chimeric mice carrying 20% p53-deficient hematopoietic cells. The chimeric mice were analyzed 13-weeks post-grafting and showed increased aortic plaque size and accumulation of macrophages within the plaque, driven by increased proliferation of p53-deficient plaque macrophages. In summary, our findings highlight the role of CHIP as a broad driver of atherosclerosis across the entire arterial system beyond the coronary arteries, and provide genetic and experimental support for a direct causal contribution of TP53-mutant CHIP to atherosclerosis.

Zekavat Seyedeh M, Viana-Huete Vanesa, Matesanz Nuria, Jorshery Saman Doroodgar, Zuriaga María A, Uddin Md Mesbah, Trinder Mark, Paruchuri Kaavya, Zorita Virginia, Ferrer-Pérez Alba, Amorós-Pérez Marta, Kunderfranco Paolo, Carriero Roberta, Greco Carolina M, Aroca-Crevillen Alejandra, Hidalgo Andrés, Damrauer Scott M, Ballantyne Christie M, Niroula Abhishek, Gibson Christopher J, Pirruccello James, Griffin Gabriel, Ebert Benjamin L, Libby Peter, Fuster Valentín, Zhao Hongyu, Ghassemi Marzyeh, Natarajan Pradeep, Bick Alexander G, Fuster José J, Klarin Derek

2023-Jan-16

atherosclerosis, clonal hematopoiesis, sequencing, somatic

General

Inferring influence of people's emotions at court on defendant's emotions using a prediction model.

In Frontiers in psychology ; h5-index 92.0

People's emotions may be affected by the sound environment in court. A courtroom's sound environment usually consists of the people's voices, such as the judge's voice, the plaintiff's voice, and the defendant's voice. The judge, plaintiff, and defendant usually express their emotions through their voices. Human communication is heavily reliant on emotions. Emotions may also reflect a person's condition. Therefore, People's emotions at the Court must be recognized, especially for vulnerable groups, and the impact of the sound on the defendant's motions and judgment must be inferred. However, people's emotions are difficult to recognize in a courtroom. In addition, as far as we know, no existing study deals with the impact of sound on people in court. Based on sound perception, we develop a deep neural network-based model to infer people's emotions in our previous work. In the proposed model, we use the convolutional neural network and long short-term memory network to obtain features from speech signals and apply a dense neural network to infer people's emotions. Applying the model for emotion prediction based on sound at court, we explore the impact of sound at court on the defendant. Using the voice data collected from fifty trail records, we demonstrate that the voice of the judge can affect the defendant's emotions. Angry, neutrality and fear are the top three emotions of the defendant in court. In particular, the judge's voice expressing anger usually induces fear in the defendant. The plaintiff's angry voice may not have a substantial impact on the defendant's emotions.

Song Yun, Zhao Tianyi

2023

AI in law, deep learning, emotion at court, emotion prediction, judgement

General

Prediction of the Prognosis of Clear Cell Renal Cell Carcinoma by Cuproptosis-Related lncRNA Signals Based on Machine Learning and Construction of ceRNA Network.

In Journal of oncology

BACKGROUND : Clear cell renal cell carcinoma's (ccRCC) occurrence and development are strongly linked to the metabolic reprogramming of tumors, and thus far, neither its prognosis nor treatment has achieved satisfying clinical outcomes.

METHODS : The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, respectively, provided us with information on the RNA expression of ccRCC patients and their clinical data. Cuproptosis-related genes (CRGS) were discovered in recent massive research. With the help of log-rank testing and univariate Cox analysis, the prognostic significance of CRGS was examined. Different cuproptosis subtypes were identified using consensus clustering analysis, and GSVA was used to further investigate the likely signaling pathways between various subtypes. Univariate Cox, least absolute shrinkage and selection operator (Lasso), random forest (RF), and multivariate stepwise Cox regression analysis were used to build prognostic models. After that, the models were verified by means of the C index, Kaplan-Meier (K-M) survival curves, and time-dependent receiver operating characteristic (ROC) curves. The association between prognostic models and the tumor immune microenvironment as well as the relationship between prognostic models and immunotherapy were next examined using ssGSEA and TIDE analysis. Four online prediction websites-Mircode, MiRDB, MiRTarBase, and TargetScan-were used to build a lncRNA-miRNA-mRNA ceRNA network.

RESULTS : By consensus clustering, two subgroups of cuproptosis were identified that represented distinct prognostic and immunological microenvironments.

CONCLUSION : A prognostic risk model with 13 CR-lncRNAs was developed. The immune microenvironment and responsiveness to immunotherapy are substantially connected with the model, which may reliably predict the prognosis of patients with ccRCC.

Xiao Zhiliang, Zhang Menglei, Shi Zhenduo, Zang Guanghui, Liang Qing, Hao Lin, Dong Yang, Pang Kun, Wang Yabin, Han Conghui

2023

Public Health

Blood Inflammatory Biomarkers Differentiate Inpatient and Outpatient Coronavirus Disease 2019 From Influenza.

In Open forum infectious diseases

BACKGROUND : The ongoing circulation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses a diagnostic challenge because symptoms of coronavirus disease 2019 (COVID-19) are difficult to distinguish from other respiratory diseases. Our goal was to use statistical analyses and machine learning to identify biomarkers that distinguish patients with COVID-19 from patients with influenza.

METHODS : Cytokine levels were analyzed in plasma and serum samples from patients with influenza and COVID-19, which were collected as part of the Centers for Disease Control and Prevention's Hospitalized Adult Influenza Vaccine Effectiveness Network (inpatient network) and the US Flu Vaccine Effectiveness (outpatient network).

RESULTS : We determined that interleukin (IL)-10 family cytokines are significantly different between COVID-19 and influenza patients. The results suggest that the IL-10 family cytokines are a potential diagnostic biomarker to distinguish COVID-19 and influenza infection, especially for inpatients. We also demonstrate that cytokine combinations, consisting of up to 3 cytokines, can distinguish SARS-CoV-2 and influenza infection with high accuracy in both inpatient (area under the receiver operating characteristics curve [AUC] = 0.84) and outpatient (AUC = 0.81) groups, revealing another potential screening tool for SARS-CoV-2 infection.

CONCLUSIONS : This study not only reveals prospective screening tools for COVID-19 infections that are independent of polymerase chain reaction testing or clinical condition, but it also emphasizes potential pathways involved in disease pathogenesis that act as potential targets for future mechanistic studies.

Luciani Lauren L, Miller Leigh M, Zhai Bo, Clarke Karen, Hughes Kramer Kailey, Schratz Lucas J, Balasubramani G K, Dauer Klancie, Nowalk M Patricia, Zimmerman Richard K, Shoemaker Jason E, Alcorn John F

2023-Mar

SARS-CoV-2, cytokine, human, machine learning, pneumonia

General

Brightfield vs Fluorescent Staining Dataset-A Test Bed Image Set for Machine Learning based Virtual Staining.

In Scientific data

Differential fluorescent staining is an effective tool widely adopted for the visualization, segmentation and quantification of cells and cellular substructures as a part of standard microscopic imaging protocols. Incompatibility of staining agents with viable cells represents major and often inevitable limitations to its applicability in live experiments, requiring extraction of samples at different stages of experiment increasing laboratory costs. Accordingly, development of computerized image analysis methodology capable of segmentation and quantification of cells and cellular substructures from plain monochromatic images obtained by light microscopy without help of any physical markup techniques is of considerable interest. The enclosed set contains human colon adenocarcinoma Caco-2 cells microscopic images obtained under various imaging conditions with different viable vs non-viable cells fractions. Each field of view is provided in a three-fold representation, including phase-contrast microscopy and two differential fluorescent microscopy images with specific markup of viable and non-viable cells, respectively, produced using two different staining schemes, representing a prominent test bed for the validation of image analysis methods.

Trizna Elena Y, Sinitca Aleksandr M, Lyanova Asya I, Baidamshina Diana R, Zelenikhin Pavel V, Kaplun Dmitrii I, Kayumov Airat R, Bogachev Mikhail I

2023-Mar-22

Public Health

COVID-19Base v3: Update of the knowledgebase for drugs and biomedical entities linked to COVID-19.

In Frontiers in public health

COVID-19 has taken a huge toll on our lives over the last 3 years. Global initiatives put forward by all stakeholders are still in place to combat this pandemic and help us learn lessons for future ones. While the vaccine rollout was not able to curb the spread of the disease for all strains, the research community is still trying to develop effective therapeutics for COVID-19. Although Paxlovid and remdesivir have been approved by the FDA against COVID-19, they are not free of side effects. Therefore, the search for a therapeutic solution with high efficacy continues in the research community. To support this effort, in this latest version (v3) of COVID-19Base, we have summarized the biomedical entities linked to COVID-19 that have been highlighted in the scientific literature after the vaccine rollout. Eight different topic-specific dictionaries, i.e., gene, miRNA, lncRNA, PDB entries, disease, alternative medicines registered under clinical trials, drugs, and the side effects of drugs, were used to build this knowledgebase. We have introduced a BLSTM-based deep-learning model to predict the drug-disease associations that outperforms the existing model for the same purpose proposed in the earlier version of COVID-19Base. For the very first time, we have incorporated disease-gene, disease-miRNA, disease-lncRNA, and drug-PDB associations covering the largest number of biomedical entities related to COVID-19. We have provided examples of and insights into different biomedical entities covered in COVID-19Base to support the research community by incorporating all of these entities under a single platform to provide evidence-based support from the literature. COVID-19Base v3 can be accessed from: https://covidbase-v3.vercel.app/. The GitHub repository for the source code and data dictionaries is available to the community from: https://github.com/91Abdullah/covidbasev3.0.

Basit Syed Abdullah, Qureshi Rizwan, Musleh Saleh, Guler Reto, Rahman M Sohel, Biswas Kabir H, Alam Tanvir

2023

CORD-19, COVID-19, SARS-CoV-2, deep learning, machine learning

General

Smart Yoga Instructor for Guiding and Correcting Yoga Postures in Real Time.

In International journal of yoga

In recent days, Yoga is gaining more prominence and people all over the world have started to practice it. Performing Yoga with proper postures is beneficial. Hence, an instructor is required to monitor the correctness of Yoga postures. However, at times, it is difficult to have an instructor. This study aims to provide a system that will act as a personal Yoga instructor and practitioners can practice Yoga in their comfort zone. The device is interactive and provides audio guidance to perform different Yoga asanas. It makes the use of a camera to capture the picture of the person performing Yoga in a particular position. This captured pose is compared with the benchmark postures. A pretrained deep learning model is used for the classification of different Yoga postures using a standard dataset. Based on the comparison, the practitioner's posture will be corrected using a voice message to move the body parts in a certain direction. As the device performs all the operations in real-time, it has a quick response time of a few seconds. Currently, this work aids the practitioners in performing five Asanas, namely, Ardha Chandrasana/Half-moon pose, Tadasana/Mountain pose, Trikonasana/Triangular pose, Veerabhadrasana/Warrior pose, and Vrikshasana/Tree pose.

Kishore D Mohan, Bindu S, Manjunath Nandi Krishnamurthy

2022

Human posture recognition, Mediapipe, Yoga, pose detection and pose correction, real-time

General

scAnnotate: an automated cell-type annotation tool for single-cell RNA-sequencing data.

In Bioinformatics advances

MOTIVATION : Single-cell RNA-sequencing (scRNA-seq) technology enables researchers to investigate a genome at the cellular level with unprecedented resolution. An organism consists of a heterogeneous collection of cell types, each of which plays a distinct role in various biological processes. Hence, the first step of scRNA-seq data analysis is often to distinguish cell types so they can be investigated separately. Researchers have recently developed several automated cell-type annotation tools, requiring neither biological knowledge nor subjective human decisions. Dropout is a crucial characteristic of scRNA-seq data widely used in differential expression analysis. However, no current cell annotation method explicitly utilizes dropout information. Fully utilizing dropout information motivated this work.

RESULTS : We present scAnnotate, a cell annotation tool that fully utilizes dropout information. We model every gene's marginal distribution using a mixture model, which describes both the dropout proportion and the distribution of the non-dropout expression levels. Then, using an ensemble machine learning approach, we combine the mixture models of all genes into a single model for cell-type annotation. This combining approach can avoid estimating numerous parameters in the high-dimensional joint distribution of all genes. Using 14 real scRNA-seq datasets, we demonstrate that scAnnotate is competitive against nine existing annotation methods. Furthermore, because of its distinct modelling strategy, scAnnotate's misclassified cells differ greatly from competitor methods. This suggests using scAnnotate together with other methods could further improve annotation accuracy.

AVAILABILITY AND IMPLEMENTATION : We implemented scAnnotate as an R package and made it publicly available from CRAN: https://cran.r-project.org/package=scAnnotate.

SUPPLEMENTARY INFORMATION : Supplementary data are available at Bioinformatics Advances online.

Ji Xiangling, Tsao Danielle, Bai Kailun, Tsao Min, Xing Li, Zhang Xuekui

2023

General

Multiplexed DNA-functionalized graphene sensor with artificial intelligence-based discrimination performance for analyzing chemical vapor compositions.

In Microsystems & nanoengineering

This study presents a new technology that can detect and discriminate individual chemical vapors to determine the chemical vapor composition of mixed chemical composition in situ based on a multiplexed DNA-functionalized graphene (MDFG) nanoelectrode without the need to condense the original vapor or target dilution. To the best of our knowledge, our artificial intelligence (AI)-operated arrayed electrodes were capable of identifying the compositions of mixed chemical gases with a mixed ratio in the early stage. This innovative technology comprised an optimized combination of nanodeposited arrayed electrodes and artificial intelligence techniques with advanced sensing capabilities that could operate within biological limits, resulting in the verification of mixed vapor chemical components. Highly selective sensors that are tolerant to high humidity levels provide a target for "breath chemovapor fingerprinting" for the early diagnosis of diseases. The feature selection analysis achieved recognition rates of 99% and above under low-humidity conditions and 98% and above under humid conditions for mixed chemical compositions. The 1D convolutional neural network analysis performed better, discriminating the compositional state of chemical vapor under low- and high-humidity conditions almost perfectly. This study provides a basis for the use of a multiplexed DNA-functionalized graphene gas sensor array and artificial intelligence-based discrimination of chemical vapor compositions in breath analysis applications.

Hwang Yun Ji, Yu Heejin, Lee Gilho, Shackery Iman, Seong Jin, Jung Youngmo, Sung Seung-Hyun, Choi Jongeun, Jun Seong Chan

2023

Computational nanotechnology, Electronic properties and materials, Nanosensors

General

Astrocyte structural heterogeneity in the mouse hippocampus.

In Glia

Astrocytes are integral components of brain circuits, where they sense, process, and respond to surrounding activity, maintaining homeostasis and regulating synaptic transmission, the sum of which results in behavior modulation. These interactions are possible due to their complex morphology, composed of a tree-like structure of processes to cover defined territories ramifying in a mesh-like system of fine leaflets unresolved by conventional optic microscopy. While recent reports devoted more attention to leaflets and their dynamic interactions with synapses, our knowledge about the tree-like "backbone" structure in physiological conditions is incomplete. Recent transcriptomic studies described astrocyte molecular diversity, suggesting structural heterogeneity in regions such as the hippocampus, which is crucial for cognitive and emotional behaviors. In this study, we carried out the structural analysis of astrocytes across the hippocampal subfields of Cornu Ammonis area 1 (CA1) and dentate gyrus in the dorsoventral axis. We found that astrocytes display heterogeneity across the hippocampal subfields, which is conserved along the dorsoventral axis. We further found that astrocytes appear to contribute in an exocytosis-dependent manner to a signaling loop that maintains the backbone structure. These findings reveal astrocyte heterogeneity in the hippocampus, which appears to follow layer-specific cues and depend on the neuro-glial environment.

Viana João Filipe, Machado João Luís, Abreu Daniela Sofia, Veiga Alexandra, Barsanti Sara, Tavares Gabriela, Martins Manuella, Sardinha Vanessa Morais, Guerra-Gomes Sónia, Domingos Cátia, Pauletti Alberto, Wahis Jérôme, Liu Chen, Calì Corrado, Henneberger Christian, Holt Matthew G, Oliveira João Filipe

2023-Mar-22

astrocyte, dorsal, hippocampus, morphology, skeleton, ventral

General

Artificial intelligence's interpretation of the neuroanatomical aspect of Peter Paul Rubens's copy of "The Battle of Anghiari" by Leonardo da Vinci.

In Perception

We tested to see how Ruben's copy of "The Battle of Anghiari" by Leonardo da Vinci would be interpreted by AI in a neuroanatomical aspect. We used WOMBO Dream, an artificial intelligence (AI)-based algorithm that creates images based on words and figures. The keyword we provided for the algorithm was "brain" and the reference image was Ruben's drawing. AI interpreted the whole drawing as a representation of the brain. The image generated by the algorithm was similar to our interpretation of the same painting.

Keshelava Grigol

2023-Mar-22

Leonardo da Vinci, Renaissance, The Battle of Anghiari, artificial intelligence, brain anatomy

General

[Identifying Depressive Disorder With Sleep Electroencephalogram Data: A Study Based on Deep Learning].

In Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition

OBJECTIVE : To explore the effectiveness of using deep learning network combined Vision Transformer (ViT) and Transformer to identify patients with depressive disorder on the basis of their sleep electroencephalogram (EEG) signals.

METHODS : The sleep EEG signals of 28 patients with depressive disorder and 37 normal controls were preprocessed. Then, the signals were converted into image format and the feature information on frequency domain and spatial domain was retained. After that, the images were transmitted to the ViT-Transformer coding network for deep learning of the EEG signal characteristics of the rapid eye movement (REM) sleep and non-rapid eye movement (NREM) sleep in patients with depressive disorder and those in normal controls, respectively, and to identify patients with depressive disorder.

RESULTS : Based on the ViT-Transformer network, after examining different EEG frequencies, we found that the combination of delta, theta, and beta waves produced better results in identifying depressive disorder. Among the different EEG frequencies, EEG signal features of delta-theta-beta combination waves in REM sleep achieved 92.8% accuracy and 93.8% precision for identifying depression, with the recall rate of patients with depression being 84.7%, and the F_0.5 value being 0.917±0.074. When using the delta-theta-beta combination EEG signal features in NREM sleep to identify depressive disorder, the accuracy was 91.7%, the precision was 90.8%, the recall rate was 85.2%, and the F_0.5 value was 0.914±0.062. In addition, through visualization of the sleep EEG of different sleep stages for the whole night, it was found that classification errors usually occurred during transition to a different sleep stage.

CONCLUSION : Using the deep learning ViT-Transformer network, we found that the EEG signal features in REM sleep based on delta-theta-beta combination waves showed better effect in identifying depressive disorder.

Tao Ran, Ding Sheng-Nan, Chen Jie, Zhu Xue-Min, Ni Zhao-Jun, Hu Ling-Ming, Zhang Yang, Xu Yan, Sun Hong-Qiang

2023-Mar

Deep learning, Depressive disorder, Non-rapid eye movement sleep, Rapid eye movement sleep, Sleep electroencephalogram

General

An agile, data-driven approach for target selection in rTMS therapy for anxiety symptoms: Proof of concept and preliminary data for two novel targets.

In Brain and behavior

INTRODUCTION : Data-driven approaches to transcranial magnetic stimulation (TMS) might yield more consistent and symptom-specific results based on individualized functional connectivity analyses compared to previous traditional approaches due to more precise targeting. We provide a proof of concept for an agile target selection paradigm based on using connectomic methods that can be used to detect patient-specific abnormal functional connectivity, guide treatment aimed at the most abnormal regions, and optimize the rapid development of new hypotheses for future study.

METHODS : We used the resting-state functional MRI data of 28 patients with medically refractory generalized anxiety disorder to perform agile target selection based on abnormal functional connectivity patterns between the Default Mode Network (DMN) and Central Executive Network (CEN). The most abnormal areas of connectivity within these regions were selected for subsequent targeted TMS treatment by a machine learning based on an anomalous functional connectivity detection matrix. Areas with mostly hyperconnectivity were stimulated with continuous theta burst stimulation and the converse with intermittent theta burst stimulation. An image-guided accelerated theta burst stimulation paradigm was used for treatment.

RESULTS : Areas 8Av and PGs demonstrated consistent abnormalities, particularly in the left hemisphere. Significant improvements were demonstrated in anxiety symptoms, and few, minor complications were reported (fatigue (n = 2) and headache (n = 1)).

CONCLUSIONS : Our study suggests that a left-lateralized DMN is likely the primary functional network disturbed in anxiety-related disorders, which can be improved by identifying and targeting abnormal regions with a rapid, data-driven, agile aTBS treatment on an individualized basis.

Young Isabella M, Taylor Hugh M, Nicholas Peter J, Mackenzie Alana, Tanglay Onur, Dadario Nicholas B, Osipowicz Karol, Davis Ethan, Doyen Stephane, Teo Charles, Sughrue Michael E

2023-Mar-22

anxiety, brain stimulation, repetitive transcranial magnetic stimulation, treatment

General

Evidence for the role of transcription factors in the co-transcriptional regulation of intron retention.

In Genome biology ; h5-index 114.0

BACKGROUND : Alternative splicing is a widespread regulatory phenomenon that enables a single gene to produce multiple transcripts. Among the different types of alternative splicing, intron retention is one of the least explored despite its high prevalence in both plants and animals. The recent discovery that the majority of splicing is co-transcriptional has led to the finding that chromatin state affects alternative splicing. Therefore, it is plausible that transcription factors can regulate splicing outcomes.

RESULTS : We provide evidence for the hypothesis that transcription factors are involved in the regulation of intron retention by studying regions of open chromatin in retained and excised introns. Using deep learning models designed to distinguish between regions of open chromatin in retained introns and non-retained introns, we identified motifs enriched in IR events with significant hits to known human transcription factors. Our model predicts that the majority of transcription factors that affect intron retention come from the zinc finger family. We demonstrate the validity of these predictions using ChIP-seq data for multiple zinc finger transcription factors and find strong over-representation for their peaks in intron retention events.

CONCLUSIONS : This work opens up opportunities for further studies that elucidate the mechanisms by which transcription factors affect intron retention and other forms of splicing.

AVAILABILITY : Source code available at https://github.com/fahadahaf/chromir.

Ullah Fahad, Jabeen Saira, Salton Maayan, Reddy Anireddy S N, Ben-Hur Asa

2023-Mar-22

Alternative splicing, Deep learning, Intron retention

Radiology

CT-based deep learning model for the prediction of DNA mismatch repair deficient colorectal cancer: a diagnostic study.

In Journal of translational medicine

BACKGROUND : Stratification of DNA mismatch repair (MMR) status in patients with colorectal cancer (CRC) enables individual clinical treatment decision making. The present study aimed to develop and validate a deep learning (DL) model based on the pre-treatment CT images for predicting MMR status in CRC.

METHODS : 1812 eligible participants (training cohort: n = 1124; internal validation cohort: n = 482; external validation cohort: n = 206) with CRC were enrolled from two institutions. All pretherapeutic CT images from three dimensions were trained by the ResNet101, then integrated by Gaussian process regression (GPR) to develop a full-automatic DL model for MMR status prediction. The predictive performance of the DL model was evaluated using the area under the receiver operating characteristic curve (AUC) and then tested in the internal and external validation cohorts. Additionally, the participants from institution 1 were sub-grouped by various clinical factors for subgroup analysis, then the predictive performance of the DL model for identifying MMR status between participants in different groups were compared.

RESULTS : The full-automatic DL model was established in the training cohort to stratify the MMR status, which presented promising discriminative ability with the AUCs of 0.986 (95% CI 0.971-1.000) in the internal validation cohort and 0.915 (95% CI 0.870-0.960) in the external validation cohort. In addition, the subgroup analysis based on the thickness of CT images, clinical T and N stages, gender, the longest diameter, and the location of tumors revealed that the DL model showed similar satisfying prediction performance.

CONCLUSIONS : The DL model may potentially serve as a noninvasive tool to facilitate the pre-treatment individualized prediction of MMR status in patients with CRC, which could promote the personalized clinical-making decision.

Cao Wuteng, Hu Huabin, Guo Jirui, Qin Qiyuan, Lian Yanbang, Li Jiao, Wu Qianyu, Chen Junhong, Wang Xinhua, Deng Yanhong

2023-Mar-22

Colorectal cancer, Computed Tomography, DNA mismatch repair, Deep learning, ResNet101

General

Multi-modal body part segmentation of infants using deep learning.

In Biomedical engineering online

BACKGROUND : Monitoring the body temperature of premature infants is vital, as it allows optimal temperature control and may provide early warning signs for severe diseases such as sepsis. Thermography may be a non-contact and wireless alternative to state-of-the-art, cable-based methods. For monitoring use in clinical practice, automatic segmentation of the different body regions is necessary due to the movement of the infant.

METHODS : This work presents and evaluates algorithms for automatic segmentation of infant body parts using deep learning methods. Based on a U-Net architecture, three neural networks were developed and compared. While the first two only used one imaging modality (visible light or thermography), the third applied a feature fusion of both. For training and evaluation, a dataset containing 600 visible light and 600 thermography images from 20 recordings of infants was created and manually labeled. In addition, we used transfer learning on publicly available datasets of adults in combination with data augmentation to improve the segmentation results.

RESULTS : Individual optimization of the three deep learning models revealed that transfer learning and data augmentation improved segmentation regardless of the imaging modality. The fusion model achieved the best results during the final evaluation with a mean Intersection-over-Union (mIoU) of 0.85, closely followed by the RGB model. Only the thermography model achieved a lower accuracy (mIoU of 0.75). The results of the individual classes showed that all body parts were well-segmented, only the accuracy on the torso is inferior since the models struggle when only small areas of the skin are visible.

CONCLUSION : The presented multi-modal neural networks represent a new approach to the problem of infant body segmentation with limited available data. Robust results were obtained by applying feature fusion, cross-modality transfer learning and classical augmentation strategies.

Voss Florian, Brechmann Noah, Lyra Simon, Rixen Jöran, Leonhardt Steffen, Hoog Antink Christoph

2023-Mar-22

Body part segmentation, Deep learning, Infrared thermography, NICU, Neonatal intensive care, Semantic segmentation

Radiology

Predicting breast cancer types on and beyond molecular level in a multi-modal fashion.

In NPJ breast cancer

Accurately determining the molecular subtypes of breast cancer is important for the prognosis of breast cancer patients and can guide treatment selection. In this study, we develop a deep learning-based model for predicting the molecular subtypes of breast cancer directly from the diagnostic mammography and ultrasound images. Multi-modal deep learning with intra- and inter-modality attention modules (MDL-IIA) is proposed to extract important relations between mammography and ultrasound for this task. MDL-IIA leads to the best diagnostic performance compared to other cohort models in predicting 4-category molecular subtypes with Matthews correlation coefficient (MCC) of 0.837 (95% confidence interval [CI]: 0.803, 0.870). The MDL-IIA model can also discriminate between Luminal and Non-Luminal disease with an area under the receiver operating characteristic curve of 0.929 (95% CI: 0.903, 0.951). These results significantly outperform clinicians' predictions based on radiographic imaging. Beyond molecular-level test, based on gene-level ground truth, our method can bypass the inherent uncertainty from immunohistochemistry test. This work thus provides a noninvasive method to predict the molecular subtypes of breast cancer, potentially guiding treatment selection for breast cancer patients and providing decision support for clinicians.

Zhang Tianyu, Tan Tao, Han Luyi, Appelman Linda, Veltman Jeroen, Wessels Ronni, Duvivier Katya M, Loo Claudette, Gao Yuan, Wang Xin, Horlings Hugo M, Beets-Tan Regina G H, Mann Ritse M

2023-Mar-22

Public Health

Blood Inflammatory Biomarkers Differentiate Inpatient and Outpatient Coronavirus Disease 2019 From Influenza.

In Open forum infectious diseases

BACKGROUND : The ongoing circulation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses a diagnostic challenge because symptoms of coronavirus disease 2019 (COVID-19) are difficult to distinguish from other respiratory diseases. Our goal was to use statistical analyses and machine learning to identify biomarkers that distinguish patients with COVID-19 from patients with influenza.

METHODS : Cytokine levels were analyzed in plasma and serum samples from patients with influenza and COVID-19, which were collected as part of the Centers for Disease Control and Prevention's Hospitalized Adult Influenza Vaccine Effectiveness Network (inpatient network) and the US Flu Vaccine Effectiveness (outpatient network).

RESULTS : We determined that interleukin (IL)-10 family cytokines are significantly different between COVID-19 and influenza patients. The results suggest that the IL-10 family cytokines are a potential diagnostic biomarker to distinguish COVID-19 and influenza infection, especially for inpatients. We also demonstrate that cytokine combinations, consisting of up to 3 cytokines, can distinguish SARS-CoV-2 and influenza infection with high accuracy in both inpatient (area under the receiver operating characteristics curve [AUC] = 0.84) and outpatient (AUC = 0.81) groups, revealing another potential screening tool for SARS-CoV-2 infection.

CONCLUSIONS : This study not only reveals prospective screening tools for COVID-19 infections that are independent of polymerase chain reaction testing or clinical condition, but it also emphasizes potential pathways involved in disease pathogenesis that act as potential targets for future mechanistic studies.

Luciani Lauren L, Miller Leigh M, Zhai Bo, Clarke Karen, Hughes Kramer Kailey, Schratz Lucas J, Balasubramani G K, Dauer Klancie, Nowalk M Patricia, Zimmerman Richard K, Shoemaker Jason E, Alcorn John F

2023-Mar

SARS-CoV-2, cytokine, human, machine learning, pneumonia

Radiology

Predictive factors for degenerative lumbar spinal stenosis: a model obtained from a machine learning algorithm technique.

In BMC musculoskeletal disorders ; h5-index 46.0

BACKGROUND : Degenerative lumbar spinal stenosis (DLSS) is the most common spine disease in the elderly population. It is usually associated with lumbar spine joints/or ligaments degeneration. Machine learning technique is an exclusive method for handling big data analysis; however, the development of this method for spine pathology is rare. This study aims to detect the essential variables that predict the development of symptomatic DLSS using the random forest of machine learning (ML) algorithms technique.

METHODS : A retrospective study with two groups of individuals. The first included 165 with symptomatic DLSS (sex ratio 80 M/85F), and the second included 180 individuals from the general population (sex ratio: 90 M/90F) without lumbar spinal stenosis symptoms. Lumbar spine measurements such as vertebral or spinal canal diameters from L1 to S1 were conducted on computerized tomography (CT) images. Demographic and health data of all the participants (e.g., body mass index and diabetes mellitus) were also recorded.

RESULTS : The decision tree model of ML demonstrate that the anteroposterior diameter of the bony canal at L5 (males) and L4 (females) levels have the greatest stimulus for symptomatic DLSS (scores of 1 and 0.938). In addition, combination of these variables with other lumbar spine features is mandatory for developing the DLSS.

CONCLUSIONS : Our results indicate that combination of lumbar spine characteristics such as bony canal and vertebral body dimensions rather than the presence of a sole variable is highly associated with symptomatic DLSS onset.

Abbas Janan, Yousef Malik, Peled Natan, Hershkovitz Israel, Hamoud Kamal

2023-Mar-23

Computer Tomography, Degenerative lumbar spinal stenosis, Machine learning, Spine dimensions

General

A study on predicting the length of hospital stay for Chinese patients with ischemic stroke based on the XGBoost algorithm.

In BMC medical informatics and decision making ; h5-index 38.0

BACKGROUND : The incidence of stroke is a challenge in China, as stroke imposes a heavy burden on families, national health services, social services, and the economy. The length of hospital stay (LOS) is an essential indicator of utilization of medical services and is usually used to assess the efficiency of hospital management and patient quality of care. This study established a prediction model based on a machine learning algorithm to predict ischemic stroke patients' LOS.

METHODS : A total of 18,195 ischemic stroke patients' electronic medical records and 28 attributes were extracted from electronic medical records in a large comprehensive hospital in China. The prediction of LOS was regarded as a multi classification problem, and LOS was divided into three categories: 1-7 days, 8-14 days and more than 14 days. After preprocessing the data and feature selection, the XGBoost algorithm was used to build a machine learning model. Ten fold cross-validation was used for model validation. The accuracy (ACC), recall rate (RE) and F1 measure were used to evaluate the performance of the prediction model of LOS of ischemic stroke patients. Finally, the XGBoost algorithm was used to identify and remove irrelevant features by ranking all attributes based on feature importance.

RESULTS : Compared with the naive Bayesian algorithm, logistic region algorithm, decision tree classifier algorithm and ADaBoost classifier algorithm, the XGBoot algorithm has higher ACC, RE and F1 measure. The average ACC, RE and F1 measure were 0.89, 0.89 and 0.89 under the 10-fold cross-validation. According to the analysis of the importance of features, the LOS of ischemic stroke patients was affected by demographic characteristics, past medical history, admission examination features, and operation characteristics. Finally, the features in terms of hemiplegia aphasia, MRS, NIHSS, TIA, Operation or not, coma index etc. were found to be the top features in importance in predicting the LOS of ischemic stroke patients.

CONCLUSIONS : The XGBoost algorithm was an appropriate machine learning method for predicting the LOS of patients with ischemic stroke. Based on the prediction model, an intelligent medical management prediction system could be developed to predict the LOS based on ischemic stroke patients' electronic medical records.

Chen Rui, Zhang Shengfa, Li Jie, Guo Dongwei, Zhang Weijun, Wang Xiaoying, Tian Donghua, Qu Zhiyong, Wang Xiaohua

2023-Mar-22

Ischemic stroke, Length of hospital stay (LOS), Machine learning (ML) model, XGBoost algorithm

General

Automated Adolescence Scoliosis Detection Using Augmented U-Net With Non-square Kernels.

In Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes

Purpose: Scoliosis is a deformity of the spine, and as a measure of scoliosis severity, Cobb angle is fundamental to the diagnosis of deformities that require treatment. Conventional Cobb angle measurement and assessment is usually done manually, which is inherently time-consuming, and associated with high inter- and intra-observer variability. While there exist automatic scoliosis measurement methods, they suffer from insufficient accuracy. In this work, we propose a two-step segmentation-based deep learning architecture to automate Cobb angle measurement for scoliosis assessment using X-Ray images. Methods: The proposed architecture involves two steps. In the first step, we utilize a novel Augmented U-Net architecture to generate segmentations of vertebrae. The second step includes a non-learning-based pipeline to extract landmark coordinates from the segmented vertebrae and filter undesirable landmarks. Results: Our proposed Augmented U-Net architecture achieved a Symmetric Mean Absolute Percentage Error of 9.2%, with approximately 90% of estimations having less than 10 degrees difference compared with the AASCE-MICCAI challenge 2019 dataset ground truths. We further validated the model using an internal dataset and achieved almost the same level of performance. Conclusion: The proposed architecture is robust in providing automated spinal vertebrae segmentations and Cobb angle measurement, and is potentially generalizable to real-world clinical settings.

Wu Yujie, Namdar Khashayar, Chen Chaojun, Hosseinpour Shahob, Shroff Manohar, Doria Andrea S, Khalvati Farzad

2023-Mar-22

Cobb angle, convolutional neural network, scoliosis, vertebra landmark

General

MSLP: mRNA subcellular localization predictor based on machine learning techniques.

In BMC bioinformatics

BACKGROUND : Subcellular localization of messenger RNA (mRNAs) plays a pivotal role in the regulation of gene expression, cell migration as well as in cellular adaptation. Experiment techniques for pinpointing the subcellular localization of mRNAs are laborious, time-consuming and expensive. Therefore, in silico approaches for this purpose are attaining great attention in the RNA community.

METHODS : In this article, we propose MSLP, a machine learning-based method to predict the subcellular localization of mRNA. We propose a novel combination of four types of features representing k-mer, pseudo k-tuple nucleotide composition (PseKNC), physicochemical properties of nucleotides, and 3D representation of sequences based on Z-curve transformation to feed into machine learning algorithm to predict the subcellular localization of mRNAs.

RESULTS : Considering the combination of the above-mentioned features, ennsemble-based models achieved state-of-the-art results in mRNA subcellular localization prediction tasks for multiple benchmark datasets. We evaluated the performance of our method  in ten subcellular locations, covering cytoplasm, nucleus, endoplasmic reticulum (ER), extracellular region (ExR), mitochondria, cytosol, pseudopodium, posterior, exosome, and the ribosome. Ablation study highlighted k-mer and PseKNC to be more dominant than other features for predicting cytoplasm, nucleus, and ER localizations. On the other hand, physicochemical properties and Z-curve based features contributed the most to ExR and mitochondria detection. SHAP-based analysis revealed the relative importance of features to provide better insights into the proposed approach.

AVAILABILITY : We have implemented a Docker container and API for end users to run their sequences on our model. Datasets, the code of API and the Docker are shared for the community in GitHub at: https://github.com/smusleh/MSLP .

Musleh Saleh, Islam Mohammad Tariqul, Qureshi Rizwan, Alajez Nihad, Alam Tanvir

2023-Mar-22

Localization prediction, Machine learning, RNA, Sequence analysis, Subcellular localization, mRNA

Internal Medicine

Anaemia in the first week may be associated with long-term mortality among critically ill patients: propensity score-based analyses.

In BMC emergency medicine

BACKGROUND : Anaemia is highly prevalent in critically ill patients; however, the long-term effect on mortality remains unclear.

METHODS : We retrospectively included patients admitted to the medical intensive care units (ICUs) during 2015-2020 at the Taichung Veterans General Hospital. The primary outcome of interest was one-year mortality, and hazard ratios (HRs) with 95% confidence intervals (CIs) were determined to assess the association. We used propensity score matching (PSM) and propensity score matching methods, including inverse probability of treatment weighting (IPTW) as well as covariate balancing propensity score (CBPS), in the present study.

RESULTS : A total of 7,089 patients were eligible for analyses, and 45.0% (3,189/7,089) of them had anaemia, defined by mean levels of haemoglobin being less than 10 g/dL. The standardised difference of covariates in this study were lower than 0.20 after matching and weighting. The application of CBPS further reduced the imbalance among covariates. We demonstrated a similar association, and adjusted HRs in original, PSM, IPTW and CBPS populations were 1.345 (95% CI 1.227-1.474), 1.265 (95% CI 1.145-1.397), 1.276 (95% CI 1.142-1.427) and 1.260 (95% CI 1.125-1.411), respectively.

CONCLUSIONS : We used propensity score-based analyses to identify that anaemia within the first week was associated with increased one-year mortality in critically ill patients.

Lin I-Hung, Liao Pei-Ya, Wong Li-Ting, Chan Ming-Cheng, Wu Chieh-Liang, Chao Wen-Cheng

2023-Mar-22

Anaemia, Critical illness, Long-term outcome, Propensity score

General

Tile-based microscopic image processing for malaria screening using a deep learning approach.

In BMC medical imaging

BACKGROUND : Manual microscopic examination remains the golden standard for malaria diagnosis. But it is laborious, and pathologists with experience are needed for accurate diagnosis. The need for computer-aided diagnosis methods is driven by the enormous workload and difficulties associated with manual microscopy based examination. While the importance of computer-aided diagnosis is increasing at an enormous pace, fostered by the advancement of deep learning algorithms, there are still challenges in detecting small objects such as malaria parasites in microscopic images of blood films. The state-of-the-art (SOTA) deep learning-based object detection models are inefficient in detecting small objects accurately because they are underrepresented on benchmark datasets. The performance of these models is affected by the loss of detailed spatial information due to in-network feature map downscaling. This is due to the fact that the SOTA models cannot directly process high-resolution images due to their low-resolution network input layer.

METHODS : In this study, an efficient and robust tile-based image processing method is proposed to enhance the performance of malaria parasites detection SOTA models. Three variants of YOLOV4-based object detectors are adopted considering their detection accuracy and speed. These models were trained using tiles generated from 1780 high-resolution P. falciparum-infected thick smear microscopic images. The tiling of high-resolution images improves the performance of the object detection models. The detection accuracy and the generalization capability of these models have been evaluated using three datasets acquired from different regions.

RESULTS : The best-performing model using the proposed tile-based approach outperforms the baseline method significantly (Recall, [95.3%] vs [57%] and Average Precision, [87.1%] vs [76%]). Furthermore, the proposed method has outperformed the existing approaches that used different machine learning techniques evaluated on similar datasets.

CONCLUSIONS : The experimental results show that the proposed method significantly improves P. falciparum detection from thick smear microscopic images while maintaining real-time detection speed. Furthermore, the proposed method has the potential to assist and reduce the workload of laboratory technicians in malaria-endemic remote areas of developing countries where there is a critical skill gap and a shortage of experts.

Shewajo Fetulhak Abdurahman, Fante Kinde Anlay

2023-Mar-22

Deep learning, Malaria, Object detection, Plasmodium falciparum, Thick smear microscopic image, Tile-based image processing, YOLOV4

General

Prediction of vancomycin initial dosage using artificial intelligence models applying ensemble strategy.

In BMC bioinformatics

BACKGROUND : Antibiotic resistance has become a global concern. Vancomycin is known as the last line of antibiotics, but its treatment index is narrow. Therefore, clinical dosing decisions must be made with the utmost care; such decisions are said to be "suitable" only when both "efficacy" and "safety" are considered. This study presents a model, namely the "ensemble strategy model," to predict the suitability of vancomycin regimens. The experimental data consisted of 2141 "suitable" and "unsuitable" patients tagged with a vancomycin regimen, including six diagnostic input attributes (sex, age, weight, serum creatinine, dosing interval, and total daily dose), and the dataset was normalized into a training dataset, a validation dataset, and a test dataset. AdaBoost.M1, Bagging, fastAdaboost, Neyman-Pearson, and Stacking were used for model training. The "ensemble strategy concept" was then used to arrive at the final decision by voting to build a model for predicting the suitability of vancomycin treatment regimens.

RESULTS : The results of the tenfold cross-validation showed that the average accuracy of the proposed "ensemble strategy model" was 86.51% with a standard deviation of 0.006, and it was robust. In addition, the experimental results of the test dataset revealed that the accuracy, sensitivity, and specificity of the proposed method were 87.54%, 89.25%, and 85.19%, respectively. The accuracy of the five algorithms ranged from 81 to 86%, the sensitivity from 81 to 92%, and the specificity from 77 to 88%. Thus, the experimental results suggest that the model proposed in this study has high accuracy, high sensitivity, and high specificity.

CONCLUSIONS : The "ensemble strategy model" can be used as a reference for the determination of vancomycin doses in clinical treatment.

Ho Wen-Hsien, Huang Tian-Hsiang, Chen Yenming J, Zeng Lang-Yin, Liao Fen-Fen, Liou Yeong-Cheng

2023-Mar-22

Ensemble strategy, Monitoring of blood concentration of drugs, Therapeutic drug monitoring (TDM), Vancomycin

General

Dense reinforcement learning for safety validation of autonomous vehicles.

In Nature ; h5-index 368.0

One critical bottleneck that impedes the development and deployment of autonomous vehicles is the prohibitively high economic and time costs required to validate their safety in a naturalistic driving environment, owing to the rarity of safety-critical events¹. Here we report the development of an intelligent testing environment, where artificial-intelligence-based background agents are trained to validate the safety performances of autonomous vehicles in an accelerated mode, without loss of unbiasedness. From naturalistic driving data, the background agents learn what adversarial manoeuvre to execute through a dense deep-reinforcement-learning (D2RL) approach, in which Markov decision processes are edited by removing non-safety-critical states and reconnecting critical ones so that the information in the training data is densified. D2RL enables neural networks to learn from densified information with safety-critical events and achieves tasks that are intractable for traditional deep-reinforcement-learning approaches. We demonstrate the effectiveness of our approach by testing a highly automated vehicle in both highway and urban test tracks with an augmented-reality environment, combining simulated background vehicles with physical road infrastructure and a real autonomous test vehicle. Our results show that the D2RL-trained agents can accelerate the evaluation process by multiple orders of magnitude (10³ to 10⁵ times faster). In addition, D2RL will enable accelerated testing and training with other safety-critical autonomous systems.

Feng Shuo, Sun Haowei, Yan Xintao, Zhu Haojie, Zou Zhengxia, Shen Shengyin, Liu Henry X

2023-Mar

General

Forecasting PM2.5 concentrations using statistical modeling for Bengaluru and Delhi regions.

In Environmental monitoring and assessment

India is home to some of the most polluted cities on the planet. The worsening air quality in most of the cities has gone to an extent of causing severe impact on human health and life expectancy. An early warning system where people are alerted well before an adverse air quality episode can go a long way in preventing exposure to harmful air conditions. Having such system can also help the government to take better mitigation and preventive measures. Forecasting systems based on machine learning are gaining importance due to their cost-effectiveness and applicability to small towns and villages, where most complex models are not feasible due to resource constraints and limited data availability. This paper presents a study of air quality forecasting by application of statistical models. Three statistical models based on autoregression (AR), moving average (MA), and autoregressive integrated moving average (ARIMA) models were applied to the datasets of PM_2.5 concentrations of Delhi and Bengaluru, and forecasting was done for 1-day-ahead and 7-day-ahead time frames. All three models forecasted the PM_2.5 reasonably well for Bengaluru, but the model performance deteriorated for the Delhi region. The AR, MA, and ARIMA models achieved mean absolute percentage error (MAPE) of 10.82%, 7.94%, and 8.17% respectively for forecast of 7 days and MAPE of 7.35%, 5.62%, and 5.87% for 1-day-ahead forecasts for Bengaluru. For the Delhi region, the model gave an MAPE of 27.82%, 24.62%, and 27.32% for the AR, MA, and ARIMA models respectively in the 7-day-ahead forecast, and 24.48%, 23.53%, and 23.72% respectively for 1-day-ahead forecast. The analysis showed that ARIMA model performs better in comparison to the other models but performance varies with varying concentration regimes. Study indicates that other topographical and meteorological parameters need to be incorporated to develop better models and account for the effects of these parameters in the study.

Agarwal Akash, Sahu Manoranjan

2023-Mar-23

ARIMA, Air quality forecasting, Data analytics, Machine learning

General

Ranking parameters driving siring success during sperm competition in the North African houbara bustard.

In Communications biology

Sperm competition is a powerful force driving the evolution of ejaculate and sperm traits. However, the outcome of sperm competition depends on many traits that extend beyond ejaculate quality. Here, we study male North African houbara bustards (Chlamydotis undulata undulata) competing for egg fertilization, after artificial insemination, with the aim to rank the importance of 14 parameters as drivers of siring success. Using a machine learning approach, we show that traits independent of male quality (i.e., insemination order, delay between insemination and egg laying) are the most important predictors of siring success. Traits describing intrinsic male quality (i.e., number of sperm in the ejaculate, mass motility index) are also positively associated with siring success, but their contribution to explaining the outcome of sperm competition is much lower than for insemination order. Overall, this analysis shows that males mating at the last position in the mating sequence have the best chance to win the competition for egg fertilization. This raises the question of the importance of female behavior as determinant of mating order.

Sorci Gabriele, Hussein Hiba Abi, Levêque Gwènaëlle, Saint Jalme Michel, Lacroix Frédéric, Hingrat Yves, Lesobre Loïc

2023-Mar-22

General

Week 168

General

Establishing a prediction model of severe acute mountain sickness using machine learning of support vector machine recursive feature elimination.

In Scientific reports ; h5-index 158.0

Severe acute mountain sickness (sAMS) can be life-threatening, but little is known about its genetic basis. The study was aimed to explore the genetic susceptibility of sAMS for the purpose of prediction, using microarray data from 112 peripheral blood mononuclear cell (PBMC) samples of 21 subjects, who were exposed to very high altitude (5260 m), low barometric pressure (406 mmHg), and hypobaric hypoxia (VLH) at various timepoints. We found that exposure to VLH activated gene expression in leukocytes, resulting in an inverted CD4/CD8 ratio that interacted with other phenotypic risk factors at the genetic level. A total of 2286 underlying risk genes were input into the support vector machine recursive feature elimination (SVM-RFE) system for machine learning, and a model with satisfactory predictive accuracy and clinical applicability was established for sAMS screening using ten featured genes with significant predictive power. Five featured genes (EPHB3, DIP2B, RHEBL1, GALNT13, and SLC8A2) were identified upstream of hypoxia- and/or inflammation-related pathways mediated by microRNAs as potential biomarkers for sAMS. The established prediction model of sAMS holds promise for clinical application as a genetic screening tool for sAMS.

Yang Min, Wu Yang, Yang Xing-Biao, Liu Tao, Zhang Ya, Zhuo Yue, Luo Yong, Zhang Nan

2023-Mar-21

Pathology

SC-MIL: Supervised Contrastive Multiple Instance Learning for Imbalanced Classification in Pathology

ArXiv Preprint

Multiple Instance learning (MIL) models have been extensively used in pathology to predict biomarkers and risk-stratify patients from gigapixel-sized images. Machine learning problems in medical imaging often deal with rare diseases, making it important for these models to work in a label-imbalanced setting. Furthermore, these imbalances can occur in out-of-distribution (OOD) datasets when the models are deployed in the real-world. We leverage the idea that decoupling feature and classifier learning can lead to improved decision boundaries for label imbalanced datasets. To this end, we investigate the integration of supervised contrastive learning with multiple instance learning (SC-MIL). Specifically, we propose a joint-training MIL framework in the presence of label imbalance that progressively transitions from learning bag-level representations to optimal classifier learning. We perform experiments with different imbalance settings for two well-studied problems in cancer pathology: subtyping of non-small cell lung cancer and subtyping of renal cell carcinoma. SC-MIL provides large and consistent improvements over other techniques on both in-distribution (ID) and OOD held-out sets across multiple imbalanced settings.

Dinkar Juyal, Siddhant Shingi, Syed Ashar Javed, Harshith Padigela, Chintan Shah, Anand Sampat, Archit Khosla, John Abel, Amaro Taylor-Weiner

2023-03-23

General

Deep learning for pancreatic diseases based on endoscopic ultrasound: A systematic review.

In International journal of medical informatics ; h5-index 49.0

BACKGROUND AND AIMS : Endoscopic ultrasonography (EUS) is one of the main examinations in pancreatic diseases. A series of the studies reported the application of deep learning (DL)-assisted EUS in the diagnosis of pancreatic diseases. This systematic review is to evaluate the role of DL algorithms in assisting EUS diagnosis of pancreatic diseases.

METHODS : Literature search were conducted in PubMed and Semantic Scholar databases. Studies that developed DL models for pancreatic diseases based on EUS were eligible for inclusion. This review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and quality assessment of the included studies was performed according to the IJMEDI checklist.

RESULTS : A total of 23 studies were enrolled into this systematic review, which could be categorized into three groups according to computer vision tasks: classification, detection and segmentation. Seventeen studies focused on the classification task, among which five studies developed simple neural network (NN) models while twelve studies constructed convolutional NN (CNN) models. Three studies were concerned the detection task and five studies were the segmentation task, all based on CNN architectures. All models presented in the studies performed well based on EUS images, videos or voice. According to the IJMEDI checklist, six studies were recognized as high-grade quality, with scores beyond 35 points.

CONCLUSIONS : DL algorithms show great potential in EUS images/videos/voice for pancreatic diseases. However, there is room for improvement such as sample sizes, multi-center cooperation, data preprocessing, model interpretability, and code sharing.

Yin Minyue, Liu Lu, Gao Jingwen, Lin Jiaxi, Qu Shuting, Xu Wei, Liu Xiaolin, Xu Chunfang, Zhu Jinzhou

2023-Mar-18

Convolutional neural networks, Deep learning, Endoscopic ultrasonography, Pancreatic diseases, Systematic review

Dermatology

HGM-cNet: Integrating hippocampal gray matter probability map into a cascaded deep learning framework improves hippocampus segmentation.

In European journal of radiology ; h5-index 47.0

A robust cascaded deep learning framework with integrated hippocampal gray matter (HGM) probability map was developed to improve the hippocampus segmentation (called HGM-cNet) due to its significance in various neuropsychiatric disorders such as Alzheimer's disease (AD). Particularly, the HGM-cNet cascaded two identical convolutional neural networks (CNN), where each CNN was devised by incorporating Attention Block, Residual Block, and DropBlock into the typical encoder-decoder architecture. The two CNNs were skip-connected between encoder components at each scale. The adoption of the cascaded deep learning framework was to conveniently incorporate the HGM probability map with the feature map generated by the first CNN. Experiments on 135T1-weighted MRI scans and manual hippocampal labels from publicly available ADNI-HarP dataset demonstrated that the proposed HGM-cNet outperformed seven multi-atlas-based hippocampus segmentation methods and six deep learning methods under comparison in most evaluation metrics. The Dice (average > 0.89 for both left and right hippocampus) was increased by around or more than 1% over other methods. The HGM-cNet also achieved a superior hippocampus segmentation performance in each group of cognitive normal, mild cognitive impairment, and AD. The stability, conveniences and generalizability of the cascaded deep learning framework with integrated HGM probability map in improving hippocampus segmentation was validated by replacing the proposed CNN with 3D-UNet, Atten-UNet, HippoDeep, QuickNet, DeepHarp, and TransBTS models. The integration of the HGM probability map in the cascaded deep learning framework was also demonstrated to facilitate capturing hippocampal atrophy more accurately than alternative methods in AD analysis. The codes are publicly available at https://github.com/Liu1436510768/HGM-cNet.git.

Zheng Qiang, Liu Bin, Gao Yan, Bai Lijun, Cheng Yu, Li Honglun

2023-Mar-15

Deep learning, Gray matter volume, Hippocampus, Image segmentation, Multi-atlas segmentation

Public Health

Application of a developed triple-classification machine learning model for carcinogenic prediction of hazardous organic chemicals to the US, EU, and WHO based on Chinese database.

In Ecotoxicology and environmental safety ; h5-index 67.0

Cancer, the second largest human disease, has become a major public health problem. The prediction of chemicals' carcinogenicity before their synthesis is crucial. In this paper, seven machine learning algorithms (i.e., Random Forest (RF), Logistic Regression (LR), Support Vector Machines (SVM), Complement Naive Bayes (CNB), K-Nearest Neighbor (KNN), XGBoost, and Multilayer Perceptron (MLP)) were used to construct the carcinogenicity triple classification prediction (TCP) model (i.e., 1A, 1B, Category 2). A total of 1444 descriptors of 118 hazardous organic chemicals were calculated by Discovery Studio 2020, Sybyl X-2.0 and PaDEL-Descriptor software. The constructed carcinogenicity TCP model was evaluated through five model evaluation indicators (i.e., Accuracy, Precision, Recall, F1 Score and AUC). The model evaluation results show that Accuracy, Precision, Recall, F1 Score and AUC evaluation indicators meet requirements (greater than 0.6). The accuracy of RF, LR, XGBoost, and MLP models for predicting carcinogenicity of Category 2 is 91.67%, 79.17%, 100%, and 100%, respectively. In addition, the constructed machine learning model in this study has potential for error correction. Taking XGBoost model as an example, the predicted carcinogenicity level of 1,2,3-Trichloropropane (96-18-4) is Category 2, but the actual carcinogenicity level is 1B. But the difference between Category 2 and 1B is only 0.004, indicating that the XGBoost is one optimum model of the seven constructed machine learning models. Besides, results showed that functional groups like chlorine and benzene ring might influence the prediction of carcinogenic classification. Therefore, considering functional group characteristics of chemicals before constructing the carcinogenicity prediction model of organic chemicals is recommended. The predicted carcinogenicity of the organic chemicals using the optimum machine leaning model (i.e., XGBoost) was also evaluated and verified by the toxicokinetics. The RF and XGBoost TCP models constructed in this paper can be used for carcinogenicity detection before synthesizing new organic substances. It also provides technical support for the subsequent management of organic chemicals.

Hao Ning, Sun Peixuan, Zhao Wenjin, Li Xixi

2023-Mar-20

Carcinogenic chemicals, Carcinogenicity classification prediction model, Machine learning, Model evaluation metrics, Molecular structure, Toxicokinetics

General

Development of prediction models for one-year brain tumour survival using machine learning: a comparison of accuracy and interpretability.

In Computer methods and programs in biomedicine

BACKGROUND AND OBJECTIVE : Prediction of survival in patients diagnosed with a brain tumour is challenging because of heterogeneous tumour behaviours and treatment response. Advances in machine learning have led to the development of clinical prognostic models, but due to the lack of model interpretability, integration into clinical practice is almost non-existent. In this retrospective study, we compare five classification models with varying degrees of interpretability for the prediction of brain tumour survival greater than one year following diagnosis.

METHODS : 1028 patients aged ≥16 years with a brain tumour diagnosis between April 2012 and April 2020 were included in our study. Three intrinsically interpretable 'glass box' classifiers (Bayesian Rule Lists [BRL], Explainable Boosting Machine [EBM], and Logistic Regression [LR]), and two 'black box' classifiers (Random Forest [RF] and Support Vector Machine [SVM]) were trained on electronic patients records for the prediction of one-year survival. All models were evaluated using balanced accuracy (BAC), F1-score, sensitivity, specificity, and receiver operating characteristics. Black box model interpretability and misclassified predictions were quantified using SHapley Additive exPlanations (SHAP) values and model feature importance was evaluated by clinical experts.

RESULTS : The RF model achieved the highest BAC of 78.9%, closely followed by SVM (77.7%), LR (77.5%) and EBM (77.1%). Across all models, age, diagnosis (tumour type), functional features, and first treatment were top contributors to the prediction of one year survival. We used EBM and SHAP to explain model misclassifications and investigated the role of feature interactions in prognosis.

CONCLUSION : Interpretable models are a natural choice for the domain of predictive medicine. Intrinsically interpretable models, such as EBMs, may provide an advantage over traditional clinical assessment of brain tumour prognosis by weighting potential risk factors and their interactions that may be unknown to clinicians. An agreement between model predictions and clinical knowledge is essential for establishing trust in the models decision making process, as well as trust that the model will make accurate predictions when applied to new data.

Charlton Colleen E, Poon Michael T C, Brennan Paul M, Fleuriot Jacques D

2023-Mar-13

Bayesian rule lists, Brain cancer, Explainable boosting machine, Interpretable models, Machine learning, Survival

General

Forecasting and Optimizing Dual Media Filter Performance via Machine Learning.

In Water research

Four different machine learning algorithms, including Decision Tree (DT), Random Forest (RF), Multivariable Linear Regression (MLR), Support Vector Regressions (SVR), and Gaussian Process Regressions (GPR), were applied to predict the performance of a multi-media filter operating as a function of raw water quality and plant operating variables. The models were trained using data collected over a seven year period covering water quality and operating variables, including true colour, turbidity, plant flow, and chemical dose for chlorine, KMnO₄, FeCl₃, and Cationic Polymer (PolyDADMAC). The machine learning algorithms have shown that the best prediction is at a 1-day time lag between input variables and unit filter run volume (UFRV). Furthermore, the RF algorithm with grid search using the input metrics mentioned above with a 1-day time lag has provided the highest reliability in predicting UFRV with a RMSE and R² of 31.58 and 0.98, respectively. Similarly, RF with grid search has shown the shortest training time, prediction accuracy, and forecasting events using a ROC-AUC curve analysis (AUC over 0.8) in extreme wet weather events. Therefore, Random Forest with grid search and a 1-day time lag is an effective and robust machine learning algorithm that can predict the filter performance to aid water treatment operators in their decision makings by providing real-time warning of the potential turbidity breakthrough from the filters.

Moradi Sina, Omar Amr, Zhou Zhuoyu, Agostino Anthony, Gandomkar Ziba, Bustamante Heriberto, Power Kaye, Henderson Rita, Leslie Greg

2023-Mar-12

Filtration performance, Hyper-parameter optimisation, Machine learning approach, Unit filter run volume

General

Prediction of brain tumor recurrence location based on multi-modal fusion and nonlinear correlation learning.

In Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society

Brain tumor is one of the leading causes of cancer death. The high-grade brain tumors are easier to recurrent even after standard treatment. Therefore, developing a method to predict brain tumor recurrence location plays an important role in the treatment planning and it can potentially prolong patient's survival time. There is still little work to deal with this issue. In this paper, we present a deep learning-based brain tumor recurrence location prediction network. Since the dataset is usually small, we propose to use transfer learning to improve the prediction. We first train a multi-modal brain tumor segmentation network on the public dataset BraTS 2021. Then, the pre-trained encoder is transferred to our private dataset for extracting the rich semantic features. Following that, a multi-scale multi-channel feature fusion model and a nonlinear correlation learning module are developed to learn the effective features. The correlation between multi-channel features is modeled by a nonlinear equation. To measure the similarity between the distributions of original features of one modality and the estimated correlated features of another modality, we propose to use Kullback-Leibler divergence. Based on this divergence, a correlation loss function is designed to maximize the similarity between the two feature distributions. Finally, two decoders are constructed to jointly segment the present brain tumor and predict its future tumor recurrence location. To the best of our knowledge, this is the first work that can segment the present tumor and at the same time predict future tumor recurrence location, making the treatment planning more efficient and precise. The experimental results demonstrated the effectiveness of our proposed method to predict the brain tumor recurrence location from the limited dataset.

Zhou Tongxue, Noeuveglise Alexandra, Modzelewski Romain, Ghazouani Fethi, Thureau Sébastien, Fontanilles Maxime, Ruan Su

2023-Mar-16

Brain tumor recurrence, Correlation learning, Deep learning, Location prediction, Multi-modal fusion

Radiology

BTMF-GAN: A multi-modal MRI fusion generative adversarial network for brain tumors.

In Computers in biology and medicine

Image fusion techniques have been widely used for multi-modal medical image fusion tasks. Most existing methods aim to improve the overall quality of the fused image and do not focus on the more important textural details and contrast between the tissues of the lesion in the regions of interest (ROIs). This can lead to the distortion of important tumor ROIs information and thus limits the applicability of the fused images in clinical practice. To improve the fusion quality of ROIs relevant to medical implications, we propose a multi-modal MRI fusion generative adversarial network (BTMF-GAN) for the task of multi-modal MRI fusion of brain tumors. Unlike existing deep learning approaches which focus on improving the global quality of the fused image, the proposed BTMF-GAN aims to achieve a balance between tissue details and structural contrasts in brain tumor, which is the region of interest crucial to many medical applications. Specifically, we employ a generator with a U-shaped nested structure and residual U-blocks (RSU) to enhance multi-scale feature extraction. To enhance and recalibrate features of the encoder, the multi-perceptual field adaptive transformer feature enhancement module (MRF-ATFE) is used between the encoder and the decoder instead of a skip connection. To increase contrast between tumor tissues of the fused image, a mask-part block is introduced to fragment the source image and the fused image, based on which, we propose a novel salient loss function. Qualitative and quantitative analysis of the results on the public and clinical datasets demonstrate the superiority of the proposed approach to many other commonly used fusion methods.

Liu Xiao, Chen Hongyi, Yao Chong, Xiang Rui, Zhou Kun, Du Peng, Liu Weifan, Liu Jie, Yu Zekuan

2023-Mar-09

Adaptive transformer, Image fusion, Multi-modal MRI, Salient loss

Public Health

Intelligent decision support in medical triage: are people robust to biased advice?

In Journal of public health (Oxford, England)

BACKGROUND : Intelligent artificial agents ('agents') have emerged in various domains of human society (healthcare, legal, social). Since using intelligent agents can lead to biases, a common proposed solution is to keep the human in the loop. Will this be enough to ensure unbiased decision making?

METHODS : To address this question, an experimental testbed was developed in which a human participant and an agent collaboratively conduct triage on patients during a pandemic crisis. The agent uses data to support the human by providing advice and extra information about the patients. In one condition, the agent provided sound advice; the agent in the other condition gave biased advice. The research question was whether participants neutralized bias from the biased artificial agent.

RESULTS : Although it was an exploratory study, the data suggest that human participants may not be sufficiently in control to correct the agent's bias.

CONCLUSIONS : This research shows how important it is to design and test for human control in concrete human-machine collaboration contexts. It suggests that insufficient human control can potentially result in people being unable to detect biases in machines and thus unable to prevent machine biases from affecting decisions.

van der Stigchel Birgit, van den Bosch Karel, van Diggelen Jurriaan, Haselager Pim

2023-Mar-20

emergency care, ethics, health intelligence

Surgery

The Role of Scribes in Orthopaedics.

In JBJS reviews

» : The rapid increase in the use of electronic medical records (EMRs) has led to some unintended consequences that negatively affect physicians and their patients.

» : The use of medical scribes may serve as a possible solution to some of the EMR-related concerns.

» : Research has demonstrated an overall positive impact of having scribes on both physician and patient well-being, safety, and satisfaction.

» : Adaptation of advances in technology, including remote and asynchronous scribing, use of face-mounted devices, voice recognition software, and applications of artificial intelligence may address some of the barriers to more traditional in-person scribes.

Lam Michelle, Sabharwal Sanjeev

2023-Mar-01

General

Mechanism of assembly of type 4 filaments: everything you always wanted to know (but were afraid to ask).

In Microbiology (Reading, England)

Type 4 filaments (T4F) are a superfamily of filamentous nanomachines - virtually ubiquitous in prokaryotes and functionally versatile - of which type 4 pili (T4P) are the defining member. T4F are polymers of type 4 pilins, assembled by conserved multi-protein machineries. They have long been an important topic for research because they are key virulence factors in numerous bacterial pathogens. Our poor understanding of the molecular mechanisms of T4F assembly is a serious hindrance to the design of anti-T4F therapeutics. This review attempts to shed light on the fundamental mechanistic principles at play in T4F assembly by focusing on similarities rather than differences between several (mostly bacterial) T4F. This holistic approach, complemented by the revolutionary ability of artificial intelligence to predict protein structures, led to an intriguing mechanistic model of T4F assembly.

Pelicic Vladimir

2023-Mar

nanomachines, pili, pilin, type 2 secretion systems, type 4 filaments, type 4 pili

Internal Medicine

Predicting outcomes of continuous renal replacement therapy using body composition monitoring: a deep-learning approach.

In Scientific reports ; h5-index 158.0

Fluid balance is a critical prognostic factor for patients with severe acute kidney injury (AKI) requiring continuous renal replacement therapy (CRRT). This study evaluated whether repeated fluid balance monitoring could improve prognosis in this clinical population. This was a multicenter retrospective study that included 784 patients (mean age, 67.8 years; males, 66.4%) with severe AKI requiring CRRT during 2017-2019 who were treated in eight tertiary hospitals in Korea. Sequential changes in total body water were compared between patients who died (event group) and those who survived (control group) using mixed-effects linear regression analyses. The performance of various machine learning methods, including recurrent neural networks, was compared to that of existing prognostic clinical scores. After adjusting for confounding factors, a marginal benefit of fluid balance was identified for the control group compared to that for the event group (p = 0.074). The deep-learning model using a recurrent neural network with an autoencoder and including fluid balance monitoring provided the best differentiation between the groups (area under the curve, 0.793) compared to 0.604 and 0.606 for SOFA and APACHE II scores, respectively. Our prognostic, deep-learning model underlines the importance of fluid balance monitoring for prognosis assessment among patients receiving CRRT.

Yoo Kyung Don, Noh Junhyug, Bae Wonho, An Jung Nam, Oh Hyung Jung, Rhee Harin, Seong Eun Young, Baek Seon Ha, Ahn Shin Young, Cho Jang-Hee, Kim Dong Ki, Ryu Dong-Ryeol, Kim Sejoong, Lim Chun Soo, Lee Jung Pyo

2023-Mar-21

Pathology

Clinically Relevant Latent Space Embedding of Cancer Histopathology Slides through Variational Autoencoder Based Image Compression

ArXiv Preprint

In this paper, we introduce a Variational Autoencoder (VAE) based training approach that can compress and decompress cancer pathology slides at a compression ratio of 1:512, which is better than the previously reported state of the art (SOTA) in the literature, while still maintaining accuracy in clinical validation tasks. The compression approach was tested on more common computer vision datasets such as CIFAR10, and we explore which image characteristics enable this compression ratio on cancer imaging data but not generic images. We generate and visualize embeddings from the compressed latent space and demonstrate how they are useful for clinical interpretation of data, and how in the future such latent embeddings can be used to accelerate search of clinical imaging data.

Mohammad Sadegh Nasr, Amir Hajighasemi, Paul Koomey, Parisa Boodaghi Malidarreh, Michael Robben, Jillur Rahman Saurav, Helen H. Shang, Manfred Huber, Jacob M. Luber

2023-03-23

General

SARS-CoV-2 related adaptation mechanisms of rehabilitation clinics affecting patient-centred care: Qualitative study of online patient reports.

In JMIR rehabilitation and assistive technologies

BACKGROUND : The SARS-CoV-2 pandemic impacted the access to inpatient rehabilitation services. At the current state of research, it is unclear to what extent the adaptation of rehabilitation services to infection-protective standards affected patient-centred care in Germany.

OBJECTIVE : This study aimed to explore which aspects of patient-centred care were relevant for patients in inpatient rehabilitation clinics under early-phase pandemic conditions.

METHODS : A deductive-inductive framework analysis of online patient reports posted on a leading German hospital rating website was conducted (www.klinikbewertungen.de). The selected hospital rating website is a third party, patient-centred commercial platform which operates independently of governmental entities. Following a theoretical sampling approach, online reports of rehabilitation stays in two federal states of Germany (Brandenburg, Saarland) uploaded between March 2020 and September 2021 were included. Independently of medical specialty groups, all reports were included. Keywords addressing framework domains were analysed descriptively.

RESULTS : In total, 649 online reports reflecting inpatient rehabilitation services of 31 clinics (Brandenburg N = 23; Saarland N = 8) were analysed. Keywords addressing the care environment were most frequently reported (59.9%) followed by staff prerequisites (33.0%), patient-centred processes (4.5%) and expected outcomes (2.6%). Qualitative in depth-analysis revealed SARS-CoV-2 related reports to be associated with domains of patient-centred processes and staff prerequisites. Discontinuous communication of infection protection standards was perceived to threaten patient autonomy. This was amplified by a tangible gratification crisis of medical staff. Established and emotional supportive relationships to clinicians and peer-groups offered the potential to mitigate adverse effects of infection protection standards.

CONCLUSIONS : Patients predominantly reported feedback associated with the care environment. SARS-CoV-2 related reports were strongly affected by increased staff workloads as well as patient-centred processes addressing discontinuous communication and organizationally demanding implementation of infection protection standards which were perceived to threaten patient autonomy. Peer-relationships formed during inpatient rehabilitation had the potential to mitigate these mechanisms.

CLINICALTRIAL : Not applicable.

Kühn Lukas, Lindert Lara, Kuper Paulina, Choi Kyung-Eun Anna

2023-Mar-05

Public Health

Predictors of Cyberchondria during the COVID-19 pandemic: A cross-sectional study using supervised machine learning.

In JMIR formative research

BACKGROUND : Cyberchondria is characterized by repeated and compulsive online searches for health information, resulting in increased health anxiety and distress. It has been conceptualized as a multi-dimensional construct fueled by both anxiety and compulsivity-related factors and described as a "transdiagnostic compulsive behavioral syndrome" which is associated with health anxiety, problematic internet use and obsessive-compulsive symptoms. Cyberchondria is not included in the ICD-11 or the DSM-5, and its defining features, etiological mechanisms and assessment continue to be debated.

OBJECTIVE : This study aimed to investigate changes in the severity of cyberchondria during the pandemic and identify predictors of cyberchondria at this time.

METHODS : Data collection started on May 4, 2020 and ended on June 10, 2020, which corresponds to the first wave of the COVID-19 pandemic in Europe. At the time the present study took place, French-speaking countries in Europe (France, Switzerland, Belgium and Luxembourg) all implemented lockdown or semi-lockdown measures. The survey consisted of a questionnaire collecting demographic information (sex, age, education level and country of residence) and information on socioeconomic circumstances during the first lockdown (e.g., economic situation, housing and employment status), and was followed by several instruments assessing various psychological and health-related constructs. Inclusion criteria for the study were being at least 18 years of age and having a good understanding of French. Self-report data were collected from 725 participants aged 18 to 77 years (mean 33.29, SD 12.88 years), with females constituting the majority (416/725, 57.4%).

RESULTS : The results show that the COVID-19 pandemic affected various facets of cyberchondria: cyberchondria-related distress and interference with functioning increased (distress z=-3.651, P<.001; compulsion z=-5.697, P<.001), whereas the reassurance facet of cyberchondria decreased (z=-6.680, P<.001). Also, COVID-19-related fears and health anxiety emerged as the strongest predictors of cyberchondria-related distress and interference with functioning during the pandemic.

CONCLUSIONS : These findings provide evidence about the impact of the COVID-19 pandemic on cyberchondria and identify factors that should be considered in efforts to prevent and manage cyberchondria at times of public health crises. Also, they are consistent with the theoretical model of cyberchondria during the COVID-19 pandemic proposed by Starcevic and his colleagues in 2020. In addition, the findings have implications for the conceptualization and future assessment of cyberchondria.

Infanti Alexandre, Starcevic Vladan, Schimmenti Adriano, Khazaal Yasser, Karila Laurent, Giardina Alessandro, Flayelle Maèva, Hedayatzadeh Razavi Seyedeh Boshra, Baggio Stéphanie, Vögele Claus, Billieux Joël

2023-Mar-09

General

The hair cell analysis toolbox is a precise and fully automated pipeline for whole cochlea hair cell quantification.

In PLoS biology

Our sense of hearing is mediated by sensory hair cells, precisely arranged and highly specialized cells subdivided into outer hair cells (OHCs) and inner hair cells (IHCs). Light microscopy tools allow for imaging of auditory hair cells along the full length of the cochlea, often yielding more data than feasible to manually analyze. Currently, there are no widely applicable tools for fast, unsupervised, unbiased, and comprehensive image analysis of auditory hair cells that work well either with imaging datasets containing an entire cochlea or smaller sampled regions. Here, we present a highly accurate machine learning-based hair cell analysis toolbox (HCAT) for the comprehensive analysis of whole cochleae (or smaller regions of interest) across light microscopy imaging modalities and species. The HCAT is a software that automates common image analysis tasks such as counting hair cells, classifying them by subtype (IHCs versus OHCs), determining their best frequency based on their location along the cochlea, and generating cochleograms. These automated tools remove a considerable barrier in cochlear image analysis, allowing for faster, unbiased, and more comprehensive data analysis practices. Furthermore, HCAT can serve as a template for deep learning-based detection tasks in other types of biological tissue: With some training data, HCAT's core codebase can be trained to develop a custom deep learning detection model for any object on an image.

Buswinka Christopher J, Osgood Richard T, Simikyan Rubina G, Rosenberg David B, Indzhykulian Artur A

2023-Mar-22

General

Characterization of RNA polymerase II trigger loop mutations using molecular dynamics simulations and machine learning.

In PLoS computational biology

Catalysis and fidelity of multisubunit RNA polymerases rely on a highly conserved active site domain called the trigger loop (TL), which achieves roles in transcription through conformational changes and interaction with NTP substrates. The mutations of TL residues cause distinct effects on catalysis including hypo- and hyperactivity and altered fidelity. We applied molecular dynamics simulation (MD) and machine learning (ML) techniques to characterize TL mutations in the Saccharomyces cerevisiae RNA Polymerase II (Pol II) system. We did so to determine relationships between individual mutations and phenotypes and to associate phenotypes with MD simulated structural alterations. Using fitness values of mutants under various stress conditions, we modeled phenotypes along a spectrum of continual values. We found that ML could predict the phenotypes with 0.68 R2 correlation from amino acid sequences alone. It was more difficult to incorporate MD data to improve predictions from machine learning, presumably because MD data is too noisy and possibly incomplete to directly infer functional phenotypes. However, a variational auto-encoder model based on the MD data allowed the clustering of mutants with different phenotypes based on structural details. Overall, we found that a subset of loss-of-function (LOF) and lethal mutations tended to increase distances of TL residues to the NTP substrate, while another subset of LOF and lethal substitutions tended to confer an increase in distances between TL and bridge helix (BH). In contrast, some of the gain-of-function (GOF) mutants appear to cause disruption of hydrophobic contacts among TL and nearby helices.

Dutagaci Bercem, Duan Bingbing, Qiu Chenxi, Kaplan Craig D, Feig Michael

2023-Mar-22

General

Classification of truck-involved crash severity: Dealing with missing, imbalanced, and high dimensional safety data.

In PloS one ; h5-index 176.0

While the cost of road traffic fatalities in the U.S. surpasses $240 billion a year, the availability of high-resolution datasets allows meticulous investigation of the contributing factors to crash severity. In this paper, the dataset for Trucks Involved in Fatal Accidents in 2010 (TIFA 2010) is utilized to classify the truck-involved crash severity where there exist different issues including missing values, imbalanced classes, and high dimensionality. First, a decision tree-based algorithm, the Synthetic Minority Oversampling Technique (SMOTE), and the Random Forest (RF) feature importance approach are employed for missing value imputation, minority class oversampling, and dimensionality reduction, respectively. Afterward, a variety of classification algorithms, including RF, K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), Gradient-Boosted Decision Trees (GBDT), and Support Vector Machine (SVM) are developed to reveal the influence of the introduced data preprocessing framework on the output quality of ML classifiers. The results show that the GBDT model outperforms all the other competing algorithms for the non-preprocessed crash data based on the G-mean performance measure, but the RF makes the most accurate prediction for the treated dataset. This finding indicates that after the feature selection is conducted to alleviate the computational cost of the machine learning algorithms, bagging (bootstrap aggregating) of decision trees in RF leads to a better model rather than boosting them via GBDT. Besides, the adopted feature importance approach decreases the overall accuracy by only up to 5% in most of the estimated models. Moreover, the worst class recall value of the RF algorithm without prior oversampling is only 34.4% compared to the corresponding value of 90.3% in the up-sampled model which validates the proposed multi-step preprocessing scheme. This study also identifies the temporal and spatial (roadway) attributes, as well as crash characteristics, and Emergency Medical Service (EMS) as the most critical factors in truck crash severity.

Mohammadpour Seyed Iman, Khedmati Majid, Zada Mohammad Javad Hassan

2023

General

Triadic influence as a proxy for compatibility in social relationships.

In Proceedings of the National Academy of Sciences of the United States of America

Networks of social interactions are the substrate upon which civilizations are built. Often, we create new bonds with people that we like or feel that our relationships are damaged through the intervention of third parties. Despite their importance and the huge impact that these processes have in our lives, quantitative scientific understanding of them is still in its infancy, mainly due to the difficulty of collecting large datasets of social networks including individual attributes. In this work, we present a thorough study of real social networks of 13 schools, with more than 3,000 students and 60,000 declared positive and negative relationships, including tests for personal traits of all the students. We introduce a metric-the "triadic influence"-that measures the influence of nearest neighbors in the relationships of their contacts. We use neural networks to predict the sign of the relationships in these social networks, extracting the probability that two students are friends or enemies depending on their personal attributes or the triadic influence. We alternatively use a high-dimensional embedding of the network structure to also predict the relationships. Remarkably, using the triadic influence (a simple one-dimensional metric) achieves the best accuracy, and adding the personal traits of the students does not improve the results, suggesting that the triadic influence acts as a proxy for the social compatibility of students. We postulate that the probabilities extracted from the neural networks-functions of the triadic influence and the personalities of the students-control the evolution of real social networks, opening an avenue for the quantitative study of these systems.

Ruiz-García Miguel, Ozaita Juan, Pereda María, Alfonso Antonio, Brañas-Garza Pablo, Cuesta José A, Sánchez Angel

2023-Mar-28

machine learning, relationship prediction, social networks, triadic influence

General

Extreme Gradient Boosting to Predict Atomic Layer Deposition for Platinum Nano-Film Coating.

In Langmuir : the ACS journal of surfaces and colloids

Extreme gradient boosting (XGBoost) is an artificial intelligence algorithm capable of high accuracy and low inference time. The current study applies this XGBoost to the production of platinum nano-film coating through atomic layer deposition (ALD). In order to generate a database for model development, platinum is coated on α-Al2O3 using a rotary-type ALD equipment. The process is controlled by four parameters: process temperature, stop valve time, precursor pulse time, and reactant pulse time. A total of 625 samples according to different process conditions are obtained. The ALD coating index is used as the Al/Pt component ratio through ICP-AES analysis during postprocessing. The four process parameters serve as the input data and produces the Al/Pt component ratio as the output data. The postprocessed data set is randomly divided into 500 training samples and 125 test samples. XGBoost demonstrates 99.9% accuracy and a coefficient of determination of 0.99. The inference time is lower than that of random forest regression, in addition to a higher prediction safety than that of the light gradient boosting machine.

Yoon Sung-Ho, Jeon Jun-Hyeok, Cho Seung-Beom, Nacpil Edric John Cruz, Jeon Il, Choi Jae-Boong, Kim Hyeongkeun

2023-Mar-22

Radiology

Virtual clinical trial based on outcome modeling with iteratively redistributed extrapolation data.

In Radiological physics and technology

Virtual clinical trials (VCTs) can potentially simulate clinical trials on a computer, but their application with a limited number of past clinical cases is challenging due to the biased estimation of the statistical population. In this study, we developed ExMixup, a novel training technique based on machine learning, using iteratively redistributed extrapolated data. Information obtained from 100 patients with prostate cancer and 385 patients with oropharyngeal cancer was used to predict the recurrence after radiotherapy. Model performance was evaluated by developing outcome prediction models based on three types of training methods: training with original data (baseline), interpolation data (Mixup), and interpolation + extrapolation data (ExMixup). Two types of VCTs were conducted to predict the treatment response of patients with distinct characteristics compared to the training data obtained from patient cohorts categorized under risk classification or cancer stage. The prediction models developed with ExMixup yielded concordance indices (95% confidence intervals) of 0.751 (0.719-0.818) and 0.752 (0.734-0.785) for VCTs on the prostate and oropharyngeal cancer datasets, respectively, which significantly outperformed the baseline and Mixup models (P < 0.01). The proposed approach could enhance the ability of VCTs to predict treatment results in patients excluded from past clinical trials.

Oguma Kohei, Magome Taiki, Someya Masanori, Hasegawa Tomokazu, Sakata Koh-Ichi

2023-Mar-22

Extrapolation data, Outcome modeling, Outcome prediction, Radiotherapy, Virtual clinical trial

Oncology

QuantImage v2: a comprehensive and integrated physician-centered cloud platform for radiomics and machine learning research.

In European radiology experimental

BACKGROUND : Radiomics, the field of image-based computational medical biomarker research, has experienced rapid growth over the past decade due to its potential to revolutionize the development of personalized decision support models. However, despite its research momentum and important advances toward methodological standardization, the translation of radiomics prediction models into clinical practice only progresses slowly. The lack of physicians leading the development of radiomics models and insufficient integration of radiomics tools in the clinical workflow contributes to this slow uptake.

METHODS : We propose a physician-centered vision of radiomics research and derive minimal functional requirements for radiomics research software to support this vision. Free-to-access radiomics tools and frameworks were reviewed to identify best practices and reveal the shortcomings of existing software solutions to optimally support physician-driven radiomics research in a clinical environment.

RESULTS : Support for user-friendly development and evaluation of radiomics prediction models via machine learning was found to be missing in most tools. QuantImage v2 (QI2) was designed and implemented to address these shortcomings. QI2 relies on well-established existing tools and open-source libraries to realize and concretely demonstrate the potential of a one-stop tool for physician-driven radiomics research. It provides web-based access to cohort management, feature extraction, and visualization and supports "no-code" development and evaluation of machine learning models against patient-specific outcome data.

CONCLUSIONS : QI2 fills a gap in the radiomics software landscape by enabling "no-code" radiomics research, including model validation, in a clinical environment. Further information about QI2, a public instance of the system, and its source code is available at https://medgift.github.io/quantimage-v2-info/ . Key points As domain experts, physicians play a key role in the development of radiomics models. Existing software solutions do not support physician-driven research optimally. QuantImage v2 implements a physician-centered vision for radiomics research. QuantImage v2 is a web-based, "no-code" radiomics research platform.

Abler Daniel, Schaer Roger, Oreiller Valentin, Verma Himanshu, Reichenbach Julien, Aidonopoulos Orfeas, Evéquoz Florian, Jreige Mario, Prior John O, Depeursinge Adrien

2023-Mar-22

Artificial intelligence, Biomarkers, Cloud computing, Decision support techniques, Radiomics

Radiology

Applications of deep learning to reduce the need for iodinated contrast media for CT imaging: a systematic review.

In International journal of computer assisted radiology and surgery

PURPOSE : The usage of iodinated contrast media (ICM) can improve the sensitivity and specificity of computed tomography (CT) for many clinical indications. However, the adverse effects of ICM administration can include renal injury, life-threatening allergic-like reactions, and environmental contamination. Deep learning (DL) models can generate full-dose ICM CT images from non-contrast or low-dose ICM administration or generate non-contrast CT from full-dose ICM CT. Eliminating the need for both contrast-enhanced and non-enhanced imaging or reducing the amount of required contrast while maintaining diagnostic capability may reduce overall patient risk, improve efficiency and minimize costs. We reviewed the current capabilities of DL to reduce the need for contrast administration in CT.

METHODS : We conducted a systematic review of articles utilizing DL to reduce the amount of ICM required in CT, searching MEDLINE, Embase, Compendex, Inspec, and Scopus to identify papers published from 2016 to 2022. We classified the articles based on the DL model and ICM reduction.

RESULTS : Eighteen papers met the inclusion criteria for analysis. Of these, ten generated synthetic full-dose (100%) ICM from real non-contrast CT, while four augmented low-dose to full-dose ICM CT. Three used DL to create synthetic non-contrast CT from real 100% ICM CT, while one paper used DL to translate the 100% ICM to non-contrast CT and vice versa. DL models commonly used generative adversarial networks trained and tested by paired contrast-enhanced and non-contrast or low ICM CTs. Image quality metrics such as peak signal-to-noise ratio and structural similarity index were frequently used for comparing synthetic versus real CT image quality.

CONCLUSION : DL-generated contrast-enhanced or non-contrast CT may assist in diagnosis and radiation therapy planning; however, further work to optimize protocols to reduce or eliminate ICM for specific pathology is still needed along with a dedicated assessment of the clinical utility of these synthetic images.

Azarfar Ghazal, Ko Seok-Bum, Adams Scott J, Babyn Paul S

2023-Mar-22

Computed tomography, Contrast enhancement, Deep learning, Iodinated contrast media reduction

General

Machine learning based combination of multi-omics data for subgroup identification in non-small cell lung cancer.

In Scientific reports ; h5-index 158.0

Non-small Cell Lung Cancer (NSCLC) is a heterogeneous disease with a poor prognosis. Identifying novel subtypes in cancer can help classify patients with similar molecular and clinical phenotypes. This work proposes an end-to-end pipeline for subgroup identification in NSCLC. Here, we used a machine learning (ML) based approach to compress the multi-omics NSCLC data to a lower dimensional space. This data is subjected to consensus K-means clustering to identify the five novel clusters (C1-C5). Survival analysis of the resulting clusters revealed a significant difference in the overall survival of clusters (p-value: 0.019). Each cluster was then molecularly characterized to identify specific molecular characteristics. We found that cluster C3 showed minimal genetic aberration with a high prognosis. Next, classification models were developed using data from each omic level to predict the subgroup of unseen patients. Decision‑level fused classification models were then built using these classifiers, which were used to classify unseen patients into five novel clusters. We also showed that the multi-omics-based classification model outperformed single-omic-based models, and the combination of classifiers proved to be a more accurate prediction model than the individual classifiers. In summary, we have used ML models to develop a classification method and identified five novel NSCLC clusters with different genetic and clinical characteristics.

Khadirnaikar Seema, Shukla Sudhanshu, Prasanna S R M

2023-Mar-21

General

SARS-CoV-2 related adaptation mechanisms of rehabilitation clinics affecting patient-centred care: Qualitative study of online patient reports.

In JMIR rehabilitation and assistive technologies

BACKGROUND : The SARS-CoV-2 pandemic impacted the access to inpatient rehabilitation services. At the current state of research, it is unclear to what extent the adaptation of rehabilitation services to infection-protective standards affected patient-centred care in Germany.

OBJECTIVE : This study aimed to explore which aspects of patient-centred care were relevant for patients in inpatient rehabilitation clinics under early-phase pandemic conditions.

METHODS : A deductive-inductive framework analysis of online patient reports posted on a leading German hospital rating website was conducted (www.klinikbewertungen.de). The selected hospital rating website is a third party, patient-centred commercial platform which operates independently of governmental entities. Following a theoretical sampling approach, online reports of rehabilitation stays in two federal states of Germany (Brandenburg, Saarland) uploaded between March 2020 and September 2021 were included. Independently of medical specialty groups, all reports were included. Keywords addressing framework domains were analysed descriptively.

RESULTS : In total, 649 online reports reflecting inpatient rehabilitation services of 31 clinics (Brandenburg N = 23; Saarland N = 8) were analysed. Keywords addressing the care environment were most frequently reported (59.9%) followed by staff prerequisites (33.0%), patient-centred processes (4.5%) and expected outcomes (2.6%). Qualitative in depth-analysis revealed SARS-CoV-2 related reports to be associated with domains of patient-centred processes and staff prerequisites. Discontinuous communication of infection protection standards was perceived to threaten patient autonomy. This was amplified by a tangible gratification crisis of medical staff. Established and emotional supportive relationships to clinicians and peer-groups offered the potential to mitigate adverse effects of infection protection standards.

CONCLUSIONS : Patients predominantly reported feedback associated with the care environment. SARS-CoV-2 related reports were strongly affected by increased staff workloads as well as patient-centred processes addressing discontinuous communication and organizationally demanding implementation of infection protection standards which were perceived to threaten patient autonomy. Peer-relationships formed during inpatient rehabilitation had the potential to mitigate these mechanisms.

CLINICALTRIAL : Not applicable.

Kühn Lukas, Lindert Lara, Kuper Paulina, Choi Kyung-Eun Anna

2023-Mar-05

Pathology

OCELOT: Overlapped Cell on Tissue Dataset for Histopathology

ArXiv Preprint

Cell detection is a fundamental task in computational pathology that can be used for extracting high-level medical information from whole-slide images. For accurate cell detection, pathologists often zoom out to understand the tissue-level structures and zoom in to classify cells based on their morphology and the surrounding context. However, there is a lack of efforts to reflect such behaviors by pathologists in the cell detection models, mainly due to the lack of datasets containing both cell and tissue annotations with overlapping regions. To overcome this limitation, we propose and publicly release OCELOT, a dataset purposely dedicated to the study of cell-tissue relationships for cell detection in histopathology. OCELOT provides overlapping cell and tissue annotations on images acquired from multiple organs. Within this setting, we also propose multi-task learning approaches that benefit from learning both cell and tissue tasks simultaneously. When compared against a model trained only for the cell detection task, our proposed approaches improve cell detection performance on 3 datasets: proposed OCELOT, public TIGER, and internal CARP datasets. On the OCELOT test set in particular, we show up to 6.79 improvement in F1-score. We believe the contributions of this paper, including the release of the OCELOT dataset at https://lunit-io.github.io/research/publications/ocelot are a crucial starting point toward the important research direction of incorporating cell-tissue relationships in computation pathology.

Jeongun Ryu, Aaron Valero Puche, JaeWoong Shin, Seonwook Park, Biagio Brattoli, Jinhee Lee, Wonkyung Jung, Soo Ick Cho, Kyunghyun Paeng, Chan-Young Ock, Donggeun Yoo, Sérgio Pereira

2023-03-23

General

CT-free quantitative SPECT for automatic evaluation of %thyroid uptake based on deep-learning.

In EJNMMI physics

PURPOSE : Quantitative thyroid single-photon emission computed tomography/computed tomography (SPECT/CT) requires computed tomography (CT)-based attenuation correction and manual thyroid segmentation on CT for %thyroid uptake measurements. Here, we aimed to develop a deep-learning-based CT-free quantitative thyroid SPECT that can generate an attenuation map (μ-map) and automatically segment the thyroid.

METHODS : Quantitative thyroid SPECT/CT data (n = 650) were retrospectively analyzed. Typical 3D U-Nets were used for the μ-map generation and automatic thyroid segmentation. Primary emission and scattering SPECTs were inputted to generate a μ-map, and the original μ-map from CT was labeled (268 and 30 for training and validation, respectively). The generated μ-map and primary emission SPECT were inputted for the automatic thyroid segmentation, and the manual thyroid segmentation was labeled (280 and 36 for training and validation, respectively). Other thyroid SPECT/CT (n = 36) and salivary SPECT/CT (n = 29) were employed for verification.

RESULTS : The synthetic μ-map demonstrated a strong correlation (R² = 0.972) and minimum error (mean square error = 0.936 × 10^-4, %normalized mean absolute error = 0.999%) of attenuation coefficients when compared to the ground truth (n = 30). Compared to manual segmentation, the automatic thyroid segmentation was excellent with a Dice similarity coefficient of 0.767, minimal thyroid volume difference of - 0.72 mL, and a short 95% Hausdorff distance of 9.416 mm (n = 36). Additionally, %thyroid uptake by synthetic μ-map and automatic thyroid segmentation (CT-free SPECT) was similar to that by the original μ-map and manual thyroid segmentation (SPECT/CT) (3.772 ± 5.735% vs. 3.682 ± 5.516%, p = 0.1090) (n = 36). Furthermore, the synthetic μ-map generation and automatic thyroid segmentation were successfully performed in the salivary SPECT/CT using the deep-learning algorithms trained by thyroid SPECT/CT (n = 29).

CONCLUSION : CT-free quantitative SPECT for automatic evaluation of %thyroid uptake can be realized by deep-learning.

Kwon Kyounghyoun, Hwang Donghwi, Oh Dongkyu, Kim Ji Hye, Yoo Jihyung, Lee Jae Sung, Lee Won Woo

2023-Mar-22

Quantification; Single-photon emission computed tomography; Deep-learning; Attenuation correction; Segmentation

Surgery

Deep learning-based recognition of key anatomical structures during robot-assisted minimally invasive esophagectomy.

In Surgical endoscopy ; h5-index 65.0

OBJECTIVE : To develop a deep learning algorithm for anatomy recognition in thoracoscopic video frames from robot-assisted minimally invasive esophagectomy (RAMIE) procedures using deep learning.

BACKGROUND : RAMIE is a complex operation with substantial perioperative morbidity and a considerable learning curve. Automatic anatomy recognition may improve surgical orientation and recognition of anatomical structures and might contribute to reducing morbidity or learning curves. Studies regarding anatomy recognition in complex surgical procedures are currently lacking.

METHODS : Eighty-three videos of consecutive RAMIE procedures between 2018 and 2022 were retrospectively collected at University Medical Center Utrecht. A surgical PhD candidate and an expert surgeon annotated the azygos vein and vena cava, aorta, and right lung on 1050 thoracoscopic frames. 850 frames were used for training of a convolutional neural network (CNN) to segment the anatomical structures. The remaining 200 frames of the dataset were used for testing the CNN. The Dice and 95% Hausdorff distance (95HD) were calculated to assess algorithm accuracy.

RESULTS : The median Dice of the algorithm was 0.79 (IQR = 0.20) for segmentation of the azygos vein and/or vena cava. A median Dice coefficient of 0.74 (IQR = 0.86) and 0.89 (IQR = 0.30) were obtained for segmentation of the aorta and lung, respectively. Inference time was 0.026 s (39 Hz). The prediction of the deep learning algorithm was compared with the expert surgeon annotations, showing an accuracy measured in median Dice of 0.70 (IQR = 0.19), 0.88 (IQR = 0.07), and 0.90 (0.10) for the vena cava and/or azygos vein, aorta, and lung, respectively.

CONCLUSION : This study shows that deep learning-based semantic segmentation has potential for anatomy recognition in RAMIE video frames. The inference time of the algorithm facilitated real-time anatomy recognition. Clinical applicability should be assessed in prospective clinical studies.

den Boer R B, Jaspers T J M, de Jongh C, Pluim J P W, van der Sommen F, Boers T, van Hillegersberg R, Van Eijnatten M A J M, Ruurda J P

2023-Mar-22

Anatomy recognition, Computer vision, Deep learning, Robotics, Surgery

Radiology

Transplant renal artery stenosis: utilization of machine learning to identify ancillary sonographic and doppler parameters to predict stenosis in patients with graft dysfunction.

In Abdominal radiology (New York)

PURPOSE : To determine if ancillary sonographic and Doppler parameters can be used to predict transplant renal artery stenosis in patients with renal graft dysfunction.

MATERIALS AND METHODS : IRB-approved, HIPAA-compliant retrospective study included 80 renal transplant patients who had renal US followed by renal angiogram between January 2018 and December 2019. A consensus read of two radiologists recorded these parameters: peak systolic velocity, persistence of elevated velocity, grayscale narrowing, parvus tardus, delayed systolic upstroke, angle of the systolic peak (SP angle), and aliasing. Univariate analysis using t-test or chi-square was performed to determine differences between patients with and without stenosis. P values under 0.05 were deemed statistically significant. We used machine learning algorithms to determine parameters that could better predict the presence of stenosis. The algorithms included logistic regression, random forest, imbalanced random forest, boosting, and CART. All 80 cases were split between training and testing using stratified sampling using a 75:25 split.

RESULTS : We found a statistically significant difference in grayscale narrowing (p = 0.0010), delayed systolic upstroke (p = 0.0002), SP angle (p = 0.0005), and aliasing (p = 0.0024) between the two groups. No significant difference was found for an elevated peak systolic velocity (p = 0.1684). The imbalanced random forest (IRF) model was selected for improved accuracy, sensitivity, and specificity. Specificity, sensitivity, AUC, and normalized Brier score for the IRF model using all parameters were 73%, 81%, 0.82, and 69 in the training set, and 78%, 58%, 0.78, and 80 in the testing set. VIMP assessment showed that the combination of variables that resulted in the most significant change of the training set performance was that of grayscale narrowing and SP angle.

CONCLUSION : Elevated peak systolic velocity did not discriminate between patients with and without TRAS. Adding ancillary parameters into the machine learning algorithm improved specificity and sensitivity similarly in the training and testing sets. The algorithm identified the combination of lumen narrowing coupled with the angle of the systolic peak as better predictor of TRAS. This model may improve the accuracy of ultrasound for transplant renal artery stenosis.

Blain Yamile, Alessandrino Francesco, Scortegagna Eduardo, Balcacer Patricia

2023-Mar-22

Doppler ultrasound, Machine learning, Renal transplant, Transplant renal artery stenosis

General

Localizing post-admixture adaptive variants with object detection on ancestry-painted chromosomes.

In Molecular biology and evolution

Gene flow between previously isolated populations during the founding of an admixed or hybrid population has the potential to introduce adaptive alleles into the new population. If the adaptive allele is common in one source population, but not the other, then as the adaptive allele rises in frequency in the admixed population, genetic ancestry from the source containing the adaptive allele will increase nearby as well. Patterns of genetic ancestry have therefore been used to identify post-admixture positive selection in humans and other animals, including examples in immunity, metabolism, and animal coloration. A common method identifies regions of the genome that have local ancestry 'outliers' compared to the distribution across the rest of the genome, considering each locus independently. However, we lack theoretical models for expected distributions of ancestry under various demographic scenarios, resulting in potential false positives and false negatives. Further, ancestry patterns between distant sites are often not independent. As a result, current methods tend to infer wide genomic regions containing many genes as under selection, limiting biological interpretation. Instead, we develop a deep learning object detection method applied to images generated from local ancestry-painted genomes. This approach preserves information from the surrounding genomic context and avoids potential pitfalls of user-defined summary statistics. We find the-method is robust to a variety of demographic misspecifications using simulated data. Applied to human genotype data from Cabo Verde, we localize a known adaptive locus to a single narrow region compared to multiple or long windows obtained using two other ancestry-based methods.

Hamid Iman, Korunes Katharine L, Schrider Daniel R, Goldberg Amy

2023-Mar-22

General

Understanding Prospective Physicians' Intention to Use Artificial Intelligence in Their Future Medical Practice: Configurational Analysis.

In JMIR medical education

BACKGROUND : Prospective physicians are expected to find artificial intelligence (AI) to be a key technology in their future practice. This transformative change has caught the attention of scientists, educators, and policy makers alike, with substantive efforts dedicated to the selection and delivery of AI topics and competencies in the medical curriculum. Less is known about the behavioral perspective or the necessary and sufficient preconditions for medical students' intention to use AI in the first place.

OBJECTIVE : Our study focused on medical students' knowledge, experience, attitude, and beliefs related to AI and aimed to understand whether they are necessary conditions and form sufficient configurations of conditions associated with behavioral intentions to use AI in their future medical practice.

METHODS : We administered a 2-staged questionnaire operationalizing the variables of interest (ie, knowledge, experience, attitude, and beliefs related to AI, as well as intention to use AI) and recorded 184 responses at t₀ (February 2020, before the COVID-19 pandemic) and 138 responses at t₁ (January 2021, during the COVID-19 pandemic). Following established guidelines, we applied necessary condition analysis and fuzzy-set qualitative comparative analysis to analyze the data.

RESULTS : Findings from the fuzzy-set qualitative comparative analysis show that the intention to use AI is only observed when students have a strong belief in the role of AI (individually necessary condition); certain AI profiles, that is, combinations of knowledge and experience, attitudes and beliefs, and academic level and gender, are always associated with high intentions to use AI (equifinal and sufficient configurations); and profiles associated with nonhigh intentions cannot be inferred from profiles associated with high intentions (causal asymmetry).

CONCLUSIONS : Our work contributes to prior knowledge by showing that a strong belief in the role of AI in the future of medical professions is a necessary condition for behavioral intentions to use AI. Moreover, we suggest that the preparation of medical students should go beyond teaching AI competencies and that educators need to account for the different AI profiles associated with high or nonhigh intentions to adopt AI.

Wagner Gerit, Raymond Louis, Paré Guy

2023-Mar-22

artificial intelligence, attitudes and beliefs, behavioral intentions, fsQCA, fuzzy-set qualitative comparative analysis, knowledge and experience, medical education

Oncology

Investigation of the Trajectory of Muscle and Body Mass as a Prognostic Factor in Patients With Colorectal Cancer: Longitudinal Cohort Study.

In JMIR public health and surveillance

BACKGROUND : Skeletal muscle and BMI are essential prognostic factors for survival in colorectal cancer (CRC). However, there is a lack of understanding due to scarce studies on the continuous aspects of these variables.

OBJECTIVE : This study aimed to evaluate the prognostic impact of the initial status and trajectories of muscle and BMI on overall survival (OS) and assess whether these 4 profiles within 1 year can represent the profiles 6 years later.

METHODS : We analyzed 4056 newly diagnosed patients with CRC between 2010 to 2020. The volume of the muscle with 5-mm thickness at the third lumbar spine level was measured using a pretrained deep learning algorithm. The skeletal muscle volume index (SMVI) was defined as the muscle volume divided by the square of the height. The correlation between BMI status at the first, third, and sixth years of diagnosis was analyzed and assessed similarly for muscle profiles. Prognostic significances of baseline BMI and SMVI and their 1-year trajectories for OS were evaluated by restricted cubic spline analysis and survival analysis. Patients were categorized based on these 4 dimensions, and prognostic risks were predicted and demonstrated using heat maps.

RESULTS : Trajectories of SMVI were categorized as decreased (812/4056, 20%), steady (2014/4056, 49.7%), or increased (1230/4056, 30.3%). Similarly, BMI trajectories were categorized as decreased (792/4056, 19.5%), steady (2253/4056, 55.5%), or increased (1011/4056, 24.9%). BMI and SMVI values in the first year after diagnosis showed a statistically significant correlation with those in the third and sixth years (P<.001). Restricted cubic spline analysis showed a nonlinear relationship between baseline BMI and SMVI change ratio and OS; BMI, in particular, showed a U-shaped correlation. According to survival analysis, increased BMI (hazard ratio [HR] 0.83; P=.02), high baseline SMVI (HR 0.82; P=.04), and obesity stage 1 (HR 0.80; P=.02) showed a favorable impact, whereas decreased SMVI trajectory (HR 1.31; P=.001), decreased BMI (HR 1.23; P=.02), and initial underweight (HR 1.38; P=.02) or obesity stages 2-3 (HR 1.79; P=.01) were negative prognostic factors for OS. Considered simultaneously, BMI >30 kg/m² with a low SMVI at the time of diagnosis resulted in the highest mortality risk. We observed improved survival in patients with increased muscle mass without BMI loss compared to those with steady muscle mass and BMI.

CONCLUSIONS : Profiles within 1 year of both BMI and muscle were surrogate indicators for predicting the later profiles. Continuous trajectories of body and muscle mass are independent prognostic factors of patients with CRC. An automatic algorithm provides a unique opportunity to conduct longitudinal evaluations of body compositions. Further studies to understand the complicated natural courses of muscularity and adiposity are necessary for clinical application.

Seo Dongjin, Kim Han Sang, Ahn Joong Bae, Park Yu Rang

2023-Mar-22

BMI, SMVI, body mass index, colorectal cancer, deep neural network model, skeletal muscle, skeletal muscle volume index

Ophthalmology

A Deep Learning-Based Fully Automated Program for Choroidal Structure Analysis Within the Region of Interest in Myopic Children.

In Translational vision science & technology

PURPOSE : To develop and validate a fully automated program for choroidal structure analysis within a 1500-µm-wide region of interest centered on the fovea (deep learning-based choroidal structure assessment program [DCAP]).

METHODS : A total of 2162 fovea-centered radial swept-source optical coherence tomography (SS-OCT) B-scans from 162 myopic children with cycloplegic spherical equivalent refraction ranging from -1.00 to -5.00 diopters were collected to develop the DCAP. Medical Transformer network and Small Attention U-Net were used to automatically segment the choroid boundaries and the nulla (the deepest point within the fovea). Automatic denoising based on choroidal vessel luminance and binarization were applied to isolate choroidal luminal/stromal areas. To further compare the DCAP with the traditional handcrafted method, the luminal/stromal areas and choroidal vascularity index (CVI) values for 20 OCT images were measured by three graders and the DCAP separately. Intraclass correlation coefficients (ICCs) and limits of agreement were used for agreement analysis.

RESULTS : The mean ± SD pixel-wise distances from the predicted choroidal inner, outer boundary, and nulla to the ground truth were 1.40 ± 1.23, 5.40 ± 2.24, and 1.92 ± 1.13 pixels, respectively. The mean times required for choroidal structure analysis were 1.00, 438.00 ± 75.88, 393.25 ± 78.77, and 410.10 ± 56.03 seconds per image for the DCAP and three graders, respectively. Agreement between the automatic and manual area measurements was excellent (ICCs > 0.900) but poor for the CVI (0.627; 95% confidence interval, 0.279-0.832). Additionally, the DCAP demonstrated better intersession repeatability.

CONCLUSIONS : The DCAP is faster than manual methods. Also, it was able to reduce the intra-/intergrader and intersession variations to a small extent.

TRANSLATIONAL RELEVANCE : The DCAP could aid in choroidal structure assessment.

Xuan Meng, Wang Wei, Shi Danli, Tong James, Zhu Zhuoting, Jiang Yu, Ge Zongyuan, Zhang Jian, Bulloch Gabriella, Peng Guankai, Meng Wei, Li Cong, Xiong Ruilin, Yuan Yixiong, He Mingguang

2023-Mar-01

Ophthalmology

PhacoTrainer: Deep Learning for Cataract Surgical Videos to Track Surgical Tools.

In Translational vision science & technology

PURPOSE : The purpose of this study was to build a deep-learning model that automatically analyzes cataract surgical videos for the locations of surgical landmarks, and to derive skill-related motion metrics.

METHODS : The locations of the pupil, limbus, and 8 classes of surgical instruments were identified by a 2-step algorithm: (1) mask segmentation and (2) landmark identification from the masks. To perform mask segmentation, we trained the YOLACT model on 1156 frames sampled from 268 videos and the public Cataract Dataset for Image Segmentation (CaDIS) dataset. Landmark identification was performed by fitting ellipses or lines to the contours of the masks and deriving locations of interest, including surgical tooltips and the pupil center. Landmark identification was evaluated by the distance between the predicted and true positions in 5853 frames of 10 phacoemulsification video clips. We derived the total path length, maximal speed, and covered area using the tip positions and examined the correlation with human-rated surgical performance.

RESULTS : The mean average precision score and intersection-over-union for mask detection were 0.78 and 0.82. The average distance between the predicted and true positions of the pupil center, phaco tip, and second instrument tip was 5.8, 9.1, and 17.1 pixels. The total path length and covered areas of these landmarks were negatively correlated with surgical performance.

CONCLUSIONS : We developed a deep-learning method to localize key anatomical portions of the eye and cataract surgical tools, which can be used to automatically derive metrics correlated with surgical skill.

TRANSLATIONAL RELEVANCE : Our system could form the basis of an automated feedback system that helps cataract surgeons evaluate their performance.

Yeh Hsu-Hang, Jain Anjal M, Fox Olivia, Sebov Kostya, Wang Sophia Y

2023-Mar-01

Surgery

Comparison of Online-Onboard Adaptive Intensity-Modulated Radiation Therapy or Volumetric-Modulated Arc Radiotherapy With Image-Guided Radiotherapy for Patients With Gynecologic Tumors in Dependence on Fractionation and the Planning Target Volume Margin.

In JAMA network open

IMPORTANCE : Patients with newly diagnosed locally advanced cervical carcinomas or recurrences after surgery undergoing radiochemotherapy whose tumor is unsuited for a brachytherapy boost need high-dose percutaneous radiotherapy with small margins to compensate for clinical target volume deformations and set-up errors. Cone-beam computed tomography-based online adaptive radiotherapy (ART) has the potential to reduce planning target volume (PTV) margins below 5 mm for these tumors.

OBJECTIVE : To compare online ART technologies with image-guided radiotherapy (IGRT) for gynecologic tumors.

DESIGN, SETTING, AND PARTICIPANTS : This comparative effectiveness study comprised all 7 consecutive patients with gynecologic tumors who were treated with ART with artificial intelligence segmentation from January to May 2022 at the West German Cancer Center. All adapted treatment plans were reviewed for the new scenario of organs at risk and target volume. Dose distributions of adapted and scheduled plans optimized on the initial planning computed tomography scan were compared.

EXPOSURE : Online ART for gynecologic tumors.

MAIN OUTCOMES AND MEASURES : Target dose coverage with ART compared with IGRT for PTV margins of 5 mm or less in terms of the generalized equivalent uniform dose (gEUD) without increasing the gEUD for the organs at risk (bladder and rectum).

RESULTS : The first 10 treatment series among 7 patients (mean [SD] age, 65.7 [16.5] years) with gynecologic tumors from a prospective observational trial performed with ART were compared with IGRT. For a clinical PTV margin of 5 mm, IGRT was associated with a median gEUD decrease in the interfractional clinical target volume of -1.5% (90% CI, -31.8% to 2.9%) for all fractions in comparison with the planned dose distribution. Online ART was associated with a decrease of -0.02% (90% CI, -3.2% to 1.5%), which was less than the decrease with IGRT (P < .001). This was not associated with an increase in the gEUD for the bladder or rectum. For a PTV margin of 0 mm, the median gEUD deviation with IGRT was -13.1% (90% CI, -47.9% to 1.6%) compared with 0.1% (90% CI, -2.3% to 6.6%) with ART (P < .001). The benefit associated with ART was larger for a PTV margin of 0 mm than of 5 mm (P = .004) due to spreading of the cold spot at the clinical target volume margin from fraction to fraction with a median SD of 2.4 cm (90% CI, 1.9-3.4 cm) for all patients.

CONCLUSIONS AND RELEVANCE : This study suggests that ART is associated with an improvement in the percentage deviation of gEUD for the interfractional clinical target volume compared with IGRT. As the gain of ART depends on fractionation and PTV margin, a strategy is proposed here to switch from IGRT to ART, if the delivered gEUD distribution becomes unfavorable in comparison with the expected distribution during the course of treatment.

Guberina Maja, Santiago Garcia Alina, Khouya Aymane, Pöttgen Christoph, Holubyev Kostyantyn, Ringbaek Toke Printz, Lachmuth Manfred, Alberti Yasemin, Hoffmann Christian, Hlouschek Julian, Gauler Thomas, Lübcke Wolfgang, Indenkämpen Frank, Stuschke Martin, Guberina Nika

2023-Mar-01

Oncology

An Unsupervised Machine Learning Approach to Evaluating the Association of Symptom Clusters With Adverse Outcomes Among Older Adults With Advanced Cancer: A Secondary Analysis of a Randomized Clinical Trial.

In JAMA network open

IMPORTANCE : Older adults with advanced cancer who have high pretreatment symptom severity often experience adverse events during cancer treatments. Unsupervised machine learning may help stratify patients into different risk groups.

OBJECTIVE : To evaluate whether clusters identified from baseline patient-reported symptom severity were associated with adverse outcomes.

DESIGN, SETTING, AND PARTICIPANTS : This secondary analysis of the Geriatric Assessment Intervention for Reducing Toxicity in Older Patients With Advanced Cancer (GAP70+) Trial (2014-2019) included patients who completed the National Cancer Institute Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE) before starting a new cancer treatment regimen and received care at community oncology sites across the United States. An unsupervised machine learning algorithm (k-means with Euclidean distance) clustered patients based on similarities of baseline symptom severities. Clustering variables included severity items of 24 PRO-CTCAE symptoms (range, 0-4; corresponding to none, mild, moderate, severe, and very severe). Total severity score was calculated as the sum of 24 items (range, 0-96). Whether the clusters were associated with unplanned hospitalization, death, and toxic effects was then examined. Analyses were conducted in January and February 2022.

EXPOSURES : Symptom severity.

MAIN OUTCOMES AND MEASURES : Unplanned hospitalization over 3 months (primary), all-cause mortality over 1 year, and any clinician-rated grade 3 to 5 toxic effect over 3 months.

RESULTS : Of 718 enrolled patients, 706 completed baseline PRO-CTCAE and were included (mean [SD] age, 77.2 [5.5] years, 401 [56.8%] male patients; 51 [7.2%] Black and 619 [87.8%] non-Hispanic White patients; 245 [34.7%] with gastrointestinal cancer; 175 [24.8%] with lung cancer; mean [SD] impaired Geriatric Assessment domains, 4.5 [1.6]). The algorithm classified 310 (43.9%), 295 (41.8%), and 101 (14.3%) into low-, medium-, and high-severity clusters (within-cluster mean [SD] severity scores: low, 6.3 [3.4]; moderate, 16.6 [4.3]; high, 29.8 [7.8]; P < .001). Controlling for sociodemographic variables, clinical factors, study group, and practice site, compared with patients in the low-severity cluster, those in the moderate-severity cluster were more likely to experience hospitalization (risk ratio, 1.36; 95% CI, 1.01-1.84; P = .046). Moderate- and high-severity clusters were associated with a higher risk of death (moderate: hazard ratio, 1.31; 95% CI, 1.01-1.69; P = .04; high: hazard ratio, 2.00; 95% CI, 1.43-2.78; P < .001), but not toxic effects.

CONCLUSIONS AND RELEVANCE : In this study, unsupervised machine learning partitioned patients into distinct symptom severity clusters; patients with higher pretreatment severity were more likely to experience hospitalization and death.

TRIAL REGISTRATION : ClinicalTrials.gov Identifier: NCT02054741.

Xu Huiwen, Mohamed Mostafa, Flannery Marie, Peppone Luke, Ramsdale Erika, Loh Kah Poh, Wells Megan, Jamieson Leah, Vogel Victor G, Hall Bianca Alexandra, Mustian Karen, Mohile Supriya, Culakova Eva

2023-Mar-01

General

A benchmark for machine-learning based non-invasive blood pressure estimation using photoplethysmogram.

In Scientific data

Blood Pressure (BP) is an important cardiovascular health indicator. BP is usually monitored non-invasively with a cuff-based device, which can be bulky and inconvenient. Thus, continuous and portable BP monitoring devices, such as those based on a photoplethysmography (PPG) waveform, are desirable. In particular, Machine Learning (ML) based BP estimation approaches have gained considerable attention as they have the potential to estimate intermittent or continuous BP with only a single PPG measurement. Over the last few years, many ML-based BP estimation approaches have been proposed with no agreement on their modeling methodology. To ease the model comparison, we designed a benchmark with four open datasets with shared preprocessing, the right validation strategy avoiding information shift and leak, and standard evaluation metrics. We also adapted Mean Absolute Scaled Error (MASE) to improve the interpretability of model evaluation, especially across different BP datasets. The proposed benchmark comes with open datasets and codes. We showcase its effectiveness by comparing 11 ML-based approaches of three different categories.

González Sergio, Hsieh Wan-Ting, Chen Trista Pei-Chun

2023-Mar-21

Public Health

Predictors of Cyberchondria during the COVID-19 pandemic: A cross-sectional study using supervised machine learning.

In JMIR formative research

BACKGROUND : Cyberchondria is characterized by repeated and compulsive online searches for health information, resulting in increased health anxiety and distress. It has been conceptualized as a multi-dimensional construct fueled by both anxiety and compulsivity-related factors and described as a "transdiagnostic compulsive behavioral syndrome" which is associated with health anxiety, problematic internet use and obsessive-compulsive symptoms. Cyberchondria is not included in the ICD-11 or the DSM-5, and its defining features, etiological mechanisms and assessment continue to be debated.

OBJECTIVE : This study aimed to investigate changes in the severity of cyberchondria during the pandemic and identify predictors of cyberchondria at this time.

METHODS : Data collection started on May 4, 2020 and ended on June 10, 2020, which corresponds to the first wave of the COVID-19 pandemic in Europe. At the time the present study took place, French-speaking countries in Europe (France, Switzerland, Belgium and Luxembourg) all implemented lockdown or semi-lockdown measures. The survey consisted of a questionnaire collecting demographic information (sex, age, education level and country of residence) and information on socioeconomic circumstances during the first lockdown (e.g., economic situation, housing and employment status), and was followed by several instruments assessing various psychological and health-related constructs. Inclusion criteria for the study were being at least 18 years of age and having a good understanding of French. Self-report data were collected from 725 participants aged 18 to 77 years (mean 33.29, SD 12.88 years), with females constituting the majority (416/725, 57.4%).

RESULTS : The results show that the COVID-19 pandemic affected various facets of cyberchondria: cyberchondria-related distress and interference with functioning increased (distress z=-3.651, P<.001; compulsion z=-5.697, P<.001), whereas the reassurance facet of cyberchondria decreased (z=-6.680, P<.001). Also, COVID-19-related fears and health anxiety emerged as the strongest predictors of cyberchondria-related distress and interference with functioning during the pandemic.

CONCLUSIONS : These findings provide evidence about the impact of the COVID-19 pandemic on cyberchondria and identify factors that should be considered in efforts to prevent and manage cyberchondria at times of public health crises. Also, they are consistent with the theoretical model of cyberchondria during the COVID-19 pandemic proposed by Starcevic and his colleagues in 2020. In addition, the findings have implications for the conceptualization and future assessment of cyberchondria.

Infanti Alexandre, Starcevic Vladan, Schimmenti Adriano, Khazaal Yasser, Karila Laurent, Giardina Alessandro, Flayelle Maèva, Hedayatzadeh Razavi Seyedeh Boshra, Baggio Stéphanie, Vögele Claus, Billieux Joël

2023-Mar-09

Surgery

LABRAD-OR: Lightweight Memory Scene Graphs for Accurate Bimodal Reasoning in Dynamic Operating Rooms

ArXiv Preprint

Modern surgeries are performed in complex and dynamic settings, including ever-changing interactions between medical staff, patients, and equipment. The holistic modeling of the operating room (OR) is, therefore, a challenging but essential task, with the potential to optimize the performance of surgical teams and aid in developing new surgical technologies to improve patient outcomes. The holistic representation of surgical scenes as semantic scene graphs (SGG), where entities are represented as nodes and relations between them as edges, is a promising direction for fine-grained semantic OR understanding. We propose, for the first time, the use of temporal information for more accurate and consistent holistic OR modeling. Specifically, we introduce memory scene graphs, where the scene graphs of previous time steps act as the temporal representation guiding the current prediction. We design an end-to-end architecture that intelligently fuses the temporal information of our lightweight memory scene graphs with the visual information from point clouds and images. We evaluate our method on the 4D-OR dataset and demonstrate that integrating temporality leads to more accurate and consistent results achieving an +5% increase and a new SOTA of 0.88 in macro F1. This work opens the path for representing the entire surgery history with memory scene graphs and improves the holistic understanding in the OR. Introducing scene graphs as memory representations can offer a valuable tool for many temporal understanding tasks.

Ege Özsoy, Tobias Czempiel, Felix Holm, Chantal Pellegrini, Nassir Navab

2023-03-23

General

Deep learning-enabled broadband full-Stokes polarimeter with a portable fiber optical spectrometer.

In Optics letters

Portable fiber optical spectrometers (PFOSs) have been widely used in the contemporary industrial and agricultural production and life due its low cost and small volume. PFOSs mainly combine one fiber to guide light and one optical spectrometer to detect spectra. In this work, we demonstrate that PFOSs can work as a broadband full-Stokes polarimeter through slightly bending the fiber several times and establishing the mapping relationship between the Stokes parameters S^ and the bending-dependent light intensities I^, i.e., S^=f(I^). The different bending geometries bring different birefringence effects and reflection effects that change the polarization state of the out-going light. In the meanwhile, the grating owns a polarization-depended diffraction efficiency especially for the asymmetric illumination geometry that introduces an extrinsic chiroptical effect, which is sensitive to both the linear and spin components of light. The minimum mean squared error (MSE) can reach to smaller than 1% for S₁, S₂, and S₃ at 810 nm, and the averaged MSE in the wave band from 440 nm to 840 nm is smaller than 2.5%, where the working wavelength can be easily extended to arbitrary wave band by applying PFOSs with proper parameters. Our findings provide a convenient and practical method for detecting full-Stokes parameters.

Xian Shilin, Yang Xiu, Zhou Jie, Gao Fuhua, Hou Yidong

2023-Mar-15

General

Conditional Forest Models Built Using Metagenomic Data Accurately Predicted Salmonella Contamination in Northeastern Streams.

In Microbiology spectrum

The use of water contaminated with Salmonella for produce production contributes to foodborne disease burden. To reduce human health risks, there is a need for novel, targeted approaches for assessing the pathogen status of agricultural water. We investigated the utility of water microbiome data for predicting Salmonella contamination of streams used to source water for produce production. Grab samples were collected from 60 New York streams in 2018 and tested for Salmonella. Separately, DNA was extracted from the samples and used for Illumina shotgun metagenomic sequencing. Reads were trimmed and used to assign taxonomy with Kraken2. Conditional forest (CF), regularized random forest (RRF), and support vector machine (SVM) models were implemented to predict Salmonella contamination. Model performance was assessed using 10-fold cross-validation repeated 10 times to quantify area under the curve (AUC) and Kappa score. CF models outperformed the other two algorithms based on AUC (0.86, CF; 0.81, RRF; 0.65, SVM) and Kappa score (0.53, CF; 0.41, RRF; 0.12, SVM). The taxa that were most informative for accurately predicting Salmonella contamination based on CF were compared to taxa identified by ALDEx2 as being differentially abundant between Salmonella-positive and -negative samples. CF and differential abundance tests both identified Aeromonas salmonicida (variable importance [VI] = 0.012) and Aeromonas sp. strain CA23 (VI = 0.025) as the two most informative taxa for predicting Salmonella contamination. Our findings suggest that microbiome-based models may provide an alternative to or complement existing water monitoring strategies. Similarly, the informative taxa identified in this study warrant further investigation as potential indicators of Salmonella contamination of agricultural water. IMPORTANCE Understanding the associations between surface water microbiome composition and the presence of foodborne pathogens, such as Salmonella, can facilitate the identification of novel indicators of Salmonella contamination. This study assessed the utility of microbiome data and three machine learning algorithms for predicting Salmonella contamination of Northeastern streams. The research reported here both expanded the knowledge on the microbiome composition of surface waters and identified putative novel indicators (i.e., Aeromonas species) for Salmonella in Northeastern streams. These putative indicators warrant further research to assess whether they are consistent indicators of Salmonella contamination across regions, waterways, and years not represented in the data set used in this study. Validated indicators identified using microbiome data may be used as targets in the development of rapid (e.g., PCR-based) detection assays for the assessment of microbial safety of agricultural surface waters.

Chung Taejung, Yan Runan, Weller Daniel L, Kovac Jasna

2023-Mar-22

Salmonella, indicators, machine learning, microbiome, surface water, water safety

General

Interpretable machine learning-based approaches for understanding suicide risk and protective factors among South Korean females using survey and social media data.

In Suicide & life-threatening behavior

OBJECTIVE : We aimed to identify and understand risk and protective factors for suicide among South Korean females by linking survey and social media data and using interpretable machine learning approaches.

MATERIALS AND METHODS : We collected a wide range of potential factors including the material, psychosocial, and behavioral data from a detailed survey, which we then linked to data from social media. In addition, we adopted interpretable machine learning approaches to (1) predict the suicide risk, (2) explain the relative importance of factors and their interactions regarding suicide, and (3) understand individual differences affecting suicide risk.

RESULTS : The best-performing machine learning model achieved an AUC of 0.737. Adverse childhood experiences, social connectedness, and mean positive sentiment score of social media posts were the three risk factors that had a monotonic or unimodal relationship with suicide, and satisfaction with life, narcissistic self-presentation, and number of close friends on social media were the three protective factors that had a monotonic or unimodal relationship with suicide. We also found several meaningful interactions between specific psychiatric symptoms and narcissistic self-presentation.

CONCLUSIONS : Our findings can help governmental organizations to better assess female suicide risk in South Korea and develop more informed and customized suicide prevention strategies.

Kim Donghun, Quan Lihong, Seo Mihye, Kim Kihyun, Kim Jae-Won, Zhu Yongjun

2023-Mar-22

female suicide, model interpretation, protective factors, risk factors, suicide risk prediction

General

Language Analytics for Assessment of Mental Health Status and Functional Competency.

In Schizophrenia bulletin ; h5-index 79.0

BACKGROUND AND HYPOTHESIS : Automated language analysis is becoming an increasingly popular tool in clinical research involving individuals with mental health disorders. Previous work has largely focused on using high-dimensional language features to develop diagnostic and prognostic models, but less work has been done to use linguistic output to assess downstream functional outcomes, which is critically important for clinical care. In this work, we study the relationship between automated language composites and clinical variables that characterize mental health status and functional competency using predictive modeling.

STUDY DESIGN : Conversational transcripts were collected from a social skills assessment of individuals with schizophrenia (n = 141), bipolar disorder (n = 140), and healthy controls (n = 22). A set of composite language features based on a theoretical framework of speech production were extracted from each transcript and predictive models were trained. The prediction targets included clinical variables for assessment of mental health status and social and functional competency. All models were validated on a held-out test sample not accessible to the model designer.

STUDY RESULTS : Our models predicted the neurocognitive composite with Pearson correlation PCC = 0.674; PANSS-positive with PCC = 0.509; PANSS-negative with PCC = 0.767; social skills composite with PCC = 0.785; functional competency composite with PCC = 0.616. Language features related to volition, affect, semantic coherence, appropriateness of response, and lexical diversity were useful for prediction of clinical variables.

CONCLUSIONS : Language samples provide useful information for the prediction of a variety of clinical variables that characterize mental health status and functional competency.

Voleti Rohit, Woolridge Stephanie M, Liss Julie M, Milanovic Melissa, Stegmann Gabriela, Hahn Shira, Harvey Philip D, Patterson Thomas L, Bowie Christopher R, Berisha Visar

2023-Mar-22

bipolar disorder, machine learning, natural language processing, schizophrenia, social skills prediction, speech analysis

General

Coreference Delays in Psychotic Discourse: Widening the Temporal Window.

In Schizophrenia bulletin ; h5-index 79.0

BACKGROUND AND HYPOTHESIS : Any form of coherent discourse depends on saying different things about the same entities at different times. Such recurrent references to the same entity need to predictably happen within certain temporal windows. We hypothesized that a failure of control over reference in speakers with schizophrenia (Sz) would become manifest through dynamic temporal measures.

STUDY DESIGN : Conversational speech with a mean of 909.2 words (SD: 178.4) from 20 Chilean Spanish speakers with chronic Sz, 20 speakers at clinical high risk (CHR), and 20 controls were collected. Using directed speech graphs with referential noun phrases (NPs) as nodes, we studied deviances in the topology and temporal distribution of such NPs and of the entities they denote over narrative time.

STUDY RESULTS : The Sz group had a larger density of NPs (number of NPs divided by total words) relative to both controls and CHR. This related to topological measures of distance between recurrent entities, which revealed that the Sz group produced more recurrences, as well as greater topological distances between them, relative to controls. A logistic regression using five topological measures showed that Sz and controls can be distinguished with 84.2% accuracy.

CONCLUSIONS : This pattern indicates a widening of the temporal window in which entities are maintained in discourse and co-referenced in it. It substantiates and extends earlier evidence for deficits in the cognitive control over linguistic reference in psychotic discourse and informs both neurocognitive models of language in Sz and machine learning-based linguistic classifiers of psychotic speech.

Palominos Claudio, Figueroa-Barra Alicia, Hinzen Wolfram

2023-Mar-22

narrative, reference, speech graphs, topological distances

General

Natural Language Processing Markers for Psychosis and Other Psychiatric Disorders: Emerging Themes and Research Agenda From a Cross-Linguistic Workshop.

In Schizophrenia bulletin ; h5-index 79.0

This workshop summary on natural language processing (NLP) markers for psychosis and other psychiatric disorders presents some of the clinical and research issues that NLP markers might address and some of the activities needed to move in that direction. We propose that the optimal development of NLP markers would occur in the context of research efforts to map out the underlying mechanisms of psychosis and other disorders. In this workshop, we identified some of the challenges to be addressed in developing and implementing NLP markers-based Clinical Decision Support Systems (CDSSs) in psychiatric practice, especially with respect to psychosis. Of note, a CDSS is meant to enhance decision-making by clinicians by providing additional relevant information primarily through software (although CDSSs are not without risks). In psychiatry, a field that relies on subjective clinical ratings that condense rich temporal behavioral information, the inclusion of computational quantitative NLP markers can plausibly lead to operationalized decision models in place of idiosyncratic ones, although ethical issues must always be paramount.

Corona Hernández Hugo, Corcoran Cheryl, Achim Amélie M, de Boer Janna N, Boerma Tessel, Brederoo Sanne G, Cecchi Guillermo A, Ciampelli Silvia, Elvevåg Brita, Fusaroli Riccardo, Giordano Silvia, Hauglid Mathias, van Hessen Arjan, Hinzen Wolfram, Homan Philipp, de Kloet Sybren F, Koops Sanne, Kuperberg Gina R, Maheshwari Kritika, Mota Natalia B, Parola Alberto, Rocca Roberta, Sommer Iris E C, Truong Khiet, Voppel Alban E, van Vugt Marieke, Wijnen Frank, Palaniyappan Lena

2023-Mar-22

digital markers, implementation, pathophysiology, psychiatric practice, speech technology

General

Language and Psychosis: Tightening the Association.

In Schizophrenia bulletin ; h5-index 79.0

This special issue of DISCOURSE in Psychosis focuses on the role of language in psychosis, including the relationships between formal thought disorder and conceptual disorganization, with speech and language markers and the neural mechanisms underlying these features in psychosis. It also covers the application of computational techniques in the study of language in psychosis, as well as the potential for using speech and language data for digital phenotyping in psychiatry.

Tan Eric J, Sommer Iris E C, Palaniyappan Lena

2023-Mar-22

artificial intelligence, clinical trials, discourse, linguistics, phenomenology, psycholinguistics, psychopathology

General

Drug Repurposing for viral cancers: A paradigm of machine learning, deep learning, and Virtual screening-based approaches.

In Journal of medical virology

Cancer management is major concern of health organizations and viral cancers account for approximately 15.4% of all known human cancers. Due to large number of patients, efficient treatments for viral cancers are needed. De novo drug discovery is time consuming and expensive process with high failure rate in clinical stages. To address this problem and provide treatments to patients suffering from viral cancers faster, drug repurposing emerges as an effective alternative which aims to find the other indications of the FDA approved drugs. Applied to viral cancers, drug repurposing studies following the niche have tried to find if already existing drugs could be used to treat viral cancers. Multiple drug repurposing approaches till date have been introduced with successful results in viral cancers and many drugs have been successfully repurposed various viral cancers. Here in this study, a critical review of viral cancer related databases, tools, and different machine learning, deep learning and virtual screening-based drug repurposing studies focusing on viral cancers is provided. Additionally, the mechanism of viral cancers is presented along with drug repurposing case study specific to each viral cancer. Finally, the limitations and challenges of various approaches along with possible solutions are provided. This article is protected by copyright. All rights reserved.

Ahmed Faheem, Kang In Suk, Kim Kyung Hwan, Asif Arun, Rahim Chethikkattuveli Salih Abdul, Samantasinghar Anupama, Memon Fida Hussain, Choi Kyung Hyun

2023-Mar-22

Anti-hepatitis B virus antivirals, Antiviral agents, Antiviral agentsAntivir, al agentsAntiviral agentsAntiv, iral agentsAntiviral agentsAntiviral agentsArtificial intelligenceBiostatistics & BioinformaticsRational drug design

Pathology

Screening over Speech in Unselected Populations for Clinical Trials in AD (PROSPECT-AD): Study Design and Protocol.

In The journal of prevention of Alzheimer's disease

BACKGROUND : Speech impairments are an early feature of Alzheimer's disease (AD) and consequently, analysing speech performance is a promising new digital biomarker for AD screening. Future clinical AD trials on disease modifying drugs will require a shift to very early identification of individuals at risk of dementia. Hence, digital markers of language and speech may offer a method for screening of at-risk populations that are at the earliest stages of AD, eventually in combination with advanced machine learning. To this end, we developed a screening battery consisting of speech-based neurocognitive tests. The automated test performs a remote primary screening using a simple telephone.

OBJECTIVES : PROSPECT-AD aims to validate speech biomarkers for identification of individuals with early signs of AD and monitor their longitudinal course through access to well-phenotyped cohorts.

DESIGN : PROSPECT-AD leverages ongoing cohorts such as EPAD (UK), DESCRIBE and DELCODE (Germany), and BioFINDER Primary Care (Sweden) and Beta-AARC (Spain) by adding a collection of speech data over the telephone to existing longitudinal follow-ups. Participants at risk of dementia are recruited from existing parent cohorts across Europe to form an AD 'probability-spectrum', i.e., individuals with a low risk to high risk of developing AD dementia. The characterization of cognition, biomarker and risk factor (genetic and environmental) status of each research participants over time combined with audio recordings of speech samples will provide a well-phenotyped population for comparing novel speech markers with current gold standard biomarkers and cognitive scores.

PARTICIPANTS : N= 1000 participants aged 50 or older will be included in total, with a clinical dementia rating scale (CDR) score of 0 or 0.5. The study protocol is planned to run according to sites between 12 and 18 months.

MEASUREMENTS : The speech protocol includes the following neurocognitive tests which will be administered remotely: Word List [Memory Function], Verbal Fluency [Executive Functions] and spontaneous free speech [Psychological and/ or behavioral symptoms]. Speech features on the linguistic and paralinguistic level will be extracted from the recordings and compared to data from CSF and blood biomarkers, neuroimaging, neuropsychological evaluations, genetic profiles, and family history. Primary candidate marker from speech will be a combination of most significant features in comparison to biomarkers as reference measure. Machine learning and computational techniques will be employed to identify the most significant speech biomarkers that could represent an early indicator of AD pathology. Furthermore, based on the analysis of speech performances, models will be trained to predict cognitive decline and disease progression across the AD continuum.

CONCLUSION : The outcome of PROSPECT-AD may support AD drug development research as well as primary or tertiary prevention of dementia by providing a validated tool using a remote approach for identifying individuals at risk of dementia and monitoring individuals over time, either in a screening context or in clinical trials.

König A, Linz N, Baykara E, Tröger J, Ritchie C, Saunders S, Teipel S, Köhler S, Sánchez-Benavides G, Grau-Rivera O, Gispert J D, Palmqvist S, Tideman P, Hansson O

2023

Alzheimer’s disease, Dementia, cognitive assessment, machine learning, phone-based, screening, speech biomarker

General

Predicting Progression from Normal to MCI and from MCI to AD Using Clinical Variables in the National Alzheimer's Coordinating Center Uniform Data Set Version 3: Application of Machine Learning Models and a Probability Calculator.

In The journal of prevention of Alzheimer's disease

Clinical trials are increasingly focused on pre-manifest and early Alzheimer's disease (AD). Accurately predicting clinical progressions from normal to MCI or from MCI to dementia/AD versus non-progression is challenging. Accurate identification of symptomatic progressors is important to avoid unnecessary treatment and improve trial efficiency. Due to large inter-individual variability, biomarker positivity and comorbidity information are often insufficient to identify those destined to have symptomatic progressions. Using only clinical variables, we aimed to predict clinical progressions, estimating probabilities of progressions with a small set of variables selected by machine learning approaches. This work updates our previous work that was applied to the National Alzheimer's Coordinating Center (NACC) Uniform Data Set Version 2 (V2), by using the most recent version (V3) with additional analyses. We generated a user-friendly conversion probability calculator which can be used for effectively pre-screening trial participants.

Pang Y, Kukull W, Sano M, Albin R L, Shen C, Zhou J, Dodge H H

2023

AD, Alzheimer’s disease, MCI, MCI conversion, National Alzheimer’s Coordinating Center (NACC), PET amyloid, dementia, machine learning

General

A self-adaptive hardware with resistive switching synapses for experience-based neurocomputing.

In Nature communications ; h5-index 260.0

Neurobiological systems continually interact with the surrounding environment to refine their behaviour toward the best possible reward. Achieving such learning by experience is one of the main challenges of artificial intelligence, but currently it is hindered by the lack of hardware capable of plastic adaptation. Here, we propose a bio-inspired recurrent neural network, mastered by a digital system on chip with resistive-switching synaptic arrays of memory devices, which exploits homeostatic Hebbian learning for improved efficiency. All the results are discussed experimentally and theoretically, proposing a conceptual framework for benchmarking the main outcomes in terms of accuracy and resilience. To test the proposed architecture for reinforcement learning tasks, we study the autonomous exploration of continually evolving environments and verify the results for the Mars rover navigation. We also show that, compared to conventional deep learning techniques, our in-memory hardware has the potential to achieve a significant boost in speed and power-saving.

Bianchi S, Muñoz-Martin I, Covi E, Bricalli A, Piccolboni G, Regev A, Molas G, Nodin J F, Andrieu F, Ielmini D

2023-Mar-21

General

Understanding Prospective Physicians' Intention to Use Artificial Intelligence in Their Future Medical Practice: Configurational Analysis.

In JMIR medical education

BACKGROUND : Prospective physicians are expected to find artificial intelligence (AI) to be a key technology in their future practice. This transformative change has caught the attention of scientists, educators, and policy makers alike, with substantive efforts dedicated to the selection and delivery of AI topics and competencies in the medical curriculum. Less is known about the behavioral perspective or the necessary and sufficient preconditions for medical students' intention to use AI in the first place.

OBJECTIVE : Our study focused on medical students' knowledge, experience, attitude, and beliefs related to AI and aimed to understand whether they are necessary conditions and form sufficient configurations of conditions associated with behavioral intentions to use AI in their future medical practice.

METHODS : We administered a 2-staged questionnaire operationalizing the variables of interest (ie, knowledge, experience, attitude, and beliefs related to AI, as well as intention to use AI) and recorded 184 responses at t₀ (February 2020, before the COVID-19 pandemic) and 138 responses at t₁ (January 2021, during the COVID-19 pandemic). Following established guidelines, we applied necessary condition analysis and fuzzy-set qualitative comparative analysis to analyze the data.

RESULTS : Findings from the fuzzy-set qualitative comparative analysis show that the intention to use AI is only observed when students have a strong belief in the role of AI (individually necessary condition); certain AI profiles, that is, combinations of knowledge and experience, attitudes and beliefs, and academic level and gender, are always associated with high intentions to use AI (equifinal and sufficient configurations); and profiles associated with nonhigh intentions cannot be inferred from profiles associated with high intentions (causal asymmetry).

CONCLUSIONS : Our work contributes to prior knowledge by showing that a strong belief in the role of AI in the future of medical professions is a necessary condition for behavioral intentions to use AI. Moreover, we suggest that the preparation of medical students should go beyond teaching AI competencies and that educators need to account for the different AI profiles associated with high or nonhigh intentions to adopt AI.

Wagner Gerit, Raymond Louis, Paré Guy

2023-Mar-22

artificial intelligence, attitudes and beliefs, behavioral intentions, fsQCA, fuzzy-set qualitative comparative analysis, knowledge and experience, medical education

General

Human Behavior in the Time of COVID-19: Learning from Big Data

ArXiv Preprint

Since the World Health Organization (WHO) characterized COVID-19 as a pandemic in March 2020, there have been over 600 million confirmed cases of COVID-19 and more than six million deaths as of October 2022. The relationship between the COVID-19 pandemic and human behavior is complicated. On one hand, human behavior is found to shape the spread of the disease. On the other hand, the pandemic has impacted and even changed human behavior in almost every aspect. To provide a holistic understanding of the complex interplay between human behavior and the COVID-19 pandemic, researchers have been employing big data techniques such as natural language processing, computer vision, audio signal processing, frequent pattern mining, and machine learning. In this study, we present an overview of the existing studies on using big data techniques to study human behavior in the time of the COVID-19 pandemic. In particular, we categorize these studies into three groups - using big data to measure, model, and leverage human behavior, respectively. The related tasks, data, and methods are summarized accordingly. To provide more insights into how to fight the COVID-19 pandemic and future global catastrophes, we further discuss challenges and potential opportunities.

Hanjia Lyu, Arsal Imtiaz, Yufei Zhao, Jiebo Luo

2023-03-23

General

Utility of Environmental Complexity as a Predictor of Alzheimer's Disease Diagnosis: A Big-Data Machine Learning Approach.

In The journal of prevention of Alzheimer's disease

BACKGROUND : Rural-urban differences and spatial navigation deficits have received much attention in Alzheimer's Disease research. While individual environmental and neighborhood factors have been independently investigated, their integrative, multifactorial effects on Alzheimer's diagnosis have not. Here we explore this "environmental complexity" for predictive power in classifying Alzheimer's from cognitively-normal status.

METHODS : We utilized data from the National Alzheimer's Coordinating Center (NACC) uniform data set containing annual visits since 2005 and selected individuals with multiple visits and who remained in their zipcode (N = 22,553). We georeferenced each subject with 3-digit zipcodes of their residences since entering the program. We calculated environmental complexity measures using geospatial tools from street networks and landmarks for spatial navigation in subjects' zipcode zones. Zipcode zones were grouped into two cognitive classes (Cognitively-Normal and Alzheimer's-inclined) based on the ratios of AD and dementia subjects to all subjects in an individual zipcode zone. We randomly selected 80% of the data to train a neural network classifier model on environmental complexity measures to predict the cognitive class for each zone, controlling for salient demographic variables. The remaining 20% served as the test set for performance evaluation.

RESULTS : Our proposed model reached excellent classification ability on the testing data: 83.87% accuracy, 95.23% precision, 83.33% recall, and 0.8889 F1-score (F1-score=1 for perfect prediction). The most salient features of "Alzheimer's-inclined" zipcode zones included longer street-length average, higher circuity, and slightly fewer points of interest. Most "cognitively-normal" zipcode zones appeared in or near urban areas with high environmental complexity measures.

CONCLUSION : Environmental complexity, reflected in frequency and density of street networks and landmarks features, predicted with high precision the cognitive status of 3-digit zipcode zones based on the etiologic diagnoses and observed cognitive impairment of NACC subjects residing in these zones. The zipcode zones vary widely in size (1.6 km2 to 35,241 km2), and large zipcode zones suffer high spatial heterogeneity. Other proven AD risk factors, such as PM2.5, disperse across zones, and so do individual's activities, leading to spatial uncertainty. Nevertheless, the model classifies diagnosis well, establishing the need for prospective experiments to quantify effects of environmental complexity on Alzheimer's development.

Yuan M, Kennedy K M

2023

Alzheimer’s disease, cognitive map, environmental complexity, geospatial mapping, neural network modelling

General

Poincaré maps for visualization of large protein families.

In Briefings in bioinformatics

In the era of constantly increasing amounts of the available protein data, a relevant and interpretable visualization becomes crucial, especially for tasks requiring human expertise. Poincaré disk projection has previously demonstrated its important efficiency for visualization of biological data such as single-cell RNAseq data. Here, we develop a new method PoincaréMSA for visual representation of complex relationships between protein sequences based on Poincaré maps embedding. We demonstrate its efficiency and potential for visualization of protein family topology as well as evolutionary and functional annotation of uncharacterized sequences. PoincaréMSA is implemented in open source Python code with available interactive Google Colab notebooks as described at https://www.dsimb.inserm.fr/POINCARE_MSA.

Susmelj Anna Klimovskaia, Ren Yani, Vander Meersche Yann, Gelly Jean-Christophe, Galochkina Tatiana

2023-Mar-22

data visualization, dimensionality reduction, machine learning, multiple sequence alignment, protein evolution, protein function, protein sequence, sequence similarity

General

Self-healing bottlebrush polymer networks enabled via a side-chain interlocking design.

In Materials horizons

Exploring novel healing mechanisms is a constant impetus for the development of self-healing materials. Herein, we find that side-chain interlocking of bottlebrush polymers can form a dynamic network and thereby serve as a driving force for the self-healing process of the materials. Molecular dynamics simulation indicates that the interlocking is formed by the interpenetration between the long side chains of adjacent molecules and stabilized by van der Waals interactions and molecular entanglements of side chains. The interlocking can be tailored by changing the length and density of the side chains through atom transfer radical polymerization. As a result, the optimized bottlebrush polymer shows a healing efficiency of up to 100%. Unlike chemical interactions, side-chain interlocking eliminates the introduction of specific chemical groups. Therefore, bottlebrush polymers can even self-heal under harsh aqueous conditions, including acid and alkali solutions. Moreover, the highly dynamic side-chain interlocking enables bottlebrush polymers to efficiently dissipate vibration energy, and thus they can be used as damping materials. Collectively, side-chain interlocking expands the scope of physical interactions in self-healing materials and hews out a versatile way for polymers to accomplish self-healing capability in various environments.

Xiong Hui, Yue Tongkui, Wu Qi, Zhang Linjun, Xie Zhengtian, Liu Jun, Zhang Liqun, Wu Jinrong

2023-Mar-22

General

A high-performance deep-learning-based pipeline for whole-brain vasculature segmentation at the capillary resolution.

In Bioinformatics (Oxford, England)

MOTIVATION : Reconstructing and analyzing all blood vessels throughout the brain is significant for understanding brain function, revealing the mechanisms of brain disease, and mapping the whole-brain vascular atlas. Vessel segmentation is a fundamental step in reconstruction and analysis. The whole-brain optical microscopic imaging method enables the acquisition of whole-brain vessel images at the capillary resolution. Due to the massive amount of data and the complex vascular features generated by high-resolution whole-brain imaging, achieving rapid and accurate segmentation of whole-brain vasculature becomes a challenge.

RESULTS : We introduce HP-VSP, a high-performance vessel segmentation pipeline based on deep learning. The pipeline consists of three processes: data blocking, block prediction, and block fusion. We used parallel computing to parallelize this pipeline to improve the efficiency of whole-brain vessel segmentation. We also designed a lightweight deep neural network based on multi-resolution vessel feature extraction to segment vessels at different scales throughout the brain accurately. We validated our approach on whole-brain vascular data from three transgenic mice collected by HD-fMOST. The results show that our proposed segmentation network achieves the state-of-the-art level under various evaluation metrics. In contrast, the parameters of the network are only 1% of those of similar networks. The established segmentation pipeline could be used on various computing platforms and complete the whole-brain vessel segmentation in 3 hours. We also demonstrated that our pipeline could be applied to the vascular analysis.

AVAILABILITY : The dataset is available at http://atlas.brainsmatics.org/a/li2301. The source code is freely available at https://github.com/visionlyx/HP-VSP.

SUPPLEMENTARY INFORMATION : Supplementary data are available at Bioinformatics online.

Li Yuxin, Liu Xuhua, Jia Xueyan, Jiang Tao, Wu Jianghao, Zhang Qianlong, Li Junhuai, Li Xiangning, Li Anan

2023-Mar-22

General

Profiling mechanisms that drive acute oral toxicity in mammals and its prediction via machine learning.

In Toxicological sciences : an official journal of the Society of Toxicology

We present a mechanistic machine-learning quantitative structure-activity relationship (QSAR) model to predict mammalian acute oral toxicity. We trained our model using a rat acute toxicity database compiled by the US National Toxicology Program. We profiled the database using new and published profilers and identified the most plausible mechanisms that drive high acute toxicity (LD50 ≤ 50 mg/kg; GHS categories 1 or 2). Our QSAR model assigns primary mechanisms to compounds, followed by predicting their acute oral LD50 using a random-forest machine-learning model. These predictions were further refined based on structural and mechanistic read-across to substances within the training set. Our model is optimized for sensitivity and aims to minimize the likelihood of under-predicting the toxicity of assessed compounds. It displays high sensitivity (76.1% or 76.6% for compounds in GHS 1-2 or GHS 1-3 categories, respectively), coupled with ≥73.7% balanced accuracy. We further demonstrate the utility of undertaking a mechanistic approach when predicting the toxicity of compounds acting via a rare mode of action (aconitase inhibition). The mechanistic profilers and framework of our QSAR model are route- and toxicity endpoint-agnostic, allowing for future applications to other endpoints and routes of administration. Furthermore, we present a preliminary exploration of the potential role of metabolic clearance in acute toxicity. To the best of our knowledge, this effort represents the first accurate mechanistic QSAR model for acute oral toxicity that combines machine-learning with mode of action (MOA) assignment, while also seeking to minimize under-prediction of more highly potent substances.

Wijeyesakere Sanjeeva J, Auernhammer Tyler, Parks Amanda, Wilson Dan

2023-Mar-22

QSAR, acute toxicity, mechanistic toxicology

General

Blood quality evaluation via on-chip classification of cell morphology using a deep learning algorithm.

In Lab on a chip

The quality of red blood cells (RBCs) in stored blood has a direct impact on the recovery of patients treated by blood transfusion, which directly reflects the quality of blood. The traditional means for blood quality evaluation involve the use of reagents and multi-step and time-consuming operations. Here, a low-cost, multi-classification, label-free and high-precision method is developed, which combines microfluidic technology and a deep learning algorithm together to recognize and classify RBCs based on morphology. The microfluidic channel is designed to effectively and controllably solve the problem of cell overlap, which has a severe negative impact on the identification of cells. The object detection model in the deep learning algorithm is optimized and used to recognize multiple RBCs simultaneously in the whole field of view, so as to classify them into six morphological subcategories and count the numbers in each subgroup. The mean average precision of the developed object detection model reaches 89.24%. The blood quality can be evaluated by calculating the morphology index (MI) according to the numbers of cells in subgroups. The validation of the method is verified by evaluating three blood samples stored for 7 days, 21 days and 42 days, which have MIs of 84.53%, 73.33% and 24.34%, respectively, indicating good agreement with the actual blood quality. This method has the merits of cell identification in a wide channel, no need for single cell alignment as the image cytometry does and it is not only applicable to the quality evaluation of RBCs, but can also be used for general cell identifications with different morphologies.

Yang Yuping, He Hong, Wang Junju, Chen Li, Xu Yi, Ge Chuang, Li Shunbo

2023-Mar-22

General

Artificial Intelligence Teaching as part of Medical Education: A Qualitative Analysis of Expert Interviews.

In JMIR medical education

BACKGROUND : The use of artificial intelligence in medicine is expected to increase significantly in the upcoming years. Advancements in AI technology have the potential to revolutionize healthcare, from aiding in the diagnosis of certain diseases to helping with treatment decisions. Current literature suggests the integration of the subject of AI in medicine as part of the medical curriculum to prepare medical students for the opportunities and challenges related to the use of the technology within the clinical context.

OBJECTIVE : The purpose of this study was to explore the relevant knowledge and understanding of the subject of AI in medicine, and to specify curricula teaching content within medical education.

METHODS : For this research, we conducted 12 guideline-based expert interviews. Experts were defined as individuals who have been engaged in full-time academic research, development, and/or teaching in the field of artificial intelligence in medicine for at least five years. As part of the data analysis, we recorded, transcribed, and analyzed the interviews using qualitative content analysis. We used the software QCAmap and inductive category formation to analyze the data.

RESULTS : The qualitative content analysis led to the formation of three main categories ("Knowledge," "Interpretation," and "Application") with a total of nine associated subcategories. The experts interviewed cited knowledge and an understanding of the fundamentals of AI, statistics, ethics, and privacy and regulation as necessary basic knowledge that should be part of medical education. The analysis also showed that medical students need to be able to interpret as well as critically reflect on the results provided by AI, taking into account the associated risks and data basis. To enable the application of AI in medicine, medical education should promote the acquisition of practical skills, including the need for basic technological skills, as well as the development of confidence in the technology and one's related competencies.

CONCLUSIONS : The analyzed expert interviews' results suggest that medical curricula should include the topic of AI in medicine to develop the knowledge, understanding, and confidence needed to use AI in the clinical context. The results further imply an imminent need for standardization of the definition of AI as the foundation to identify, define, and teach respective content on AI within medical curricula.

Weidener Lukas, Fischer Michael

2023-Mar-21

General

Memristor-based neural networks: a bridge from device to artificial intelligence.

In Nanoscale horizons

Since the beginning of the 21st century, there is no doubt that the importance of artificial intelligence has been highlighted in many fields, among which the memristor-based artificial neural network technology is expected to break through the limitation of von Neumann so as to realize the replication of the human brain by enabling strong parallel computing ability and efficient data processing and become an important way towards the next generation of artificial intelligence. A new type of nanodevice, namely memristor, which is based on the variability of its resistance value, not only has very important applications in nonvolatile information storage, but also presents obsessive progressiveness in highly integrated circuits, making it one of the most promising circuit components in the post-Moore era. In particular, memristors can effectively simulate neural synapses and build neural networks; thus, they can be applied for the preparation of various artificial intelligence systems. This study reviews the research progress of memristors in artificial neural networks in detail and highlights the structural advantages and frontier applications of neural networks based on memristors. Finally, some urgent problems and challenges in current research are summarized and corresponding solutions and future development trends are put forward.

Cao Zelin, Sun Bai, Zhou Guangdong, Mao Shuangsuo, Zhu Shouhui, Zhang Jie, Ke Chuan, Zhao Yong, Shao Jinyou

2023-Mar-22

Radiology

Can automated CT body composition analysis predict high-grade Clavien-Dindo complications in patients with RCC undergoing partial and radical nephrectomy?

In Scottish medical journal

INTRODUCTION : This study investigated the relationship between body tissue composition analysis and complications according to the Clavien-Dindo classification in patients with renal cell carcinoma (RCC) who underwent partial (PN) or radical nephrectomies (RN).

METHODS : We obtained all data of 210 patients with RCC from the 2019 Kidney and Kidney Tumor Segmentation Challenge (C4KC-KiTS) dataset and obtained radiological images from the cancer image archive. Body composition was assessed with automated artificial intelligence software using the convolutional network segmentation technique from abdominal computed tomography images. We included 125 PN and 63 RN in the study. The relationship between body fat and muscle tissue distribution and complications according to the Clavien-Dindo classification was evaluated between these two groups.

RESULTS : Clavien-Dindo 3A and higher (high grade) complications were developed in 9 of 125 patients who underwent PN and 7 of 63 patients who underwent RN. There was no significant difference between all body composition values between patients with and without high-grade complications.

CONCLUSION : This study showed that body muscle-fat tissue distribution did not affect patients with 3A and above complications according to the Clavien-Dindo classification in patients who underwent nephrectomy due to RCC.

Demirel Emin, Dilek Okan

2023-Mar-22

Clavien–Dindo classification, Nephrectomy, adipose, muscle, renal cell cancer, surgical complication

General

[Construction and evaluation of an artificial intelligence-based risk prediction model for death in patients with nasopharyngeal cancer].

In Nan fang yi ke da xue xue bao = Journal of Southern Medical University

OBJECTIVE : To screen the risk factors for death in patients with nasopharyngeal carcinoma (NPC) using artificial intelligence (AI) technology and establish a risk prediction model.

METHODS : The clinical data of NPC patients obtained from SEER database (1973-2015). The patients were randomly divided into model building and verification group at a 7∶3 ratio. Based on the data in the model building group, R software was used to identify the risk factors for death in NPC patients using 4 AI algorithms, namely eXtreme Gradient Boosting (XGBoost), Decision Tree (DT), Least absolute shrinkage and selection operator (LASSO) and random forest (RF), and a risk prediction model was constructed based on the risk factor identified. The C-Index, decision curve analysis (DCA), receiver operating characteristic (ROC) curve and calibration curve (CC) were used for internal validation of the model; the data in the validation group and clinical data of 96 NPC patients (collected from First Affiliated Hospital of Bengbu Medical College) were used for internal and external validation of the model.

RESULTS : The clinical data of a total of 2116 NPC patients were included (1484 in model building group and 632 in verification group). Risk factor screening showed that age, race, gender, stage M, stage T, and stage N were all risk factors of death in NPC patients. The risk prediction model for NPC-related death constructed based on these factors had a C-index of 0.76 for internal evaluation, an AUC of 0.74 and a net benefit rate of DCA of 9%-93%. The C-index of the model in internal verification was 0.740 with an AUC of 0.749 and a net benefit rate of DCA of 3%-89%, suggesting a high consistency of the two calibration curves. In external verification, the C-index of this model was 0.943 with a net benefit rate of DCA of 3%-97% and an AUC of 0.851, and the predicted value was consistent with the actual value.

CONCLUSIONS : Gender, age, race and TNM stage are risk factors of death of NPC patients, and the risk prediction model based on these factors can accurately predict the risks of death in NPC patients.

Zhang H, Lu J, Jiang C, Fang M

2023-Feb-20

artificial intelligence, nasopharyngeal carcinoma, nomogram, predictive model, risk factors

General

Accelerating network layouts using graph neural networks.

In Nature communications ; h5-index 260.0

Graph layout algorithms used in network visualization represent the first and the most widely used tool to unveil the inner structure and the behavior of complex networks. Current network visualization software relies on the force-directed layout (FDL) algorithm, whose high computational complexity makes the visualization of large real networks computationally prohibitive and traps large graphs into high energy configurations, resulting in hard-to-interpret "hairball" layouts. Here we use Graph Neural Networks (GNN) to accelerate FDL, showing that deep learning can address both limitations of FDL: it offers a 10 to 100 fold improvement in speed while also yielding layouts which are more informative. We analytically derive the speedup offered by GNN, relating it to the number of outliers in the eigenspectrum of the adjacency matrix, predicting that GNNs are particularly effective for networks with communities and local regularities. Finally, we use GNN to generate a three-dimensional layout of the Internet, and introduce additional measures to assess the layout quality and its interpretability, exploring the algorithm's ability to separate communities and the link-length distribution. The novel use of deep neural networks can help accelerate other network-based optimization problems as well, with applications from reaction-diffusion systems to epidemics.

Both Csaba, Dehmamy Nima, Yu Rose, Barabási Albert-László

2023-Mar-21

General

Risk Factors and Predictive Modeling for Post-Acute Sequelae of SARS-CoV-2 Infection: Findings from EHR Cohorts of the RECOVER Initiative.

In Research square

Background Patients who were SARS-CoV-2 infected could suffer from newly incidental conditions in their post-acute infection period. These conditions, denoted as the post-acute sequelae of SARS-CoV-2 infection (PASC), are highly heterogeneous and involve a diverse set of organ systems. Limited studies have investigated the predictability of these conditions and their associated risk factors. Method In this retrospective cohort study, we investigated two large-scale PCORnet clinical research networks, INSIGHT and OneFlorida+, including 11 million patients in the New York City area and 16.8 million patients from Florida, to develop machine learning prediction models for those who are at risk for newly incident PASC and to identify factors associated with newly incident PASC conditions. Adult patients aged 20 with SARS-CoV-2 infection and without recorded infection between March 1 ^st , 2020, and November 30 ^th , 2021, were used for identifying associated factors with incident PASC after removing background associations. The predictive models were developed on infected adults. Results We find several incident PASC, e.g., malnutrition, COPD, dementia, and acute kidney failure, were associated with severe acute SARS-CoV-2 infection, defined by hospitalization and ICU stay. Older age and extremes of weight were also associated with these incident conditions. These conditions were better predicted (C-index >0.8). Moderately predictable conditions included diabetes and thromboembolic disease (C-index 0.7-0.8). These were associated with a wider variety of baseline conditions. Less predictable conditions included fatigue, anxiety, sleep disorders, and depression (C-index around 0.6). Conclusions This observational study suggests that a set of likely risk factors for different PASC conditions were identifiable from EHRs, predictability of different PASC conditions was heterogeneous, and using machine learning-based predictive models might help in identifying patients who were at risk of developing incident PASC.

Zang Chengxi, Hou Yu, Schenck Edward, Xu Zhenxing, Zhang Yongkang, Xu Jie, Bian Jiang, Morozyuk Dmitry, Khullar Dhruv, Nordvig Anna, Shenkman Elizabeth, Rothman Russel, Block Jason, Lyman Kristin, Zhang Yiye, Varma Jay, Weiner Mark, Carton Thomas, Wang Fei, Kaushal Rainu, Consortium The Recover

2023-Mar-08

General

SPeC: A Soft Prompt-Based Calibration on Mitigating Performance Variability in Clinical Notes Summarization

ArXiv Preprint

Electronic health records (EHRs) store an extensive array of patient information, encompassing medical histories, diagnoses, treatments, and test outcomes. These records are crucial for enabling healthcare providers to make well-informed decisions regarding patient care. Summarizing clinical notes further assists healthcare professionals in pinpointing potential health risks and making better-informed decisions. This process contributes to reducing errors and enhancing patient outcomes by ensuring providers have access to the most pertinent and current patient data. Recent research has shown that incorporating prompts with large language models (LLMs) substantially boosts the efficacy of summarization tasks. However, we show that this approach also leads to increased output variance, resulting in notably divergent outputs even when prompts share similar meanings. To tackle this challenge, we introduce a model-agnostic Soft Prompt-Based Calibration (SPeC) pipeline that employs soft prompts to diminish variance while preserving the advantages of prompt-based summarization. Experimental findings on multiple clinical note tasks and LLMs indicate that our method not only bolsters performance but also effectively curbs variance for various LLMs, providing a more uniform and dependable solution for summarizing vital medical information.

Yu-Neng Chuang, Ruixiang Tang, Xiaoqian Jiang, Xia Hu

2023-03-23

General

Assessing the performance of ChatGPT in answering questions regarding cirrhosis and hepatocellular carcinoma.

In Clinical and molecular hepatology

BACKGROUND & AIMS : Patients with cirrhosis and hepatocellular carcinoma (HCC) require extensive and personalized care to improve outcomes. ChatGPT (Generative Pre-trained Transformer), a large language model, holds the potential to provide professional yet patient-friendly support. We aimed to examine the accuracy and reproducibility of ChatGPT in answering questions regarding knowledge, management, and emotional support for cirrhosis and HCC.

METHODS : ChatGPT's responses to 164 questions were independently graded by two transplant hepatologists and resolved by a third reviewer. The performance of ChatGPT was also assessed using two published questionnaires and 26 questions formulated from the quality measures of cirrhosis management. Finally, its emotional support capacity was tested.

RESULTS : We showed that ChatGPT regurgitated extensive knowledge of cirrhosis (79.1% correct) and HCC (74.0% correct), but only small proportions (47.3% in cirrhosis, 41.1% in HCC) were labeled as comprehensive. The performance was better in basic knowledge, lifestyle, and treatment than in the domains of diagnosis and preventive medicine. For the quality measures, the model answered 76.9% of questions correctly but failed to specify decision-making cut-offs and treatment durations. ChatGPT lacked knowledge of regional guidelines variations, such as HCC screening criteria. However, it provided practical and multifaceted advice to patients and caregivers regarding the next steps and adjusting to a new diagnosis.

CONCLUSIONS : We analyzed the areas of robustness and limitations of ChatGPT's responses on the management of cirrhosis and HCC and relevant emotional support. ChatGPT may have a role as an adjunct informational tool for patients and physicians to improve outcomes.

Yeo Yee Hui, Samaan Jamil S, Ng Wee Han, Ting Peng-Sheng, Trivedi Hirsh, Vipani Aarshi, Ayoub Walid, Yang Ju Dong, Liran Omer, Spiegel Brennan, Kuo Alexander

2023-Mar-22

accuracy, artificial intelligence, health literacy, patient knowledge, reproducibility

General

Decreased Resting-State Alpha Self-Synchronization in Depressive Disorder.

In Clinical EEG and neuroscience

Background. Depression disorder has been associated with altered oscillatory brain activity. The common methods to quantify oscillatory activity are Fourier and wavelet transforms. Both methods have difficulties distinguishing synchronized oscillatory activity from nonrhythmic and large-amplitude artifacts. Here we proposed a method called self-synchronization index (SSI) to quantify synchronized oscillatory activities in neural data. The method considers temporal characteristics of neural oscillations, amplitude, and cycles, to estimate the synchronization value for a specific frequency band. Method. The recorded electroencephalography (EEG) data of 45 depressed and 55 healthy individuals were used. The SSI method was applied to each EEG electrode filtered in the alpha frequency band (8-13 Hz). The multiple linear regression model was used to predict depression severity (Beck Depression Inventory-II scores) using alpha SSI values. Results. Patients with severe depression showed a lower alpha SSI than those with moderate depression and healthy controls in all brain regions. Moreover, the alpha SSI values negatively correlated with depression severity in all brain regions. The regression model showed a significant performance of depression severity prediction using alpha SSI. Conclusion. The findings support the SSI measure as a powerful tool for quantifying synchronous oscillatory activity. The data examined in this article support the idea that there is a strong link between the synchronization of alpha oscillatory neural activities and the level of depression. These findings yielded an objective and quantitative depression severity prediction.

Mohammadi Yousef, Kafraj Mohadeseh Shafiei, Graversen Carina, Moradi Mohammad Hassan

2023-Mar-21

Beck Depression Inventory-II, EEG, alpha self-synchronization, depression severity, neural oscillations

Radiology

Attention fusion network with self-supervised learning for staging of osteonecrosis of the femoral head (ONFH) using multiple MR protocols.

In Medical physics ; h5-index 59.0

BACKGROUND : Osteonecrosis of the femoral head (ONFH) is characterized as bone cell death in the hip joint, involving a severe pain in the groin. The staging of ONFH is commonly based on MRI and CT, which are important for establishing effective treatment plans. There have been some attempts to automate ONFH staging using deep learning, but few of them used only MR images.

PURPOSE : To propose a deep learning model for MR-only ONFH staging, which can reduce additional cost and radiation exposure from the acquisition of CT images.

METHODS : We integrated information from the MR images of five different imaging protocols by a newly proposed attention fusion method, which was composed of intra-modality attention and inter-modality attention. In addition, a self-supervised learning was used to learn deep representations from a large amount of paired MR-CT dataset. The encoder part of the MR-CT translation network was used as a pretraining network for the staging, which aimed to overcome the lack of annotated data for staging. Ablation studies were performed to investigate the contributions of each proposed method. The area under the receiver operating characteristic curve (AUROC) was used to evaluate the performance of the networks.

RESULTS : Our model improved the performance of the four-way classification of the association research circulation osseous (ARCO) stage using MR images of the multiple protocols by 6.8%p in AUROC over a plain VGG network. Each proposed method increased the performance by 4.7%p (self-supervised learning) and 2.6%p (attention fusion) in AUROC, which was demonstrated by the ablation experiments.

CONCLUSIONS : We have shown the feasibility of the MR-only ONFH staging by using self-supervised learning and attention fusion. A large amount of paired MR-CT data in hospitals can be used to further improve the performance of the staging, and the proposed method has potential to be used in the diagnosis of various diseases that require staging from multiple MR protocols. This article is protected by copyright. All rights reserved.

Kim Bomin, Lee Geun Young, Park Sung-Hong

2023-Mar-21

attention fusion, mr-only staging, multiple mr protocols, osteonecrosis of femoral head, self-supervised learning

General

Need for Objective Task-based Evaluation of Deep Learning-Based Denoising Methods: A Study in the Context of Myocardial Perfusion SPECT.

In ArXiv

Artificial intelligence-based methods have generated substantial interest in nuclear medicine. An area of significant interest has been using deep-learning (DL)-based approaches for denoising images acquired with lower doses, shorter acquisition times, or both. Objective evaluation of these approaches is essential for clinical application. DL-based approaches for denoising nuclear-medicine images have typically been evaluated using fidelity-based figures of merit (FoMs) such as RMSE and SSIM. However, these images are acquired for clinical tasks and thus should be evaluated based on their performance in these tasks. Our objectives were to (1) investigate whether evaluation with these FoMs is consistent with objective clinical-task-based evaluation; (2) provide a theoretical analysis for determining the impact of denoising on signal-detection tasks; (3) demonstrate the utility of virtual clinical trials (VCTs) to evaluate DL-based methods. A VCT to evaluate a DL-based method for denoising myocardial perfusion SPECT (MPS) images was conducted. The impact of DL-based denoising was evaluated using fidelity-based FoMs and AUC, which quantified performance on detecting perfusion defects in MPS images as obtained using a model observer with anthropomorphic channels. Based on fidelity-based FoMs, denoising using the considered DL-based method led to significantly superior performance. However, based on ROC analysis, denoising did not improve, and in fact, often degraded detection-task performance. The results motivate the need for objective task-based evaluation of DL-based denoising approaches. Further, this study shows how VCTs provide a mechanism to conduct such evaluations using VCTs. Finally, our theoretical treatment reveals insights into the reasons for the limited performance of the denoising approach.

Yu Zitong, Rahman Md Ashequr, Laforest Richard, Schindler Thomas H, Gropler Robert J, Wahl Richard L, Siegel Barry A, Jha Abhinav K

2023-Mar-16

General

Understanding metric-related pitfalls in image analysis validation.

In ArXiv

Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately in relation to the underlying research problem. This could be attributed to a lack of accessibility of metric-related knowledge: While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Focusing on biomedical image analysis but with the potential of transfer to other fields, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. To facilitate comprehension, illustrations and specific examples accompany each pitfall. As a structured body of information accessible to researchers of all levels of expertise, this work enhances global comprehension of a key topic in image analysis validation.

Reinke Annika, Tizabi Minu D, Baumgartner Michael, Eisenmann Matthias, Heckmann-Nötzel Doreen, Kavur A Emre, Rädsch Tim, Sudre Carole H, Acion Laura, Antonelli Michela, Arbel Tal, Bakas Spyridon, Benis Arriel, Blaschko Matthew, Büttner Florian, Cardoso M Jorge, Cheplygina Veronika, Chen Jianxu, Christodoulou Evangelia, Cimini Beth A, Collins Gary S, Farahani Keyvan, Ferrer Luciana, Galdran Adrian, van Ginneken Bram, Glocker Ben, Godau Patrick, Haase Robert, Hashimoto Daniel A, Hoffman Michael M, Huisman Merel, Isensee Fabian, Jannin Pierre, Kahn Charles E, Kainmueller Dagmar, Kainz Bernhard, Karargyris Alexandros, Karthikesalingam Alan, Kenngott Hannes, Kleesiek Jens, Kofler Florian, Kooi Thijs, Kopp-Schneider Annette, Kozubek Michal, Kreshuk Anna, Kurc Tahsin, Landman Bennett A, Litjens Geert, Madani Amin, Maier-Hein Klaus, Martel Anne L, Mattson Peter, Meijering Erik, Menze Bjoern, Moons Karel G M, Müller Henning, Nichyporuk Brennan, Nickel Felix, Petersen Jens, Rafelski Susanne M, Rajpoot Nasir, Reyes Mauricio, Riegler Michael A, Rieke Nicola, Saez-Rodriguez Julio, Sánchez Clara I, Shetty Shravya, van Smeden Maarten, Summers Ronald M, Taha Abdel A, Tiulpin Aleksei, Tsaftaris Sotirios A, Calster Ben Van, Varoquaux Gaël, Wiesenfarth Manuel, Yaniv Ziv R, Jäger Paul F, Maier-Hein Lena

2023-Feb-09

General

Roadmap on Deep Learning for Microscopy.

In ArXiv

Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasing resolution and throughput. Artificial intelligence, deep neural networks, and machine learning are all niche terms describing computational methods that have gained a pivotal role in microscopy-based research over the past decade. This Roadmap is written collectively by prominent researchers and encompasses selected aspects of how machine learning is applied to microscopy image data, with the aim of gaining scientific knowledge by improved image quality, automated detection, segmentation, classification and tracking of objects, and efficient merging of information from multiple imaging modalities. We aim to give the reader an overview of the key developments and an understanding of possibilities and limitations of machine learning for microscopy. It will be of interest to a wide cross-disciplinary audience in the physical sciences and life sciences.

Volpe Giovanni, Wählby Carolina, Tian Lei, Hecht Michael, Yakimovich Artur, Monakhova Kristina, Waller Laura, Sbalzarini Ivo F, Metzler Christopher A, Xie Mingyang, Zhang Kevin, Lenton Isaac C D, Rubinsztein-Dunlop Halina, Brunner Daniel, Bai Bijie, Ozcan Aydogan, Midtvedt Daniel, Wang Hao, Sladoje Nataša, Lindblad Joakim, Smith Jason T, Ochoa Marien, Barroso Margarida, Intes Xavier, Qiu Tong, Yu Li-Yu, You Sixian, Liu Yongtao, Ziatdinov Maxim A, Kalinin Sergei V, Sheridan Arlo, Manor Uri, Nehme Elias, Goldenberg Ofri, Shechtman Yoav, Moberg Henrik K, Langhammer Christoph, Špačková Barbora, Helgadottir Saga, Midtvedt Benjamin, Argun Aykut, Thalheim Tobias, Cichos Frank, Bo Stefano, Hubatsch Lars, Pineda Jesus, Manzo Carlo, Bachimanchi Harshith, Selander Erik, Homs-Corbera Antoni, Fränzl Martin, de Haan Kevin, Rivenson Yair, Korczak Zofia, Adiels Caroline Beck, Mijalkov Mite, Veréb Dániel, Chang Yu-Wei, Pereira Joana B, Matuszewski Damian, Kylberg Gustaf, Sintorn Ida-Maria, Caicedo Juan C, Cimini Beth A, Bell Muyinatu A Lediju, Saraiva Bruno M, Jacquemet Guillaume, Henriques Ricardo, Ouyang Wei, Le Trang, Gómez-de-Mariscal Estibaliz, Sage Daniel, Muñoz-Barrutia Arrate, Lindqvist Ebba Josefson, Bergman Johanna

2023-Mar-07

General

Risk Factors and Predictive Modeling for Post-Acute Sequelae of SARS-CoV-2 Infection: Findings from EHR Cohorts of the RECOVER Initiative.

In Research square

Background Patients who were SARS-CoV-2 infected could suffer from newly incidental conditions in their post-acute infection period. These conditions, denoted as the post-acute sequelae of SARS-CoV-2 infection (PASC), are highly heterogeneous and involve a diverse set of organ systems. Limited studies have investigated the predictability of these conditions and their associated risk factors. Method In this retrospective cohort study, we investigated two large-scale PCORnet clinical research networks, INSIGHT and OneFlorida+, including 11 million patients in the New York City area and 16.8 million patients from Florida, to develop machine learning prediction models for those who are at risk for newly incident PASC and to identify factors associated with newly incident PASC conditions. Adult patients aged 20 with SARS-CoV-2 infection and without recorded infection between March 1 ^st , 2020, and November 30 ^th , 2021, were used for identifying associated factors with incident PASC after removing background associations. The predictive models were developed on infected adults. Results We find several incident PASC, e.g., malnutrition, COPD, dementia, and acute kidney failure, were associated with severe acute SARS-CoV-2 infection, defined by hospitalization and ICU stay. Older age and extremes of weight were also associated with these incident conditions. These conditions were better predicted (C-index >0.8). Moderately predictable conditions included diabetes and thromboembolic disease (C-index 0.7-0.8). These were associated with a wider variety of baseline conditions. Less predictable conditions included fatigue, anxiety, sleep disorders, and depression (C-index around 0.6). Conclusions This observational study suggests that a set of likely risk factors for different PASC conditions were identifiable from EHRs, predictability of different PASC conditions was heterogeneous, and using machine learning-based predictive models might help in identifying patients who were at risk of developing incident PASC.

Zang Chengxi, Hou Yu, Schenck Edward, Xu Zhenxing, Zhang Yongkang, Xu Jie, Bian Jiang, Morozyuk Dmitry, Khullar Dhruv, Nordvig Anna, Shenkman Elizabeth, Rothman Russel, Block Jason, Lyman Kristin, Zhang Yiye, Varma Jay, Weiner Mark, Carton Thomas, Wang Fei, Kaushal Rainu, Consortium The Recover

2023-Mar-08

General

Right Ventricular Sarcomere Contractile Depression and the Role of Thick Filament Activation in Human Heart Failure with Pulmonary Hypertension.

In bioRxiv : the preprint server for biology

RATIONALE : Right ventricular (RV) contractile dysfunction commonly occurs and worsens outcomes in heart failure patients with reduced ejection fraction and pulmonary hypertension (HFrEF-PH). However, such dysfunction often goes undetected by standard clinical RV indices, raising concerns that they may not reflect aspects of underlying myocyte dysfunction.

OBJECTIVE : To determine components of myocyte contractile depression in HFrEF-PH, identify those reflected by clinical RV indices, and elucidate their underlying biophysical mechanisms.

METHODS AND RESULTS : Resting, calcium- and load-dependent mechanics were measured in permeabilized RV cardiomyocytes isolated from explanted hearts from 23 HFrEF-PH patients undergoing cardiac transplantation and 9 organ-donor controls. Unsupervised machine learning using myocyte mechanical data with the highest variance yielded two HFrEF-PH subgroups that in turn mapped to patients with depressed (RVd) or compensated (RVc) clinical RV function. This correspondence was driven by reduced calcium-activated isometric tension in RVd, while surprisingly, many other major myocyte contractile measures including peak power, maximum unloaded shortening velocity, and myocyte active stiffness were similarly depressed in both groups. Similar results were obtained when subgroups were first defined by clinical indices, and then myocyte mechanical properties in each group compared. To test the role of thick-filament defects, myofibrillar structure was assessed by X-ray diffraction of muscle fibers. This revealed more myosin heads associated with the thick filament backbone in RVd but not RVc, as compared to controls. This corresponded to reduced myosin ATP turnover in RVd myocytes, indicating less myosin in a cross-bridge ready disordered-relaxed (DRX) state. Altering DRX proportion (%DRX) affected peak calcium-activated tension in the patient groups differently, depending on their basal %DRX, highlighting potential roles for precision-guided therapeutics. Lastly, increasing myocyte preload (sarcomere length) increased %DRX 1.5-fold in controls but only 1.2-fold in both HFrEF-PH groups, revealing a novel mechanism for reduced myocyte active stiffness and by extension Frank-Starling reserve in human HF.

CONCLUSIONS : While there are multiple RV myocyte contractile deficits In HFrEF-PH, clinical indices primarily detect reduced isometric calcium-stimulated force related to deficits in basal and recruitable %DRX myosin. Our results support use of therapies to increase %DRX and enhance length-dependent recruitment of DRX myosin heads in such patients.

Jani Vivek, Aslam M Imran, Fenwick Axel J, Ma Weikang, Gong Henry, Milburn Gregory, Nissen Devin, Salazar Ilton Cubero, Hanselman Olivia, Mukherjee Monica, Halushka Marc K, Margulies Kenneth B, Campbell Kenneth, Irving Thomas C, Kass David A, Hsu Steven

2023-Mar-12

General

MAHOMES II: A webserver for predicting if a metal binding site is enzymatic.

In bioRxiv : the preprint server for biology

UNLABELLED : Recent advances have enabled high-quality computationally generated structures for proteins with no solved crystal structures. However, protein function data remains largely limited to experimental methods and homology mapping. Since structure determines function, it is natural that methods capable of using computationally generated structures for functional annotations need to be advanced. Our laboratory recently developed a method to distinguish between metalloenzyme and non-enzyme sites. Here we report improvements to this method by upgrading our physicochemical features to alleviate the need for structures with sub-angstrom precision and using machine learning to reduce training data labeling error. Our improved classifier identifies protein bound metal sites as enzymatic or non-enzymatic with 94% precision and 92% recall. We demonstrate that both adjustments increased predictive performance and reliability on sites with sub-angstrom variations. We constructed a set of predicted metalloprotein structures with no solved crystal structures and no detectable homology to our training data. Our model had an accuracy of 90 - 97.5% depending on the quality of the predicted structures included in our test. Finally, we found the physicochemical trends that drove this model's successful performance were local protein density, second shell ionizable residue burial, and the pocket's accessibility to the site. We anticipate that our model's ability to correctly identify catalytic metal sites could enable identification of new enzymatic mechanisms and improve de novo metalloenzyme design success rates.

SIGNIFICANCE STATEMENT : Identification of enzyme active sites on proteins with unsolved crystallographic structures can accelerate discovery of novel biochemical reactions, which can impact healthcare, industrial processes, and environmental remediation. Our lab has developed an ML tool for predicting sites on computationally generated protein structures as enzymatic and non-enzymatic. We have made our tool available on a webserver, allowing the scientific community to rapidly search previously unknown protein function space.

Feehan Ryan, Copeland Matthew, Franklin Meghan W, Slusky Joanna S G

2023-Mar-12

General

Structure-Kinetics Relationships of Opioids from Metadynamics and Machine Learning.

In bioRxiv : the preprint server for biology

The nation's opioid overdose deaths reached an all-time high in 2021. The majority of deaths are due to synthetic opioids represented by fentanyl. Naloxone, which is an FDA-approved reversal agent, antagonizes opioids through competitive binding at the mu-opioid receptor (mOR). Thus, knowledge of opioid's residence time is important for assessing the effectiveness of naloxone. Here we estimated the residence times of 15 fentanyl and 4 morphine analogs using metadynamics, and compared them with the most recent measurement of the opioid kinetic, dissociation, and naloxone inhibitory constants (Mann, Li et al, Clin. Pharmacol. Therapeut. 2022). Importantly, the microscopic simulations offered a glimpse at the common binding mechanism and molecular determinants of dissociation kinetics for fentanyl analogs. The insights inspired us to develop a machine learning (ML) approach to analyze the kinetic impact of fentanyl's substituents based on the interactions with mOR residues. This proof-of-concept approach is general; for example, it may be used to tune ligand residence times in computer-aided drug discovery.

Mahinthichaichan Paween, Liu Ruibin, Vo Quynh N, Ellis Christopher R, Stavitskaya Lidiya, Shen Jana

2023-Mar-07

Cardiology

Clinical and genetic associations of deep learning-derived cardiac magnetic resonance-based left ventricular mass.

In Nature communications ; h5-index 260.0

Left ventricular mass is a risk marker for cardiovascular events, and may indicate an underlying cardiomyopathy. Cardiac magnetic resonance is the gold-standard for left ventricular mass estimation, but is challenging to obtain at scale. Here, we use deep learning to enable genome-wide association study of cardiac magnetic resonance-derived left ventricular mass indexed to body surface area within 43,230 UK Biobank participants. We identify 12 genome-wide associations (1 known at TTN and 11 novel for left ventricular mass), implicating genes previously associated with cardiac contractility and cardiomyopathy. Cardiac magnetic resonance-derived indexed left ventricular mass is associated with incident dilated and hypertrophic cardiomyopathies, and implantable cardioverter-defibrillator implant. An indexed left ventricular mass polygenic risk score ≥90^th percentile is also associated with incident implantable cardioverter-defibrillator implant in separate UK Biobank (hazard ratio 1.22, 95% CI 1.05-1.44) and Mass General Brigham (hazard ratio 1.75, 95% CI 1.12-2.74) samples. Here, we perform a genome-wide association study of cardiac magnetic resonance-derived indexed left ventricular mass to identify 11 novel variants and demonstrate that cardiac magnetic resonance-derived and genetically predicted indexed left ventricular mass are associated with incident cardiomyopathy.

Khurshid Shaan, Lazarte Julieta, Pirruccello James P, Weng Lu-Chen, Choi Seung Hoan, Hall Amelia W, Wang Xin, Friedman Samuel F, Nauffal Victor, Biddinger Kiran J, Aragam Krishna G, Batra Puneet, Ho Jennifer E, Philippakis Anthony A, Ellinor Patrick T, Lubitz Steven A

2023-Mar-21

General

A ventilation early warning system (VEWS) for diaphanous workspaces considering COVID-19 and future pandemics scenarios.

In Heliyon

The COVID-19 pandemic has generated new needs due to the associated health risks and, more specifically, its rapid infection rate. Prevention measures to avoid contagions in indoor spaces, especially in office and public buildings (e.g., hospitals, public administration, educational centres, etc.), have led to the need for adequate ventilation to dilute the possible concentration of the virus. This article presents our contribution to this new challenge, namely the Ventilation Early Warning System (VEWS) which has aims to adapt the operation of the current Heating, Ventilating and Air Conditioning (HVAC) systems to the ventilation needs of diaphanous workspaces, based on a Smart Campus Digital Twin (SCDT) framework approach, while maintaining sustainability. Different technologies such as the Internet of Things (IoT), Building Information Modelling (BIM) and Artificial Intelligence (AI) algorithms are combined to collect and integrate monitoring data (historical records, real-time information, and location-related patterns) to carry out forecasting simulations in this digital twin. The generated outputs serve to assist facility managers in their building governance, considering the appropriate application of health measures to reduce the risk of coronavirus contagion in combination with sustainability criteria. The article also provides the results of the implementation of the VEWS in a university workspace as a case study. Its application has made it possible to detect and warn of inadequate ventilation situations for the daily flow of people in the different controlled zones.

Costa Gonçal, Arroyo Oriol, Rueda Pablo, Briones Alan

2023-Mar

BIM, Building digital twin, COVID-19, Facilities management, IoT, Simulation, Smart building

General

Fault Prognosis of Turbofan Engines: Eventual Failure Prediction and Remaining Useful Life Estimation

ArXiv Preprint

In the era of industrial big data, prognostics and health management is essential to improve the prediction of future failures to minimize inventory, maintenance, and human costs. Used for the 2021 PHM Data Challenge, the new Commercial Modular Aero-Propulsion System Simulation dataset from NASA is an open-source benchmark containing simulated turbofan engine units flown under realistic flight conditions. Deep learning approaches implemented previously for this application attempt to predict the remaining useful life of the engine units, but have not utilized labeled failure mode information, impeding practical usage and explainability. To address these limitations, a new prognostics approach is formulated with a customized loss function to simultaneously predict the current health state, the eventual failing component(s), and the remaining useful life. The proposed method incorporates principal component analysis to orthogonalize statistical time-domain features, which are inputs into supervised regressors such as random forests, extreme random forests, XGBoost, and artificial neural networks. The highest performing algorithm, ANN-Flux, achieves AUROC and AUPR scores exceeding 0.95 for each classification. In addition, ANN-Flux reduces the remaining useful life RMSE by 38% for the same test split of the dataset compared to past work, with significantly less computational cost.

Joseph Cohen, Xun Huan, Jun Ni

2023-03-23

Surgery

Computer Vision Analysis of Specimen Mammography to Predict Margin Status.

In medRxiv : the preprint server for health sciences

Intra-operative specimen mammography is a valuable tool in breast cancer surgery, providing immediate assessment of margins for a resected tumor. However, the accuracy of specimen mammography in detecting microscopic margin positivity is low. We sought to develop a deep learning-based model to predict the pathologic margin status of resected breast tumors using specimen mammography. A dataset of specimen mammography images matched with pathology reports describing margin status was collected. Models pre-trained on radiologic images were developed and compared with models pre-trained on non-medical images. Model performance was assessed using sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC). The dataset included 821 images and 53% had positive margins. For three out of four model architectures tested, models pre-trained on radiologic images outperformed domain-agnostic models. The highest performing model, InceptionV3, showed a sensitivity of 84%, a specificity of 42%, and AUROC of 0.71. These results compare favorably with the published literature on surgeon and radiologist interpretation of specimen mammography. With further development, these models could assist clinicians with identifying positive margins intra-operatively and decrease the rate of positive margins and re-operation in breast-conserving surgery.

Chen Kevin A, Kirchoff Kathryn E, Butler Logan R, Holloway Alexa D, Kapadia Muneera R, Gallagher Kristalyn K, Gomez Shawn M

2023-Mar-08

General

Sequence characteristics and an accurate model of high-occupancy target loci in the human genome.

In bioRxiv : the preprint server for biology

Enhancers and promoters are considered to be bound by a small set of TFs in a sequence-specific manner. This assumption has come under increasing skepticism as the datasets of ChIP-seq assays have expanded. Particularly, high-occupancy target (HOT) loci attract hundreds of TFs with seemingly no detectable correlation between ChIP-seq peaks and DNA-binding motif presence. Here, we used 1,003 TF ChIP-seq datasets in HepG2, K562, and H1 cells to analyze the patterns of ChIP-seq peak co-occurrence combined with functional genomics datasets. We identified 43,891 HOT loci forming at the promoter (53%) and enhancer (47%) regions and determined that HOT promoters regulate housekeeping genes, whereas the HOT enhancers are involved in extremely tissue-specific processes. HOT loci form the foundation of human super-enhancers and evolve under strong negative selection, with some of them being ultraconserved regions. Sequence-based classification of HOT loci using deep learning suggests that their formation is driven by sequence features, and the density of ChIP-seq peaks correlates with sequence features. Based on their affinities to bind to promoters and enhancers, we detected five distinct clusters of TFs that form the core of the HOT loci. We also observed that HOT loci are enriched in 3D chromatin hubs and disease-causal variants. In a challenge to the classical model of enhancer activity, we report an abundance of HOT loci in human genome and a commitment of 51% of all ChIP-seq binding events to HOT locus formation and propose a model of HOT locus formation based on the existence of large transcriptional condensates.

Hudaiberdiev Sanjarbek, Ovcharenko Ivan

2023-Feb-05

General

MOVER: Medical Informatics Operating Room Vitals and Events Repository.

In medRxiv : the preprint server for health sciences

Artificial Intelligence (AI) holds great promise for transforming the healthcare industry. However, despite its potential, AI is yet to see widespread deployment in clinical settings in significant part due to the lack of publicly available clinical data and the lack of transparency in the published AI algorithms. There are few clinical data repositories publicly accessible to researchers to train and test AI algorithms, and even fewer that contain specialized data from the perioperative setting. To address this gap, we present and release the Medical Informatics Operating Room Vitals and Events Repository, which includes data from 58,799 unique patients and 83,468 surgeries collected from the UCI Medical Center over a period of seven years. MOVER is freely available to all researchers who sign a data usage agreement, and we hope that it will accelerate the integration of AI into healthcare settings, ultimately leading to improved patient outcomes.

Samad Muntaha, Rinehart Joseph, Angel Mirana, Kanomata Yuzo, Baldi Pierre, Cannesson Maxime

2023-Mar-12

General

Preterm Preeclampsia Risk Modelling: Examining Hemodynamic, Biochemical, and Biophysical Markers Prior to Pregnancy.

In medRxiv : the preprint server for health sciences

UNLABELLED : Preeclampsia (PE) is a leading cause of maternal and perinatal death globally and can lead to unplanned preterm birth. Predicting risk for preterm or early-onset PE, has been investigated primarily after conception, and particularly in the early and mid-gestational periods. However, there is a distinct clinical advantage in identifying individuals at risk for PE prior to conception, when a wider array of preventive interventions are available. In this work, we leverage machine learning techniques to identify potential pre-pregnancy biomarkers of PE in a sample of 80 women, 10 of whom were diagnosed with preterm preeclampsia during their subsequent pregnancy. We explore biomarkers derived from hemodynamic, biophysical, and biochemical measurements and several modeling approaches. A support vector machine (SVM) optimized with stochastic gradient descent yields the highest overall performance with ROC AUC and detection rates up to .88 and .70, respectively on subject-wise cross validation. The best performing models leverage biophysical and hemodynamic biomarkers. While preliminary, these results indicate the promise of a machine learning based approach for detecting individuals who are at risk for developing preterm PE before they become pregnant. These efforts may inform gestational planning and care, reducing risk for adverse PE-related outcomes.

CLINICAL RELEVANCE : This work considers the development and optimization of pre-pregnancy biomarkers for improving the identification of preterm (early-onset) preeclampsia risk prior to conception.

Loftness Bryn C, Bernstein Ira, McBride Carole A, Cheney Nick, McGinnis Ellen W, McGinnis Ryan S

2023-Mar-06

General

Synthesize Extremely High-dimensional Longitudinal Electronic Health Records via Hierarchical Autoregressive Language Model.

In Research square

Synthetic electronic health records (EHRs) that are both realistic and preserve privacy can serve as an alternative to real EHRs for machine learning (ML) modeling and statistical analysis. However, generating high-fidelity and granular electronic health record (EHR) data in its original, highly-dimensional form poses challenges for existing methods due to the complexities inherent in high-dimensional data. In this paper, we propose Hierarchical Autoregressive Language mOdel (HALO) for generating longitudinal high-dimensional EHR, which preserve the statistical properties of real EHR and can be used to train accurate ML models without privacy concerns. Our HALO method, designed as a hierarchical autoregressive model, generates a probability density function of medical codes, clinical visits, and patient records, allowing for the generation of realistic EHR data in its original, unaggregated form without the need for variable selection or aggregation. Additionally, our model also produces high-quality continuous variables in a longitudinal and probabilistic manner. We conducted extensive experiments and demonstrate that HALO can generate high-fidelity EHR data with high-dimensional disease code probabilities ( d ≈ 10,000), disease code co-occurrence probabilities within a visit ( d ≈ 1,000,000), and conditional probabilities across consecutive visits ( d ≈ 5,000,000) and achieve above 0.9 R ² correlation in comparison to real EHR data. In comparison to the leading baseline, HALO improves predictive modeling by over 17% in its predictive accuracy and perplexity on a hold-off test set of real EHR data. This performance then enables downstream ML models trained on its synthetic data to achieve comparable accuracy to models trained on real data (0.938 area under the ROC curve with HALO data vs. 0.943 with real data). Finally, using a combination of real and synthetic data enhances the accuracy of ML models beyond that achieved by using only real EHR data.

Theodorou Brandon, Xiao Cao, Sun Jimeng

2023-Mar-10

General

Direct prediction of Homologous Recombination Deficiency from routine histology in ten different tumor types with attention-based Multiple Instance Learning: a development and validation study.

In medRxiv : the preprint server for health sciences

BACKGROUND : Homologous Recombination Deficiency (HRD) is a pan-cancer predictive biomarker that identifies patients who benefit from therapy with PARP inhibitors (PARPi). However, testing for HRD is highly complex. Here, we investigated whether Deep Learning can predict HRD status solely based on routine Hematoxylin & Eosin (H&E) histology images in ten cancer types.

METHODS : We developed a fully automated deep learning pipeline with attention-weighted multiple instance learning (attMIL) to predict HRD status from histology images. A combined genomic scar HRD score, which integrated loss of heterozygosity (LOH), telomeric allelic imbalance (TAI) and large-scale state transitions (LST) was calculated from whole genome sequencing data for n=4,565 patients from two independent cohorts. The primary statistical endpoint was the Area Under the Receiver Operating Characteristic curve (AUROC) for the prediction of genomic scar HRD with a clinically used cutoff value.

RESULTS : We found that HRD status is predictable in tumors of the endometrium, pancreas and lung, reaching cross-validated AUROCs of 0.79, 0.58 and 0.66. Predictions generalized well to an external cohort with AUROCs of 0.93, 0.81 and 0.73 respectively. Additionally, an HRD classifier trained on breast cancer yielded an AUROC of 0.78 in internal validation and was able to predict HRD in endometrial, prostate and pancreatic cancer with AUROCs of 0.87, 0.84 and 0.67 indicating a shared HRD-like phenotype is across tumor entities.

CONCLUSION : In this study, we show that HRD is directly predictable from H&E slides using attMIL within and across ten different tumor types.

Lavinia Loeffler Chiara Maria, El Nahhas Omar S M, Muti Hannah Sophie, Seibel Tobias, Cifci Didem, van Treeck Marko, Gustav Marco, Carrero Zunamys I, Gaisa Nadine T, Lehmann Kjong-Van, Leary Alexandra, Selenica Pier, Reis-Filho Jorge S, Bruechle Nadina Ortiz, Kather Jakob Nikolas

2023-Mar-10

General

Combining pairwise structural similarity and deep learning interface contact prediction to estimate protein complex model accuracy in CASP15.

In bioRxiv : the preprint server for biology

Estimating the accuracy of quaternary structural models of protein complexes and assemblies (EMA) is important for predicting quaternary structures and applying them to studying protein function and interaction. The pairwise similarity between structural models is proven useful for estimating the quality of protein tertiary structural models, but it has been rarely applied to predicting the quality of quaternary structural models. Moreover, the pairwise similarity approach often fails when many structural models are of low quality and similar to each other. To address the gap, we developed a hybrid method (MULTICOM_qa) combining a pairwise similarity score (PSS) and an interface contact probability score (ICPS) based on the deep learning inter-chain contact prediction for estimating protein complex model accuracy. It blindly participated in the 15th Critical Assessment of Techniques for Protein Structure Prediction (CASP15) in 2022 and ranked first out of 24 predictors in estimating the global accuracy of assembly models. The average per-target correlation coefficient between the model quality scores predicted by MULTICOM_qa and the true quality scores of the models of CASP15 assembly targets is 0.66. The average per-target ranking loss in using the predicted quality scores to rank the models is 0.14. It was able to select good models for most targets. Moreover, several key factors (i.e., target difficulty, model sampling difficulty, skewness of model quality, and similarity between good/bad models) for EMA are identified and analayzed. The results demonstrate that combining the multi-model method (PSS) with the complementary single-model method (ICPS) is a promising approach to EMA. The source code of MULTICOM_qa is available at https://github.com/BioinfoMachineLearning/MULTICOM_qa .

Cheng Jianlin, Roy Raj Shekhor, Liu Jian, Giri Nabin, Guo Zhiye

2023-Mar-12

General

A multiscale functional map of somatic mutations in cancer integrating protein structure and network topology.

In bioRxiv : the preprint server for biology

A major goal of cancer biology is to understand the mechanisms underlying tumorigenesis driven by somatically acquired mutations. Existing computational approaches focus on either scoring the pathogenicity of mutations or characterizing their effects at specific scales. Here, we established a unified computational framework, NetFlow3D, that systematically maps the multiscale mechanistic effects of somatic mutations in cancer. The establishment of NetFlow3D hinges upon the Human Protein Structurome, a complete repository we first compiled that incorporates the 3D structures of every single protein as well as the binding interfaces for all known PPIs in humans. The vast majority of 3D structural information was resolved by recent deep learning algorithms. By applying NetFlow3D to 415,017 somatic protein-altering mutations in 5,950 TCGA tumors across 19 cancer types, we identified 1,656 intra- and 3,343 inter-protein 3D clusters of mutations throughout the Human Protein Structurome, of which ~50% would not have been found if using only experimentally-determined protein structures. These 3D clusters have converging effects on 377 cellular subnetworks. Compared to canonical PPI network analyses, NetFlow3D achieved a 5.5-fold higher statistical power for identifying significantly dysregulated subnetworks. The majority of identified subnetworks were previously obscured by the overwhelming background noise of non-clustered passenger mutations, including portions of non-canonical PRC1, mediator complex, MCM2-7 complex, neddylation of cullins, complement system, TRiC, etc. NetFlow3D and our pan-cancer results can be accessed from http://netflow3d.yulab.org. This work shows that mapping how individual mutations act across scales requires the integration of their local spatial organization on protein structures and their global topological organization in the PPI network.

Zhang Yingying, Leung Alden K, Qiu Tian, Li Le, Zhang Junke, Wierbowski Shayne, Booth James, Yu Haiyuan

2023-Mar-07

General

Fully-automated sarcopenia assessment in head and neck cancer: development and external validation of a deep learning pipeline.

In medRxiv : the preprint server for health sciences

PURPOSE : Sarcopenia is an established prognostic factor in patients diagnosed with head and neck squamous cell carcinoma (HNSCC). The quantification of sarcopenia assessed by imaging is typically achieved through the skeletal muscle index (SMI), which can be derived from cervical neck skeletal muscle (SM) segmentation and cross-sectional area. However, manual SM segmentation is labor-intensive, prone to inter-observer variability, and impractical for large-scale clinical use. To overcome this challenge, we have developed and externally validated a fully-automated image-based deep learning (DL) platform for cervical vertebral SM segmentation and SMI calculation, and evaluated the relevance of this with survival and toxicity outcomes.

MATERIALS AND METHODS : 899 patients diagnosed as having HNSCC with CT scans from multiple institutes were included, with 335 cases utilized for training, 96 for validation, 48 for internal testing and 393 for external testing. Ground truth single-slice segmentations of SM at the C3 vertebra level were manually generated by experienced radiation oncologists. To develop an efficient method of segmenting the SM, a multi-stage DL pipeline was implemented, consisting of a 2D convolutional neural network (CNN) to select the middle slice of C3 section and a 2D U-Net to segment SM areas. The model performance was evaluated using the Dice Similarity Coefficient (DSC) as the primary metric for the internal test set, and for the external test set the quality of automated segmentation was assessed manually by two experienced radiation oncologists. The L3 skeletal muscle area (SMA) and SMI were then calculated from the C3 cross sectional area (CSA) of the auto-segmented SM. Finally, established SMI cut-offs were used to perform further analyses to assess the correlation with survival and toxicity endpoints in the external institution with univariable and multivariable Cox regression.

RESULTS : DSCs for validation set (n = 96) and internal test set (n = 48) were 0.90 (95% CI: 0.90 - 0.91) and 0.90 (95% CI: 0.89 - 0.91), respectively. The predicted CSA is highly correlated with the ground-truth CSA in both validation (r = 0.99, p < 0.0001) and test sets (r = 0.96, p < 0.0001). In the external test set (n = 377), 96.2% of the SM segmentations were deemed acceptable by consensus expert review. Predicted SMA and SMI values were highly correlated with the ground-truth values, with Pearson r β 0.99 (p < 0.0001) for both the female and male patients in all datasets. Sarcopenia was associated with worse OS (HR 2.05 [95% CI 1.04 - 4.04], p = 0.04) and longer PEG tube duration (median 162 days vs. 134 days, HR 1.51 [95% CI 1.12 - 2.08], p = 0.006 in multivariate analysis.

CONCLUSION : We developed and externally validated a fully-automated platform that strongly correlates with imaging-assessed sarcopenia in patients with H&N cancer that correlates with survival and toxicity outcomes. This study constitutes a significant stride towards the integration of sarcopenia assessment into decision-making for individuals diagnosed with HNSCC.

SUMMARY STATEMENT : In this study, we developed and externally validated a deep learning model to investigate the impact of sarcopenia, defined as the loss of skeletal muscle mass, on patients with head and neck squamous cell carcinoma (HNSCC) undergoing radiotherapy. We demonstrated an efficient, fullyautomated deep learning pipeline that can accurately segment C3 skeletal muscle area, calculate cross-sectional area, and derive a skeletal muscle index to diagnose sarcopenia from a standard of care CT scan. In multi-institutional data, we found that pre-treatment sarcopenia was associated with significantly reduced overall survival and an increased risk of adverse events. Given the increased vulnerability of patients with HNSCC, the assessment of sarcopenia prior to radiotherapy may aid in informed treatment decision-making and serve as a predictive marker for the necessity of early supportive measures.

Ye Zezhong, Saraf Anurag, Ravipati Yashwanth, Hoebers Frank, Zha Yining, Zapaishchykova Anna, Likitlersuang Jirapat, Tishler Roy B, Schoenfeld Jonathan D, Margalit Danielle N, Haddad Robert I, Mak Raymond H, Naser Mohamed, Wahid Kareem A, Sahlsten Jaakko, Jaskari Joel, Kaski Kimmo, Mäkitie Antti A, Fuller Clifton D, Aerts Hugo J W L, Kann Benjamin H

2023-Mar-06

General

Microbiome Preterm Birth DREAM Challenge: Crowdsourcing Machine Learning Approaches to Advance Preterm Birth Research.

In medRxiv : the preprint server for health sciences

Globally, every year about 11% of infants are born preterm, defined as a birth prior to 37 weeks of gestation, with significant and lingering health consequences. Multiple studies have related the vaginal microbiome to preterm birth. We present a crowdsourcing approach to predict: (a) preterm or (b) early preterm birth from 9 publicly available vaginal microbiome studies representing 3,578 samples from 1,268 pregnant individuals, aggregated from raw sequences via an open-source tool, MaLiAmPi. We validated the crowdsourced models on novel datasets representing 331 samples from 148 pregnant individuals. From 318 DREAM challenge participants we received 148 and 121 submissions for our two separate prediction sub-challenges with top-ranking submissions achieving bootstrapped AUROC scores of 0.69 and 0.87, respectively. Alpha diversity, VALENCIA community state types, and composition (via phylotype relative abundance) were important features in the top performing models, most of which were tree based methods. This work serves as the foundation for subsequent efforts to translate predictive tests into clinical practice, and to better understand and prevent preterm birth.

Golob Jonathan L, Oskotsky Tomiko T, Tang Alice S, Roldan Alennie, Chung Verena, Ha Connie W Y, Wong Ronald J, Flynn Kaitlin J, Parraga-Leo Antonio, Wibrand Camilla, Minot Samuel S, Andreoletti Gaia, Kosti Idit, Bletz Julie, Nelson Amber, Gao Jifan, Wei Zhoujingpeng, Chen Guanhua, Tang Zheng-Zheng, Novielli Pierfrancesco, Romano Donato, Pantaleo Ester, Amoroso Nicola, Monaco Alfonso, Vacca Mirco, Angelis Maria De, Bellotti Roberto, Tangaro Sabina, Kuntzleman Abigail, Bigcraft Isaac, Techtmann Stephen, Bae Daehun, Kim Eunyoung, Jeon Jongbum, Joe Soobok, Theis Kevin R, Ng Sherrianne, Lee Li Yun S, Bennett Phillip R, MacIntyre David A, Stolovitzky Gustavo, Lynch Susan V, Albrecht Jake, Gomez-Lopez Nardhy, Romero Roberto, Stevenson David K, Aghaeepour Nima, Tarca Adi L, Costello James C, Sirota Marina

2023-Mar-09

Radiology

A Computed Tomography-Based Radiomics Analysis of Low-Energy Proximal Femur Fractures in the Elderly Patients.

In Current radiopharmaceuticals

INTRODUCTION : Low-energy proximal femur fractures in elderly patients result from factors, like osteoporosis and falls. These fractures impose high rates of economic and social costs. In this study, we aimed to build predictive models by applying machine learning (ML) methods on radiomics features to predict low-energy proximal femur fractures.

METHODS : Computed tomography scans of 40 patients (mean ± standard deviation of age = 71 ± 6) with low-energy proximal femur fractures (before a fracture occurs) and 40 individuals (mean ± standard deviation of age = 73 ± 7) as a control group were included. The regions of interest, including neck, trochanteric, and intertrochanteric, were drawn manually. The combinations of 25 classification methods and 8 feature selection methods were applied to radiomics features extracted from ROIs. Accuracy and the area under the receiver operator characteristic curve (AUC) were used to assess ML models' performance.

RESULTS : AUC and accuracy values ranged from 0.408 to 1 and 0.697 to 1, respectively. Three classification methods, including multilayer perceptron (MLP), sequential minimal optimization (SMO), and stochastic gradient descent (SGD), in combination with the feature selection method, SVM attribute evaluation (SAE), exhibited the highest performance in the neck (AUC= 0.999, 0.971 and 0.971, respectively; accuracy = 0.988, 0.988, and 0.988, respectively) and the trochanteric (AUC = 1, 1 and 1, respectively; accuracy = 1, 1 and 1, respectively) regions. The same methods demonstrated the highest performance for the combination of the 3 ROIs' features (AUC = 1, 1 and 1, respectively; accuracy =1, 1 and 1, respectively). In the intertrochanteric region, the combination methods, MLP+SAE, SMO+SAE, and SGD+SAE, as well as the combination of the SAE method and logistic regression (LR) classification method exhibited the highest performance (AUC= 1, 1, 1 and 1, respectively; accuracy= 1, 1, 1 and 1, respectively).

CONCLUSION : Applying machine learning methods to radiomics features is a powerful tool to predict low-energy proximal femur fractures. The results of this study can be verified by conducting more research on bigger datasets.

Mohammadi Seyed Mohammad, Moniri Samir, Mohammadhoseini Payam, Hanafi Mohammad Ghasem, Farasat Maryam, Cheki Mohsen

2023-Mar-21

Radiomics, computed tomography, low-energy fracture, machine learning, osteoporosis, proximal femur

General

COVID-19 diagnosis: A comprehensive review of pre-trained deep learning models based on feature extraction algorithm.

In Results in engineering

Due to the augmented rise of COVID-19, clinical specialists are looking for fast faultless diagnosis strategies to restrict Covid spread while attempting to lessen the computational complexity. In this way, swift diagnosis techniques for COVID-19 with high precision can offer valuable aid to clinical specialists. RT- PCR test is an expensive and tedious COVID diagnosis technique in practice. Medical imaging is feasible to diagnose COVID-19 by X-ray chest radiography to get around the shortcomings of RT-PCR. Through a variety of Deep Transfer-learning models, this research investigates the potential of Artificial Intelligence -based early diagnosis of COVID-19 via X-ray chest radiographs. With 10,192 normal and 3616 Covid X-ray chest radiographs, the deep transfer-learning models are optimized to further the accurate diagnosis. The x-ray chest radiographs undergo a data augmentation phase before developing a modified dataset to train the Deep Transfer-learning models. The Deep Transfer-learning architectures are trained using the extracted features from the Feature Extraction stage. During training, the classification of X-ray Chest radiographs based on feature extraction algorithm values is converted into a feature label set containing the classified image data with a feature string value representing the number of edges detected after edge detection. The feature label set is further tested with the SVM, KNN, NN, Naive Bayes and Logistic Regression classifiers to audit the quality metrics of the proposed model. The quality metrics include accuracy, precision, F1 score, recall and AUC. The Inception-V3 dominates the six Deep Transfer-learning models, according to the assessment results, with a training accuracy of 84.79% and a loss function of 2.4%. The performance of Cubic SVM was superior to that of the other SVM classifiers, with an AUC score of 0.99, precision of 0.983, recall of 0.8977, accuracy of 95.8%, and F1 score of 0.9384. Cosine KNN fared better than the other KNN classifiers with an AUC score of 0.95, precision of 0.974, recall of 0.777, accuracy of 90.8%, and F1 score of 0.864. Wide NN fared better than the other NN classifiers with an AUC score of 0.98, precision of 0.975, recall of 0.907, accuracy of 95.5%, and F1 score of 0.939. According to the findings, SVM classifiers topped other classifiers in terms of performance indicators like accuracy, precision, recall, F1-score, and AUC. The SVM classifiers reported better mean optimal scores compared to other classifiers. The performance assessment metrics uncover that the proposed methodology can aid in preliminary COVID diagnosis.

Poola Rahul Gowtham, Pl Lahari, Y Siva Sankar

2023-Jun

Boundary tracing, Covid diagnosis, Deep transfer-learning, Medical imaging, Neural network models and classifiers

General

Forecast-Aware Model Driven LSTM

ArXiv Preprint

Poor air quality can have a significant impact on human health. The National Oceanic and Atmospheric Administration (NOAA) air quality forecasting guidance is challenged by the increasing presence of extreme air quality events due to extreme weather events such as wild fires and heatwaves. These extreme air quality events further affect human health. Traditional methods used to correct model bias make assumptions about linearity and the underlying distribution. Extreme air quality events tend to occur without a strong signal leading up to the event and this behavior tends to cause existing methods to either under or over compensate for the bias. Deep learning holds promise for air quality forecasting in the presence of extreme air quality events due to its ability to generalize and learn nonlinear problems. However, in the presence of these anomalous air quality events, standard deep network approaches that use a single network for generalizing to future forecasts, may not always provide the best performance even with a full feature-set including geography and meteorology. In this work we describe a method that combines unsupervised learning and a forecast-aware bi-directional LSTM network to perform bias correction for operational air quality forecasting using AirNow station data for ozone and PM2.5 in the continental US. Using an unsupervised clustering method trained on station geographical features such as latitude and longitude, urbanization, and elevation, the learned clusters direct training by partitioning the training data for the LSTM networks. LSTMs are forecast-aware and implemented using a unique way to perform learning forward and backwards in time across forecasting days. When comparing the RMSE of the forecast model to the RMSE of the bias corrected model, the bias corrected model shows significant improvement (27\% lower RMSE for ozone) over the base forecast.

Sophia Hamer, Jennifer Sleeman, Ivanka Stajner

2023-03-23

General

Human Behavior in the Time of COVID-19: Learning from Big Data

ArXiv Preprint

Since the World Health Organization (WHO) characterized COVID-19 as a pandemic in March 2020, there have been over 600 million confirmed cases of COVID-19 and more than six million deaths as of October 2022. The relationship between the COVID-19 pandemic and human behavior is complicated. On one hand, human behavior is found to shape the spread of the disease. On the other hand, the pandemic has impacted and even changed human behavior in almost every aspect. To provide a holistic understanding of the complex interplay between human behavior and the COVID-19 pandemic, researchers have been employing big data techniques such as natural language processing, computer vision, audio signal processing, frequent pattern mining, and machine learning. In this study, we present an overview of the existing studies on using big data techniques to study human behavior in the time of the COVID-19 pandemic. In particular, we categorize these studies into three groups - using big data to measure, model, and leverage human behavior, respectively. The related tasks, data, and methods are summarized accordingly. To provide more insights into how to fight the COVID-19 pandemic and future global catastrophes, we further discuss challenges and potential opportunities.

Hanjia Lyu, Arsal Imtiaz, Yufei Zhao, Jiebo Luo

2023-03-23

General

Natural language processing models reveal neural dynamics of human conversation.

In bioRxiv : the preprint server for biology

Human verbal communication requires a rapid interplay between speech planning, production, and comprehension. These processes are subserved by local and long-range neural dynamics across widely distributed brain areas. How linguistic information is precisely represented during natural conversation or what shared neural processes are involved, however, remain largely unknown. Here we used intracranial neural recordings in participants engaged in free dialogue and employed deep learning natural language processing models to find a striking similarity not only between neural-to-artificial network activities but also between how linguistic information is encoded in brain during production and comprehension. Collectively, neural activity patterns that encoded linguistic information were closely aligned to those reflecting speaker-listener transitions and were reduced after word utterance or when no conversation was held. They were also observed across distinct mesoscopic areas and frequency bands during production and comprehension, suggesting that these signals reflected the hierarchically structured information being conveyed during dialogue. Together, these findings suggest that linguistic information is encoded in the brain through similar neural representations during both speaking and listening, and start to reveal the distributed neural dynamics subserving human communication.

Cai Jing, Hadjinicolaou Alex E, Paulk Angelique C, Williams Ziv M, Cash Sydney S

2023-Mar-11

General

Digitally Diagnosing Multiple Developmental Delays using Crowdsourcing fused with Machine Learning: A Research Protocol.

In medRxiv : the preprint server for health sciences

BACKGROUND : Roughly 17% percent of minors in the United States aged 3 through 17 years have a diagnosis of one or more developmental or psychiatric conditions, with the true prevalence likely being higher due to underdiagnosis in rural areas and for minority populations. Unfortunately, timely diagnostic services are inaccessible to a large portion of the United States and global population due to cost, distance, and clinician availability. Digital phenotyping tools have the potential to shorten the time-to-diagnosis and to bring diagnostic services to more people by enabling accessible evaluations. While automated machine learning (ML) approaches for detection of pediatric psychiatry conditions have garnered increased research attention in recent years, existing approaches use a limited set of social features for the prediction task and focus on a single binary prediction.

OBJECTIVE : I propose the development of a gamified web system for data collection followed by a fusion of novel crowdsourcing algorithms with machine learning behavioral feature extraction approaches to simultaneously predict diagnoses of Autism Spectrum Disorder (ASD) and Attention-Deficit/Hyperactivity Disorder (ADHD) in a precise and specific manner.

METHODS : The proposed pipeline will consist of: (1) a gamified web applications to curate videos of social interactions adaptively based on needs of the diagnostic system, (2) behavioral feature extraction techniques consisting of automated ML methods and novel crowdsourcing algorithms, and (3) development of ML models which classify several conditions simultaneously and which adaptively request additional information based on uncertainties about the data.

CONCLUSIONS : The prospective for high reward stems from the possibility of creating the first AI-powered tool which can identify complex social behaviors well enough to distinguish conditions with nuanced differentiators such as ASD and ADHD.

Washington Peter

2023-Mar-07

General

Conserved cysteine residues in Kaposi's sarcoma herpesvirus ORF34 are necessary for viral production and viral pre-initiation complex formation.

In bioRxiv : the preprint server for biology

UNLABELLED : Kaposi's sarcoma herpesvirus (KSHV) ORF34 is a component of the viral pre-initiation complex (vPIC), a highly conserved piece of machinery essential for late gene expression among beta- and gamma-herpes viruses. KSHV ORF34 is also estimated to be a hub protein, associated with the majority of vPIC components. However, the precise mechanisms underlying how the ORF34 molecule contributes to the vPIC function, including the binding manner to other vPIC components, remain unclear. Therefore, we constructed ORF34 alanine-scanning mutants, in which amino-acid residues that were conserved among other herpesviruses had been replaced by alanine. The mutants were analyzed for their binding functions to other vPIC factors, and then were evaluated for their recovering ability of viral production using the cells harboring ORF34-deficient KSHV-BAC. The results demonstrated that at least four cysteines conserved in ORF34 were crucial for binding to other vPIC components, ORF24 and ORF66, virus production, and late gene transcription and expression. Based on the amino acid sequence of ORF34, these four cysteines were expected to constitute a pair of C-Xn-C consensus motifs. An artificial intelligence-predicted structure model revealed that the four cysteines were present tetrahedrally in an intramolecular fashion. Another prediction algorithm indicated the possible capture of metal cations by ORF34. Furthermore, it was experimentally observed that the elimination of cations by a selective chelator resulted in the loss of ORF34's binding ability to other vPIC components. In conclusion, our results suggest the functional importance of KSHV ORF34 conserved cysteines for vPIC components assembly and viral replication.

IMPORTANCE : The gamma- and beta-herpesvirus family conserve the viral-factor based mechanism for initiating viral late gene transcription. This viral pre-initiation complex (vPIC) is a functional analog to cellular PIC consisting of general transcriptional factors. We focused on KSHV ORF34, an essential factor for viral replication as a vPIC component. The precise mechanism underlying vPIC formation and critical domain structure of ORF34 for its function are presently unclear. Therefore, we investigated the contribution of conserved amino-acid residues among ORF34 homologs to virus production, late gene expression, and interaction with other vPIC components. We demonstrated for the first time that four conserved cysteines (C170, C175, C256, and C259) in ORF34 are essential for vPIC formation, late gene transcription, and viral production. Importantly, the predicted structure model and biochemical experiment provide evidence showing that these four conserved cysteines are present in a tetrahedral formation which helped to maintain metal cation.

Watanabe Tadashi, Narahari Akshara, Bhardwaj Esha, Kuriyama Kazushi, Nishimura Mayu, Izumi Taisuke, Fujimuro Masahiro, Ohno Shinji

2023-Mar-09

General

Aberrant phase separation is a common killing strategy of positively charged peptides in biology and human disease.

In bioRxiv : the preprint server for biology

Positively charged repeat peptides are emerging as key players in neurodegenerative diseases. These peptides can perturb diverse cellular pathways but a unifying framework for how such promiscuous toxicity arises has remained elusive. We used mass-spectrometry-based proteomics to define the protein targets of these neurotoxic peptides and found that they all share similar sequence features that drive their aberrant condensation with these positively charged peptides. We trained a machine learning algorithm to detect such sequence features and unexpectedly discovered that this mode of toxicity is not limited to human repeat expansion disorders but has evolved countless times across the tree of life in the form of cationic antimicrobial and venom peptides. We demonstrate that an excess in positive charge is necessary and sufficient for this killer activity, which we name 'polycation poisoning'. These findings reveal an ancient and conserved mechanism and inform ways to leverage its design rules for new generations of bioactive peptides.

Boeynaems Steven, Ma X Rosa, Yeong Vivian, Ginell Garrett M, Chen Jian-Hua, Blum Jacob A, Nakayama Lisa, Sanyal Anushka, Briner Adam, Van Haver Delphi, Pauwels Jarne, Ekman Axel, Schmidt H Broder, Sundararajan Kousik, Porta Lucas, Lasker Keren, Larabell Carolyn, Hayashi Mirian A F, Kundaje Anshul, Impens Francis, Obermeyer Allie, Holehouse Alex S, Gitler Aaron D

2023-Mar-09

General

Interpreting Generative Adversarial Networks to Infer Natural Selection from Genetic Data.

In bioRxiv : the preprint server for biology

MOTIVATION : Understanding the landscape of natural selection in humans and other species has been a major focus for the use of machine learning methods in population genetics. Existing methods rely on computationally intensive simulated training data incorporating selection. Unlike efficient neutral coalescent simulations for demographic inference, realistic selection typically requires slow forward simulations. Large populations sizes (for example due to recent exponential growth in humans) make these simulations even more prohibitive. Because there are many possible modes of selection, a high dimensional parameter space must be explored, with no guarantee that the simulated models are close to the real processes. Since machine learning methods use the simulated data for training, mismatches between simulated training data and real test data are particularly problematic. In addition, it has been difficult to interpret the trained neural networks, leading to a lack of understanding about what features contribute to identifying selected variants.

RESULTS : Here we develop a new approach to detect selection that does not require selection simulations during training. We use a Generative Adversarial Network (GAN) that has been trained to simulate neutral data that mirrors a real genomic dataset. The resulting GAN consists of a generator (demographic model) and a discriminator (convolutional neural network). For a given genomic region, the discriminator predicts whether it is "real" genomic data or "fake" in the sense that it could have been simulated by the generator. As the "real" training data includes regions that experienced selection and the generator cannot produce such regions, regions with a high probability of being real may have experienced selection. This enables us to apply the trained discriminator of the GAN to held-out test data and identify candidate selected regions. We show that this approach has high power to identify regions under selection in simulations, and that it reliably identifies selected regions identified by state-of-the art population genetic methods in three human populations (YRI, CEU, and CHB). Finally, we show how to interpret the trained networks by clustering hidden units of the discriminator based on their correlation patterns with known summary statistics. In summary, our approach is a novel, efficient, and powerful way to use machine learning to detect natural selection.

AVAILABILITY : Our software is available open-source at https://github.com/mathiesonlab/disc-pg-gan.

Riley Rebecca, Mathieson Iain, Mathieson Sara

2023-Mar-08

General

Comparative study of convolutional neural network architectures for gastrointestinal lesions classification.

In PeerJ

The gastrointestinal (GI) tract can be affected by different diseases or lesions such as esophagitis, ulcers, hemorrhoids, and polyps, among others. Some of them can be precursors of cancer such as polyps. Endoscopy is the standard procedure for the detection of these lesions. The main drawback of this procedure is that the diagnosis depends on the expertise of the doctor. This means that some important findings may be missed. In recent years, this problem has been addressed by deep learning (DL) techniques. Endoscopic studies use digital images. The most widely used DL technique for image processing is the convolutional neural network (CNN) due to its high accuracy for modeling complex phenomena. There are different CNNs that are characterized by their architecture. In this article, four architectures are compared: AlexNet, DenseNet-201, Inception-v3, and ResNet-101. To determine which architecture best classifies GI tract lesions, a set of metrics; accuracy, precision, sensitivity, specificity, F1-score, and area under the curve (AUC) were used. These architectures were trained and tested on the HyperKvasir dataset. From this dataset, a total of 6,792 images corresponding to 10 findings were used. A transfer learning approach and a data augmentation technique were applied. The best performing architecture was DenseNet-201, whose results were: 97.11% of accuracy, 96.3% sensitivity, 99.67% specificity, and 95% AUC.

Cuevas-Rodriguez Erik O, Galvan-Tejada Carlos E, Maeda-Gutiérrez Valeria, Moreno-Chávez Gamaliel, Galván-Tejada Jorge I, Gamboa-Rosales Hamurabi, Luna-García Huizilopoztli, Moreno-Baez Arturo, Celaya-Padilla José María

2023

Classification, Computer-aided diagnostic, Convolutional neural network, Deep learning, Endoscopy, Gastrointestinal, Gastrointestinal lesions

General

A ventilation early warning system (VEWS) for diaphanous workspaces considering COVID-19 and future pandemics scenarios.

In Heliyon

The COVID-19 pandemic has generated new needs due to the associated health risks and, more specifically, its rapid infection rate. Prevention measures to avoid contagions in indoor spaces, especially in office and public buildings (e.g., hospitals, public administration, educational centres, etc.), have led to the need for adequate ventilation to dilute the possible concentration of the virus. This article presents our contribution to this new challenge, namely the Ventilation Early Warning System (VEWS) which has aims to adapt the operation of the current Heating, Ventilating and Air Conditioning (HVAC) systems to the ventilation needs of diaphanous workspaces, based on a Smart Campus Digital Twin (SCDT) framework approach, while maintaining sustainability. Different technologies such as the Internet of Things (IoT), Building Information Modelling (BIM) and Artificial Intelligence (AI) algorithms are combined to collect and integrate monitoring data (historical records, real-time information, and location-related patterns) to carry out forecasting simulations in this digital twin. The generated outputs serve to assist facility managers in their building governance, considering the appropriate application of health measures to reduce the risk of coronavirus contagion in combination with sustainability criteria. The article also provides the results of the implementation of the VEWS in a university workspace as a case study. Its application has made it possible to detect and warn of inadequate ventilation situations for the daily flow of people in the different controlled zones.

Costa Gonçal, Arroyo Oriol, Rueda Pablo, Briones Alan

2023-Mar

BIM, Building digital twin, COVID-19, Facilities management, IoT, Simulation, Smart building

General

COVID-19 diagnosis: A comprehensive review of pre-trained deep learning models based on feature extraction algorithm.

In Results in engineering

Due to the augmented rise of COVID-19, clinical specialists are looking for fast faultless diagnosis strategies to restrict Covid spread while attempting to lessen the computational complexity. In this way, swift diagnosis techniques for COVID-19 with high precision can offer valuable aid to clinical specialists. RT- PCR test is an expensive and tedious COVID diagnosis technique in practice. Medical imaging is feasible to diagnose COVID-19 by X-ray chest radiography to get around the shortcomings of RT-PCR. Through a variety of Deep Transfer-learning models, this research investigates the potential of Artificial Intelligence -based early diagnosis of COVID-19 via X-ray chest radiographs. With 10,192 normal and 3616 Covid X-ray chest radiographs, the deep transfer-learning models are optimized to further the accurate diagnosis. The x-ray chest radiographs undergo a data augmentation phase before developing a modified dataset to train the Deep Transfer-learning models. The Deep Transfer-learning architectures are trained using the extracted features from the Feature Extraction stage. During training, the classification of X-ray Chest radiographs based on feature extraction algorithm values is converted into a feature label set containing the classified image data with a feature string value representing the number of edges detected after edge detection. The feature label set is further tested with the SVM, KNN, NN, Naive Bayes and Logistic Regression classifiers to audit the quality metrics of the proposed model. The quality metrics include accuracy, precision, F1 score, recall and AUC. The Inception-V3 dominates the six Deep Transfer-learning models, according to the assessment results, with a training accuracy of 84.79% and a loss function of 2.4%. The performance of Cubic SVM was superior to that of the other SVM classifiers, with an AUC score of 0.99, precision of 0.983, recall of 0.8977, accuracy of 95.8%, and F1 score of 0.9384. Cosine KNN fared better than the other KNN classifiers with an AUC score of 0.95, precision of 0.974, recall of 0.777, accuracy of 90.8%, and F1 score of 0.864. Wide NN fared better than the other NN classifiers with an AUC score of 0.98, precision of 0.975, recall of 0.907, accuracy of 95.5%, and F1 score of 0.939. According to the findings, SVM classifiers topped other classifiers in terms of performance indicators like accuracy, precision, recall, F1-score, and AUC. The SVM classifiers reported better mean optimal scores compared to other classifiers. The performance assessment metrics uncover that the proposed methodology can aid in preliminary COVID diagnosis.

Poola Rahul Gowtham, Pl Lahari, Y Siva Sankar

2023-Jun

Boundary tracing, Covid diagnosis, Deep transfer-learning, Medical imaging, Neural network models and classifiers

Pathology

AMLnet, A deep-learning pipeline for the differential diagnosis of acute myeloid leukemia from bone marrow smears.

In Journal of hematology & oncology ; h5-index 60.0

Acute myeloid leukemia (AML) is a deadly hematological malignancy. Cellular morphology detection of bone marrow smears based on the French-American-British (FAB) classification system remains an essential criterion in the diagnosis of hematological malignancies. However, the diagnosis and discrimination of distinct FAB subtypes of AML obtained from bone marrow smear images are tedious and time-consuming. In addition, there is considerable variation within and among pathologists, particularly in rural areas, where pathologists may not have relevant expertise. Here, we established a comprehensive database encompassing 8245 bone marrow smear images from 651 patients based on a retrospective dual-center study between 2010 and 2021 for the purpose of training and testing. Furthermore, we developed AMLnet, a deep-learning pipeline based on bone marrow smear images, that can discriminate not only between AML patients and healthy individuals but also accurately identify various AML subtypes. AMLnet achieved an AUC of 0.885 at the image level and 0.921 at the patient level in distinguishing nine AML subtypes on the test dataset. Furthermore, AMLnet outperformed junior human experts and was comparable to senior experts on the test dataset at the patient level. Finally, we provided an interactive demo website to visualize the saliency maps and the results of AMLnet for aiding pathologists' diagnosis. Collectively, AMLnet has the potential to serve as a fast prescreening and decision support tool for cytomorphological pathologists, especially in areas where pathologists are overburdened by medical demands as well as in rural areas where medical resources are scarce.

Yu Zebin, Li Jianhu, Wen Xiang, Han Yingli, Jiang Penglei, Zhu Meng, Wang Minmin, Gao Xiangli, Shen Dan, Zhang Ting, Zhao Shuqi, Zhu Yijing, Tong Jixiang, Yuan Shuchong, Zhu HongHu, Huang He, Qian Pengxu

2023-Mar-21

Acute myeloid leukemia, Bone marrow smears, Deep learning, Diagnosis

Oncology

Serum immuno-oncology markers carry independent prognostic information in patients with newly diagnosed metastatic breast cancer, from a prospective observational study.

In Breast cancer research : BCR

BACKGROUND : Metastatic breast cancer (MBC) is a challenging disease, and despite new therapies, prognosis is still poor for a majority of patients. There is a clinical need for improved prognostication where immuno-oncology markers can provide important information. The aim of this study was to evaluate serum immuno-oncology markers in MBC patients and their respective relevance for prediction of survival.

PATIENTS AND METHODS : We investigated a broad panel of 92 immuno-oncology proteins in serum from 136 MBC patients included in a prospective observational study (NCT01322893) with long-term follow-up. Serum samples were collected before start of systemic therapy and analyzed using multiplex proximity extension assay (Olink Target 96 Immuno-Oncology panel). Multiple machine learning techniques were used to identify serum markers with highest importance for prediction of overall and progression-free survival (OS and PFS), and associations to survival were further evaluated using Cox regression analyses. False discovery rate was then used to adjust for multiple comparisons.

RESULTS : Using random forest and random survival forest analyses, we identified the top nine and ten variables of highest predictive importance for OS and PFS, respectively. Cox regression analyses revealed significant associations (P < 0.005) of higher serum levels of IL-8, IL-10 and CAIX with worse OS in multivariable analyses, adjusted for established clinical prognostic factors including circulating tumor cells (CTCs). Similarly, high serum levels of IL-8, IL-10, ADA and CASP8 significantly associated with worse PFS. Interestingly, high serum levels of FasL significantly associated with improved OS and PFS. In addition, CSF-1, IL-6, MUC16, TFNSFR4 and CD244 showed suggestive evidence (P < 0.05) for an association to survival in multivariable analyses. After correction for multiple comparisons, IL-8 still showed strong evidence for correlation to survival.

CONCLUSION : To conclude, we found six serum immuno-oncology markers that were significantly associated with OS and/or PFS in MBC patients, independently of other established prognostic factors including CTCs. Furthermore, an additional five serum immuno-oncology markers provided suggestive evidence for an independent association to survival. These findings highlight the relevance of immuno-oncology serum markers in MBC patients and support their usefulness for improved prognostication. Trial registration Clinical Trials (NCT01322893), registered March 25, 2011.

Gunnarsdottir Frida Björk, Bendahl Pär-Ola, Johansson Alexandra, Benfeitas Rui, Rydén Lisa, Bergenfelz Caroline, Larsson Anna-Maria

2023-Mar-21

Immuno-oncology, Marker, Metastatic breast cancer, Serum, Survival

Surgery

Using a New Deep Learning Method for 3D Cephalometry in Patients With Cleft Lip and Palate.

In The Journal of craniofacial surgery

Deep learning algorithms based on automatic 3-dimensional (D) cephalometric marking points about people without craniomaxillofacial deformities has achieved good results. However, there has been no previous report about cleft lip and palate. The purpose of this study is to apply a new deep learning method based on a 3D point cloud graph convolutional neural network to predict and locate landmarks in patients with cleft lip and palate based on the relationships between points. The authors used the PointNet++ model to investigate the automatic 3D cephalometric marking points. And the mean distance error of the center coordinate position and the success detection rate (SDR) were used to evaluate the accuracy of systematic labeling. A total of 150 patients were enrolled. The mean distance error for all 27 landmarks was 1.33 mm, and 9 landmarks (30%) showed SDRs at 2 mm over 90%, and 3 landmarks (35%) showed SDRs at 2 mm under 70%. The automatic 3D cephalometric marking points take 16 seconds per dataset. In summary, our training sets were derived from the cleft lip with/without palate computed tomography to achieve accurate results. The 3D cephalometry system based on the graph convolutional neural network algorithm may be suitable for 3D cephalometry system in cleft lip and palate cases. More accurate results may be obtained if the cleft lip and palate training set is expanded in the future.

Xu Meng, Liu Bingyang, Luo Zhaoyang, Ma Hengyuan, Sun Min, Wang Yongqian, Yin Ningbei, Tang Xiaojun, Song Tao

2023-Mar-22

Radiology

Impact of inactivated COVID-19 vaccines on lung injury in B.1.617.2 (Delta) variant-infected patients.

In Annals of clinical microbiology and antimicrobials

BACKGROUND : Chest computerized tomography (CT) scan is an important strategy that quantifies the severity of COVID-19 pneumonia. To what extent inactivated COVID-19 vaccines could impact the COVID-19 pneumonia on chest CT is not clear.

METHODS : This study recruited 357 SARS-COV-2 B.1.617.2 (Delta) variant-infected patients admitted to the Second Hospital of Nanjing from July to August 2021. An artificial intelligence-assisted CT imaging system was used to quantify the severity of COVID-19 pneumonia. We compared the volume of infection (VOI), percentage of infection (POI) and chest CT scores among patients with different vaccination statuses.

RESULTS : Of the 357 Delta variant-infected patients included for analysis, 105 were unvaccinated, 72 were partially vaccinated and 180 were fully vaccinated. Fully vaccination had the least lung injuries when quantified by VOI (median VOI of 222.4 cm³, 126.6 cm³ and 39.9 cm³ in unvaccinated, partially vaccinated and fully vaccinated, respectively; p < 0.001), POI (median POI of 7.60%, 3.55% and 1.20% in unvaccinated, partially vaccinated and fully vaccinated, respectively; p < 0.001) and chest CT scores (median CT score of 8.00, 6.00 and 4.00 in unvaccinated, partially vaccinated and fully vaccinated, respectively; p < 0.001). After adjustment for age, sex, comorbidity, time from illness onset to hospitalization and viral load, fully vaccination but not partial vaccination was significantly associated with less lung injuries quantified by VOI {adjust coefficient[95%CI] for "full vaccination": - 106.10(- 167.30,44.89); p < 0.001}, POI {adjust coefficient[95%CI] for "full vaccination": - 3.88(- 5.96, - 1.79); p = 0.001} and chest CT scores {adjust coefficient[95%CI] for "full vaccination": - 1.81(- 2.72, - 0.91); p < 0.001}. The extent of reduction of pulmonary injuries was more profound in fully vaccinated patients with older age, having underlying diseases, and being female sex, as demonstrated by relatively larger absolute values of adjusted coefficients. Finally, even within the non-severe COVID-19 population, fully vaccinated patients were found to have less lung injuries.

CONCLUSION : Fully vaccination but not partially vaccination could significantly protect lung injury manifested on chest CT. Our study provides additional evidence to encourage a full course of vaccination.

Lai Miao, Wang Kai, Ding Chengyuan, Yin Yi, Lin Xiaoling, Xu Chuanjun, Hu Zhiliang, Peng Zhihang

2023-Mar-21

Artificial intelligence (AI), COVID-19, COVID-19 vaccines, Chest CT, Lung injury

Radiology

Compositional Zero-Shot Domain Transfer with Text-to-Text Models

ArXiv Preprint

Label scarcity is a bottleneck for improving task performance in specialised domains. We propose a novel compositional transfer learning framework (DoT5 - domain compositional zero-shot T5) for zero-shot domain transfer. Without access to in-domain labels, DoT5 jointly learns domain knowledge (from MLM of unlabelled in-domain free text) and task knowledge (from task training on more readily available general-domain data) in a multi-task manner. To improve the transferability of task training, we design a strategy named NLGU: we simultaneously train NLG for in-domain label-to-data generation which enables data augmentation for self-finetuning and NLU for label prediction. We evaluate DoT5 on the biomedical domain and the resource-lean subdomain of radiology, focusing on NLI, text summarisation and embedding learning. DoT5 demonstrates the effectiveness of compositional transfer learning through multi-task learning. In particular, DoT5 outperforms the current SOTA in zero-shot transfer by over 7 absolute points in accuracy on RadNLI. We validate DoT5 with ablations and a case study demonstrating its ability to solve challenging NLI examples requiring in-domain expertise.

Fangyu Liu, Qianchu Liu, Shruthi Bannur, Fernando Pérez-García, Naoto Usuyama, Sheng Zhang, Tristan Naumann, Aditya Nori, Hoifung Poon, Javier Alvarez-Valle, Ozan Oktay, Stephanie L. Hyland

2023-03-23

General

Fault Prognosis of Turbofan Engines: Eventual Failure Prediction and Remaining Useful Life Estimation

ArXiv Preprint

In the era of industrial big data, prognostics and health management is essential to improve the prediction of future failures to minimize inventory, maintenance, and human costs. Used for the 2021 PHM Data Challenge, the new Commercial Modular Aero-Propulsion System Simulation dataset from NASA is an open-source benchmark containing simulated turbofan engine units flown under realistic flight conditions. Deep learning approaches implemented previously for this application attempt to predict the remaining useful life of the engine units, but have not utilized labeled failure mode information, impeding practical usage and explainability. To address these limitations, a new prognostics approach is formulated with a customized loss function to simultaneously predict the current health state, the eventual failing component(s), and the remaining useful life. The proposed method incorporates principal component analysis to orthogonalize statistical time-domain features, which are inputs into supervised regressors such as random forests, extreme random forests, XGBoost, and artificial neural networks. The highest performing algorithm, ANN-Flux, achieves AUROC and AUPR scores exceeding 0.95 for each classification. In addition, ANN-Flux reduces the remaining useful life RMSE by 38% for the same test split of the dataset compared to past work, with significantly less computational cost.

Joseph Cohen, Xun Huan, Jun Ni

2023-03-23

Radiology

Mesoporous nanodrug delivery system: a powerful tool for a new paradigm of remodeling of the tumor microenvironment.

In Journal of nanobiotechnology

Tumor microenvironment (TME) plays an important role in tumor progression, metastasis and therapy resistance. Remodeling the TME has recently been deemed an attractive tumor therapeutic strategy. Due to its complexity and heterogeneity, remodeling the TME still faces great challenges. With the great advantage of drug loading ability, tumor accumulation, multifactor controllability, and persistent guest molecule release ability, mesoporous nanodrug delivery systems (MNDDSs) have been widely used as effective antitumor drug delivery tools as well as remolding TME. This review summarizes the components and characteristics of the TME, as well as the crosstalk between the TME and cancer cells and focuses on the important role of drug delivery strategies based on MNDDSs in targeted remodeling TME metabolic and synergistic anticancer therapy.

Hang Yinhui, Liu Yanfang, Teng Zhaogang, Cao Xiongfeng, Zhu Haitao

2023-Mar-21

Mesoporous nanodrug delivery systems, Metabolism, Targeted remodeling, Tumor microenvironment, Tumor therapy

Surgery

Radiomics nomogram for preoperative differentiation of pulmonary mucinous adenocarcinoma from tuberculoma in solitary pulmonary solid nodules.

In BMC cancer

OBJECTIVE : To develop and validate predictive models using clinical parameters, radiomic features and a combination of both for preoperative differentiation of pulmonary nodular mucinous adenocarcinoma (PNMA) from pulmonary tuberculoma (PTB).

METHOD : A total of 124 and 53 patients with PNMA and PTB, respectively, were retrospectively analyzed from January 2017 to November 2022 in The Fourth Affiliated Hospital of Hebei Medical University (Ligang et al., A machine learning model based on CT and clinical features to distinguish pulmonary nodular mucinous adenocarcinoma from tuberculoma, 2023). A total of 1037 radiomic features were extracted from contrast-enhanced computed tomography (CT). The patients were randomly divided into a training group and a test group at a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) algorithm was used for radiomic feature selection. Three radiomics prediction models were applied: logistic regression (LR), support vector machine (SVM) and random forest (RF). The best performing model was adopted, and the radiomics score (Radscore) was then computed. The clinical model was developed using logistic regression. Finally, a combined model was established based on clinical factors and radiomics features. We externally validated the three models in a group of 68 patients (46 and 22 patients with PNMA and PTB, respectively) from Xing Tai People's Hospital (30 and 14 patients with PNMA and PTB, respectively) and The First Hospital of Xing Tai (16 and 8 patients with PNMA and PTB, respectively). The area under the receiver operating characteristic (ROC) curve (AUC) value and decision curve analysis were used to evaluate the predictive value of the developed models.

RESULTS : The combined model established by the logistic regression method had the best performance. The ROC-AUC (also a decision curve analysis) of the combined model was 0.940, 0.990 and 0.960 in the training group, test group and external validation group, respectively, and the combined model showed good predictive performance for the differentiation of PNMA from PTB. The Brier scores of the combined model were 0.132 and 0.068 in the training group and test group, respectively.

CONCLUSION : The combined model incorporating radiomics features and clinical parameters may have potential value for the preoperative differentiation of PNMA from PTB.

Zhang Junjie, Hao Ligang, Qi MingWei, Xu Qian, Zhang Ning, Feng Hui, Shi Gaofeng

2023-Mar-21

Contrast-enhanced CT, Mucinous adenocarcinoma, Nomogram, Radiomics, Tuberculoma

Radiology

Impact of inactivated COVID-19 vaccines on lung injury in B.1.617.2 (Delta) variant-infected patients.

In Annals of clinical microbiology and antimicrobials

BACKGROUND : Chest computerized tomography (CT) scan is an important strategy that quantifies the severity of COVID-19 pneumonia. To what extent inactivated COVID-19 vaccines could impact the COVID-19 pneumonia on chest CT is not clear.

METHODS : This study recruited 357 SARS-COV-2 B.1.617.2 (Delta) variant-infected patients admitted to the Second Hospital of Nanjing from July to August 2021. An artificial intelligence-assisted CT imaging system was used to quantify the severity of COVID-19 pneumonia. We compared the volume of infection (VOI), percentage of infection (POI) and chest CT scores among patients with different vaccination statuses.

RESULTS : Of the 357 Delta variant-infected patients included for analysis, 105 were unvaccinated, 72 were partially vaccinated and 180 were fully vaccinated. Fully vaccination had the least lung injuries when quantified by VOI (median VOI of 222.4 cm³, 126.6 cm³ and 39.9 cm³ in unvaccinated, partially vaccinated and fully vaccinated, respectively; p < 0.001), POI (median POI of 7.60%, 3.55% and 1.20% in unvaccinated, partially vaccinated and fully vaccinated, respectively; p < 0.001) and chest CT scores (median CT score of 8.00, 6.00 and 4.00 in unvaccinated, partially vaccinated and fully vaccinated, respectively; p < 0.001). After adjustment for age, sex, comorbidity, time from illness onset to hospitalization and viral load, fully vaccination but not partial vaccination was significantly associated with less lung injuries quantified by VOI {adjust coefficient[95%CI] for "full vaccination": - 106.10(- 167.30,44.89); p < 0.001}, POI {adjust coefficient[95%CI] for "full vaccination": - 3.88(- 5.96, - 1.79); p = 0.001} and chest CT scores {adjust coefficient[95%CI] for "full vaccination": - 1.81(- 2.72, - 0.91); p < 0.001}. The extent of reduction of pulmonary injuries was more profound in fully vaccinated patients with older age, having underlying diseases, and being female sex, as demonstrated by relatively larger absolute values of adjusted coefficients. Finally, even within the non-severe COVID-19 population, fully vaccinated patients were found to have less lung injuries.

CONCLUSION : Fully vaccination but not partially vaccination could significantly protect lung injury manifested on chest CT. Our study provides additional evidence to encourage a full course of vaccination.

Lai Miao, Wang Kai, Ding Chengyuan, Yin Yi, Lin Xiaoling, Xu Chuanjun, Hu Zhiliang, Peng Zhihang

2023-Mar-21

Artificial intelligence (AI), COVID-19, COVID-19 vaccines, Chest CT, Lung injury

General

PWN: enhanced random walk on a warped network for disease target prioritization.

In BMC bioinformatics

BACKGROUND : Extracting meaningful information from unbiased high-throughput data has been a challenge in diverse areas. Specifically, in the early stages of drug discovery, a considerable amount of data was generated to understand disease biology when identifying disease targets. Several random walk-based approaches have been applied to solve this problem, but they still have limitations. Therefore, we suggest a new method that enhances the effectiveness of high-throughput data analysis with random walks.

RESULTS : We developed a new random walk-based algorithm named prioritization with a warped network (PWN), which employs a warped network to achieve enhanced performance. Network warping is based on both internal and external features: graph curvature and prior knowledge.

CONCLUSIONS : We showed that these compositive features synergistically increased the resulting performance when applied to random walk algorithms, which led to PWN consistently achieving the best performance among several other known methods. Furthermore, we performed subsequent experiments to analyze the characteristics of PWN.

Han Seokjin, Hong Jinhee, Yun So Jeong, Koo Hee Jung, Kim Tae Yong

2023-Mar-21

Disease-target identification, Machine learning, Protein–protein interaction, Random walk

Surgery

TRandAugment: temporal random augmentation strategy for surgical activity recognition from videos.

In International journal of computer assisted radiology and surgery

PURPOSE : Automatic recognition of surgical activities from intraoperative surgical videos is crucial for developing intelligent support systems for computer-assisted interventions. Current state-of-the-art recognition methods are based on deep learning where data augmentation has shown the potential to improve the generalization of these methods. This has spurred work on automated and simplified augmentation strategies for image classification and object detection on datasets of still images. Extending such augmentation methods to videos is not straightforward, as the temporal dimension needs to be considered. Furthermore, surgical videos pose additional challenges as they are composed of multiple, interconnected, and long-duration activities.

METHODS : This work proposes a new simplified augmentation method, called TRandAugment, specifically designed for long surgical videos, that treats each video as an assemble of temporal segments and applies consistent but random transformations to each segment. The proposed augmentation method is used to train an end-to-end spatiotemporal model consisting of a CNN (ResNet50) followed by a TCN.

RESULTS : The effectiveness of the proposed method is demonstrated on two surgical video datasets, namely Bypass40 and CATARACTS, and two tasks, surgical phase and step recognition. TRandAugment adds a performance boost of 1-6% over previous state-of-the-art methods, that uses manually designed augmentations.

CONCLUSION : This work presents a simplified and automated augmentation method for long surgical videos. The proposed method has been validated on different datasets and tasks indicating the importance of devising temporal augmentation methods for long surgical videos.

Ramesh Sanat, Dall’Alba Diego, Gonzalez Cristians, Yu Tong, Mascagni Pietro, Mutter Didier, Marescaux Jacques, Fiorini Paolo, Padoy Nicolas

2023-Mar-22

Cataract procedures, Data augmentation, Gastric bypass procedures, Surgical activity recognition, Temporal augmentation, Temporal convolutional networks

General

A new uncertain remanufacturing scheduling model with rework risk using hybrid optimization algorithm.

In Environmental science and pollution research international

As a resource-conserving and environmentally friendly manufacturing paradigm, remanufacturing with the potential to realize sustainability in production has been extensively investigated. Scheduling plays a significant role in achieving the remanufacturing benefits. However, the remanufacturing process involves intricate uncertainties because it takes end-of-life products with different qualities as workblanks, which increases the risk of rework and complicates remanufacturing scheduling. Though the traditional stochastic optimization methods or fuzzy theory have been employed to address uncertainties in the remanufacturing scheduling problem, they are constrained with the limited historical data which renders it difficult to describe uncertainties accurately and intuitively. Therefore, a new uncertain remanufacturing scheduling model with rework risk is proposed, in which the interval grey numbers are applied to describe the uncertainty clearly and consider the rework risk in remanufacturing process. To solve this model, a hybrid optimization algorithm that combines differential evolution and particle swarm optimization algorithms through an efficient representation scheme is proposed. Besides, this algorithm integrates multiple improvements to maintain the diversity of the population and enhance its performance. Simulation experiments are conducted on 18 sets of instances with different scales, and the results demonstrated that the proposed algorithm obtains a better optimal solution than other baseline algorithms on 17 sets of instances. The main finding of this study is providing a new method for solving uncertain remanufacturing scheduling problem with rework risk practically and effectively.

Zhang Wenyu, Wang Jun, Liu Xiangqi, Zhang Shuai

2023-Mar-22

Differential evolution algorithm, Interval grey number, Particle swarm optimization algorithm, Remanufacturing scheduling, Rework risk

Radiology

Application of Deep Learning-Based Denoising Technique for Radiation Dose Reduction in Dynamic Abdominal CT: Comparison with Standard-Dose CT Using Hybrid Iterative Reconstruction Method.

In Journal of digital imaging

The purpose is to evaluate whether deep learning-based denoising (DLD) algorithm provides sufficient image quality for abdominal computed tomography (CT) with a 30% reduction in radiation dose, compared to standard-dose CT reconstructed with conventional hybrid iterative reconstruction (IR). The subjects consisted of 50 patients who underwent abdominal CT with standard dose and reconstructed with hybrid IR (ASiR-V50%) and another 50 patients who underwent abdominal CT with approximately 30% less dose and reconstructed with ASiR-V50% and DLD at low-, medium- and high-strength (DLD-L, DLD-M and DLD-H, respectively). The standard deviation of attenuation in liver parenchyma was measured as image noise. Contrast-to-noise ratio (CNR) for portal vein on portal venous phase was calculated. Lesion conspicuity in 23 abdominal solid mass on the reduced-dose CT was rated on a 5-point scale: 0 (best) to -4 (markedly inferior). Compared with hybrid IR of standard-dose CT, DLD-H of reduced-dose CT provided significantly lower image noise (portal phase: 9.0 (interquartile range, 8.7-9.4) HU vs 12.0 (11.4-12.7) HU, P < 0.0001) and significantly higher CNR (median, 5.8 (4.4-7.4) vs 4.3 (3.3-5.3), P = 0.0019). As for DLD-M of reduced-dose CT, no significant difference was found in image noise and CNR compared to hybrid IR of standard-dose CT (P > 0.99). Lesion conspicuity scores for DLD-H and DLD-M were significantly better than hybrid IR (P < 0.05). Dynamic contrast-enhanced abdominal CT acquired with approximately 30% lower radiation dose and generated with the DLD algorithm exhibit lower image noise and higher CNR compared to standard-dose CT with hybrid IR.

Nagata Motonori, Ichikawa Yasutaka, Domae Kensuke, Yoshikawa Kazuya, Kanii Yoshinori, Yamazaki Akio, Nagasawa Naoki, Ishida Masaki, Sakuma Hajime

2023-Mar-21

Abdomen, Computed tomography, Deep learning, Image reconstruction, Radiation dose reduction

General

Distributed dynamic strain sensing of very long period and long period events on telecom fiber-optic cables at Vulcano, Italy.

In Scientific reports ; h5-index 158.0

Volcano-seismic signals can help for volcanic hazard estimation and eruption forecasting. However, the underlying mechanism for their low frequency components is still a matter of debate. Here, we show signatures of dynamic strain records from Distributed Acoustic Sensing in the low frequencies of volcanic signals at Vulcano Island, Italy. Signs of unrest have been observed since September 2021, with CO₂ degassing and occurrence of long period and very long period events. We interrogated a fiber-optic telecommunication cable on-shore and off-shore linking Vulcano Island to Sicily. We explore various approaches to automatically detect seismo-volcanic events both adapting conventional algorithms and using machine learning techniques. During one month of acquisition, we found 1488 events with a great variety of waveforms composed of two main frequency bands (from 0.1 to 0.2 Hz and from 3 to 5 Hz) with various relative amplitudes. On the basis of spectral signature and family classification, we propose a model in which gas accumulates in the hydrothermal system and is released through a series of resonating fractures until the surface. Our findings demonstrate that fiber optic telecom cables in association with cutting-edge machine learning algorithms contribute to a better understanding and monitoring of volcanic hydrothermal systems.

Currenti Gilda, Allegra Martina, Cannavò Flavio, Jousset Philippe, Prestifilippo Michele, Napoli Rosalba, Sciotto Mariangela, Di Grazia Giuseppe, Privitera Eugenio, Palazzo Simone, Krawczyk Charlotte

2023-Mar-21

Oncology

DeepGeni: deep generalized interpretable autoencoder elucidates gut microbiota for better cancer immunotherapy.

In Scientific reports ; h5-index 158.0

Recent studies revealed that gut microbiota modulates the response to cancer immunotherapy and fecal microbiota transplantation has clinical benefits in melanoma patients during treatment. Understanding how microbiota affects individual responses is crucial for precision oncology. However, it is challenging to identify key microbial taxa with limited data as statistical and machine learning models often lose their generalizability. In this study, DeepGeni, a deep generalized interpretable autoencoder, is proposed to improve the generalizability and interpretability of microbiome profiles by augmenting data and by introducing interpretable links in the autoencoder. DeepGeni-based machine learning classifier outperforms state-of-the-art classifier in the microbiome-driven prediction of responsiveness of melanoma patients treated with immune checkpoint inhibitors. Moreover, the interpretable links of DeepGeni elucidate the most informative microbiota associated with cancer immunotherapy response. DeepGeni not only improves microbiome-driven prediction of immune checkpoint inhibitor responsiveness but also suggests potential microbial targets for fecal microbiota transplant or probiotics improving the outcome of cancer immunotherapy.

Oh Min, Zhang Liqing

2023-Mar-21

Radiology

Ultra-low-dose CT lung screening with artificial intelligence iterative reconstruction: evaluation via automatic nodule-detection software.

In Clinical radiology

AIM : To test the feasibility of ultra-low-dose (ULD) computed tomography (CT) combined with an artificial intelligence iterative reconstruction (AIIR) algorithm for screening pulmonary nodules using computer-assisted diagnosis (CAD).

MATERIALS AND METHODS : A chest phantom with artificial pulmonary nodules was first scanned using the routine protocol and the ULD protocol (3.28 versus 0.18 mSv) to compare the image quality and to test the acceptability of the ULD CT protocol. Next, 147 lung-screening patients were enrolled prospectively, undergoing an additional ULD CT immediately after their routine CT examination for clinical validation. Images were reconstructed with filtered back-projection (FBP), hybrid iterative reconstruction (HIR), the AIIR, and were imported to the CAD software for preliminary nodule detection. Subjective image quality on the phantom was scored using a five-point scale and compared using the Mann-Whitney U-test. Nodule detection using CAD was evaluated for ULD HIR and AIIR images using the routine dose image as reference.

RESULTS : Higher image quality was scored for AIIR than for FBP and HIR at ULD (p<0.001). As reported by CAD, 107 patients were presented with more than five nodules on routine dose images and were chosen to represent the challenging cases at an early stage of pulmonary disease. Among such, the performance of nodule detection by CAD on ULD HIR and AIIR images was 75.2% and 92.2% of the routine dose image, respectively.

CONCLUSION : Combined with AIIR, it was feasible to use an ULD CT protocol with 95% dose reduction for CAD-based screening of pulmonary nodules.

Yang L, Liu H, Han J, Xu S, Zhang G, Wang Q, Du Y, Yang F, Zhao X, Shi G

2023-Feb-03

Surgery

Using the Field Artificial Intelligence Triage (FAIT) tool to predict hospital critical care resource utilization in patients with truncal gunshot wounds.

In American journal of surgery

BACKGROUND : Tiered trauma triage systems have resulted in a significant mortality reduction, but models have remained unchanged. The aim of this study was to develop and test an artificial intelligence algorithm to predict critical care resource utilization.

METHODS : We queried the ACS-TQIP 2017-18 database for truncal gunshot wounds(GSW). An information-aware deep neural network (DNN-IAD) model was trained to predict ICU admission and need for mechanical ventilation (MV). Input variables included demographics, comorbidities, vital signs, and external injuries. The model's performance was assessed using the area under receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC).

RESULTS : For the ICU admission analysis, we included 39,916 patients. For the MV need analysis, 39,591 patients were included. Median (IQR) age was 27 (22,36). AUROC and AUPRC for predicting ICU need were 84.8 ± 0.5 and 75.4 ± 0.5, and the AUROC and AUPRC for MV need were 86.8 ± 0.5 and 72.5 ± 0.6.

CONCLUSIONS : Our model predicts hospital utilization outcomes in patients with truncal GSW with high accuracy, allowing early resource mobilization and rapid triage decisions in hospitals with capacity issues and austere environments.

Alser Osaid, Dorken-Gallastegi Ander, Proaño-Zamudio Jefferson A, Nederpelt Charlie, Mokhtari Ava K, Mashbari Hassan, Tsiligkaridis Theodoros, Saillant Noelle N

2023-Mar-17

AI, Gunshot, Machine learning, Resource utilization, Triage

General

High-performance pediatric surgical risk calculator: A novel algorithm based on machine learning and pediatric NSQIP data.

In American journal of surgery

BACKGROUNDS : New methods such as machine learning could provide accurate predictions with little statistical assumptions. We seek to develop prediction model of pediatric surgical complications based on pediatric National Surgical Quality Improvement Program(NSQIP).

METHODS : All 2012-2018 pediatric-NSQIP procedures were reviewed. Primary outcome was defined as 30-day post-operative morbidity/mortality. Morbidity was further classified as any, major and minor. Models were developed using 2012-2017 data. 2018 data was used as independent performance evaluation.

RESULTS : 431,148 patients were included in the 2012-2017 training and 108,604 were included in the 2018 testing set. Our prediction models had high performance in mortality prediction at 0.94 AUC in testing set. Our models outperformed ACS-NSQIP Calculator in all categories for morbidity (0.90 AUC for major, 0.86 AUC for any, 0.69 AUC in minor complications).

CONCLUSIONS : We developed a high-performing pediatric surgical risk prediction model. This powerful tool could potentially be used to improve the surgical care quality.

Bertsimas Dimitris, Li Michael, Zhang Nova, Estrada Carlos, Scott Wang Hsin-Hsiao

2023-Mar-13

Machine learning, Pediatric surgical risk, Personalized care, Prediction model

Pathology

Artificial Intelligence in Pediatric Endoscopy: Current Status and Future Applications.

In Gastrointestinal endoscopy clinics of North America

The application of artificial intelligence (AI) has great promise for improving pediatric endoscopy. The majority of preclinical studies have been undertaken in adults, with the greatest progress being made in the context of colorectal cancer screening and surveillance. This development has only been possible with advances in deep learning, like the convolutional neural network model, which has enabled real-time detection of pathology. Comparatively, the majority of deep learning systems developed in inflammatory bowel disease have focused on predicting disease severity and were developed using still images rather than videos. The application of AI to pediatric endoscopy is in its infancy, thus providing an opportunity to develop clinically meaningful and fair systems that do not perpetuate societal biases. In this review, we provide an overview of AI, summarize the advances of AI in endoscopy, and describe its potential application to pediatric endoscopic practice and education.

Dhaliwal Jasbir, Walsh Catharine M

2023-Apr

Artificial intelligence, Artificial neural networks, CADe, CADx, Computer-aided diagnosis, Convolutional neural network, Deep learning, Pediatric gastrointestinal endoscopy

Surgery

Interpretation and Use of Applied/Operational Machine Learning and Artificial Intelligence in Surgery.

In The Surgical clinics of North America

Applications for artificial intelligence (AI) and machine learning in surgery include image interpretation, data summarization, automated narrative construction, trajectory and risk prediction, and operative navigation and robotics. The pace of development has been exponential, and some AI applications are working well. However, demonstrations of clinical utility, validity, and equity have lagged algorithm development and limited widespread adoption of AI into clinical practice. Outdated computing infrastructure and regulatory challenges which promote data silos are key barriers. Multidisciplinary teams will be needed to address these challenges and to build AI systems that are relevant, equitable, and dynamic.

Douglas Molly J, Callcut Rachel, Celi Leo Anthony, Merchant Nirav

2023-Apr

Artificial intelligence (AI), Augmented reality (AR), Computer vision, Computer-aided diagnosis, Deep learning, Machine learning (ML), Prediction, Surgery

Surgery

Machine Learning and Artificial Intelligence in Surgical Research.

In The Surgical clinics of North America

Machine learning, a subtype of artificial intelligence, is an emerging field of surgical research dedicated to predictive modeling. From its inception, machine learning has been of interest in medical and surgical research. Built on traditional research metrics for optimal success, avenues of research include diagnostics, prognosis, operative timing, and surgical education, in a variety of surgical subspecialties. Machine learning represents an exciting and developing future in the world of surgical research that will not only allow for more personalized and comprehensive medical care.

Srinivas Shruthi, Young Andrew J

2023-Apr

Artificial intelligence, Machine learning, Surgical research

Surgery

Generation Learning Differences in Surgery: Why They Exist, Implication, and Future Directions.

In The Surgical clinics of North America

The evolution of the knowledge economy and technology industry have fundamentally changed the learning environments occupied by contemporary surgical trainees and created pressures that will force the surgical community to consider. Although some learning differences are intrinsic to the generations themselves, these differences are primarily a function of the environments in which surgeons of different generations trained. Acknowledgment of the principles of connectivism and thoughtful integration of artificial intelligence and computerized decision support tools must play a central role in charting the future course of surgical education.

Weykamp Mike, Bingham Jason

2023-Apr

Artificial intelligence, Asynchronous learning, Connectivism, Generational learning, Learning disparities, Learning theory, Machine learning, Medical education

General

Dynamic 3D imaging of cerebral blood flow in awake mice using self-supervised-learning-enhanced optical coherence Doppler tomography.

In Communications biology

Cerebral blood flow (CBF) is widely used to assess brain function. However, most preclinical CBF studies have been performed under anesthesia, which confounds findings. High spatiotemporal-resolution CBF imaging of awake animals is challenging due to motion artifacts and background noise, particularly for Doppler-based flow imaging. Here, we report ultrahigh-resolution optical coherence Doppler tomography (µODT) for 3D imaging of CBF velocity (CBFv) dynamics in awake mice by developing self-supervised deep-learning for effective image denoising and motion-artifact removal. We compare cortical CBFv in awake vs. anesthetized mice and their dynamic responses in arteriolar, venular and capillary networks to acute cocaine (1 mg/kg, i.v.), a highly addictive drug associated with neurovascular toxicity. Compared with awake, isoflurane (2-2.5%) induces vasodilation and increases CBFv within 2-4 min, whereas dexmedetomidine (0.025 mg/kg, i.p.) does not change vessel diameters nor flow. Acute cocaine decreases CBFv to the same extent in dexmedetomidine and awake states, whereas decreases are larger under isoflurane, suggesting that isoflurane-induced vasodilation might have facilitated detection of cocaine-induced vasoconstriction. Awake mice after chronic cocaine show severe vasoconstriction, CBFv decreases and vascular adaptations with extended diving arteriolar/venular vessels that prioritize blood supply to deeper cortical capillaries. The 3D imaging platform we present provides a powerful tool to study dynamic changes in vessel diameters and morphology alongside CBFv networks in the brain of awake animals that can advance our understanding of the effects of drugs and disease conditions (ischemia, tumors, wound healing).

Pan Yingtian, Park Kicheon, Ren Jiaxiang, Volkow Nora D, Ling Haibin, Koretsky Alan P, Du Congwu

2023-Mar-21

Pathology

Medical diffusion on a budget: textual inversion for medical image generation

ArXiv Preprint

Diffusion-based models for text-to-image generation have gained immense popularity due to recent advancements in efficiency, accessibility, and quality. Although it is becoming increasingly feasible to perform inference with these systems using consumer-grade GPUs, training them from scratch still requires access to large datasets and significant computational resources. In the case of medical image generation, the availability of large, publicly accessible datasets that include text reports is limited due to legal and ethical concerns. While training a diffusion model on a private dataset may address this issue, it is not always feasible for institutions lacking the necessary computational resources. This work demonstrates that pre-trained Stable Diffusion models, originally trained on natural images, can be adapted to various medical imaging modalities by training text embeddings with textual inversion. In this study, we conducted experiments using medical datasets comprising only 100 samples from three medical modalities. Embeddings were trained in a matter of hours, while still retaining diagnostic relevance in image generation. Experiments were designed to achieve several objectives. Firstly, we fine-tuned the training and inference processes of textual inversion, revealing that larger embeddings and more examples are required. Secondly, we validated our approach by demonstrating a 2\% increase in the diagnostic accuracy (AUC) for detecting prostate cancer on MRI, which is a challenging multi-modal imaging modality, from 0.78 to 0.80. Thirdly, we performed simulations by interpolating between healthy and diseased states, combining multiple pathologies, and inpainting to show embedding flexibility and control of disease appearance. Finally, the embeddings trained in this study are small (less than 1 MB), which facilitates easy sharing of medical data with reduced privacy concerns.

Bram de Wilde, Anindo Saha, Richard P. G. ten Broek, Henkjan Huisman

2023-03-23

Surgery

Big Data in Surgery.

In The Surgical clinics of North America

The emergence of Big Data has been facilitated by technological advancements in the processing, storage, and analysis of large quantities of data. Its strength is derived from its size, ease of access, and speed of analysis, and it has enabled surgeons to investigate areas of interest that traditional research models have historically been unable to address. In the future, Big Data will likely assist in the incorporation of more advanced technologies into surgical practice, including artificial intelligence and machine learning to realize the full potential of Big Data in Surgery.

Prien Christopher, Lincango Eddy P, Holubar Stefan D

2023-Apr

Artificial intelligence, Big data, Machine learning, Natural language processing, Quality, Registries, Surgery, outcomes research

Radiology

Radiologist's Disease: Imaging for Renal Cancer.

In The Urologic clinics of North America

There is a clear benefit of imaging-based differentiation of small indeterminate masses to its subtypes of clear cell renal cell carcinoma (RCC), chromophobe RCC, papillary RCC, fat poor angiomyolipoma and oncocytoma because it helps determine the next step options for the patients. The work thus far in radiology has explored different parameters in computed tomography, MRI, and contrast-enhanced ultrasound with the discovery of many reliable imaging features that suggest certain tissue subtypes. Likert score-based risk stratification systems can help determine management, and new techniques such as perfusion, radiogenomics, single-photon emission tomography, and artificial intelligence can add to the imaging-based evaluation of indeterminate renal masses.

Chung Alex, Raman Steven S

2023-May

CT, MRI, Multiphasic contrast imaging, Ultrasound

General

[Estimation of Shooting Part Using a Camera with Depth Sensors and Pose Estimation Method and Automatic Setting of Optimal X-ray Imaging Conditions].

In Nihon Hoshasen Gijutsu Gakkai zasshi

PURPOSE : In this study, we propose a system that combines a depth camera with a deep learning model for estimating the human skeleton and a depth camera to estimate the shooting part to be radiographed and to acquire the thickness of the subject, thereby providing optimized X-ray imaging conditions.

METHODS : We propose a system that provides optimized X-ray imaging conditions by estimating the shooting part and measuring the thickness of the subject using an RGB camera and a depth camera. The system uses OpenPose, a posture estimation library, to estimate the shooting part.

RESULTS : The recognition rate of the shooting part was 15.38% for the depth camera and 84.62% for the RGB camera at a distance of 100 cm, and 42.31% for the depth camera and 100% for the RGB camera at a distance of 120 cm. The measurement accuracy of the subject thickness was within ±10 mm except for a few cases, indicating that the X-ray imaging conditions were optimized for the subject thickness.

CONCLUSION : The implementation of this system in an X-ray system is expected to enable automatic setting of X-ray imaging conditions. The system is also useful in preventing increased exposure dose due to excessive dose or decreased image quality due to insufficient dose caused by incorrect setting of X-ray imaging conditions.

Eto Michihiro, Nakawatari Tomofumi, Hatanaka Yuji

2023-Mar-23

X-ray image, depth camera, dose management, exposure dose, pose estimation

Public Health

Priorities for Artificial Intelligence Applications in Primary Care: A Canadian Deliberative Dialogue with Patients, Providers, and Health System Leaders.

In Journal of the American Board of Family Medicine : JABFM

BACKGROUND : Artificial intelligence (AI) implementation in primary care is limited. Those set to be most impacted by AI technology in this setting should guide it's application. We organized a national deliberative dialogue with primary care stakeholders from across Canada to explore how they thought AI should be applied in primary care.

METHODS : We conducted 12 virtual deliberative dialogues with participants from 8 Canadian provinces to identify shared priorities for applying AI in primary care. Dialogue data were thematically analyzed using interpretive description approaches.

RESULTS : Participants thought that AI should first be applied to documentation, practice operations, and triage tasks, in hopes of improving efficiency while maintaining person-centered delivery, relationships, and access. They viewed complex AI-driven clinical decision support and proactive care tools as impactful but recognized potential risks. Appropriate training and implementation support were the most important external enablers of safe, effective, and patient-centered use of AI in primary care settings.

INTERPRETATION : Our findings offer an agenda for the future application of AI in primary care grounded in the shared values of patients and providers. We propose that, from conception, AI developers work with primary care stakeholders as codesign partners, developing tools that respond to shared priorities.

Upshaw Tara L, Craig-Neil Amy, Macklin Jillian, Gray Carolyn Steele, Chan Timothy C Y, Gibson Jennifer, Pinto Andrew D

2023-Mar-22

Artificial Intelligence, Canada, Family Medicine, Qualitative Research

Public Health

Perceptions of Artificial Intelligence Use in Primary Care: A Qualitative Study with Providers and Staff of Ontario Community Health Centres.

In Journal of the American Board of Family Medicine : JABFM

PURPOSE : To understand staff and health care providers' views on potential use of artificial intelligence (AI)-driven tools to help care for patients within a primary care setting.

METHODS : We conducted a qualitative descriptive study using individual semistructured interviews. As part of province-wide Learning Health Organization, Community Health Centres (CHCs) are a community-governed, team-based delivery model providing primary care for people who experience marginalization in Ontario, Canada. CHC health care providers and staff were invited to participate. Interviews were audio-recorded and transcribed verbatim. We performed a thematic analysis using a team approach.

RESULTS : We interviewed 27 participants across 6 CHCs. Participants lacked in-depth knowledge about AI. Trust was essential to acceptance of AI; people need to be receptive to using AI and feel confident that the information is accurate. We identified internal influences of AI acceptance, including ease of use and complementing clinical judgment rather than replacing it. External influences included privacy, liability, and financial considerations. Participants felt AI could improve patient care and help prevent burnout for providers; however, there were concerns about the impact on the patient-provider relationship.

CONCLUSIONS : The information gained in this study can be used for future research, development, and integration of AI technology.

Nash Danielle M, Thorpe Cathy, Brown Judith Belle, Kueper Jacqueline K, Rayner Jennifer, Lizotte Daniel J, Terry Amanda L, Zwarenstein Merrick

2023-Mar-22

Artificial Intelligence, Canada, Family Medicine, Informatics, Qualitative Research

General

Identification of key genes for IgA nephropathy based on machine learning algorithm and correlation analysis of immune cells.

In Transplant immunology ; h5-index 20.0

INTRODUCTION : The pathogenesis and progression mechanism of Immunoglobulin A nephropathy (IgAN) is not fully understood. There is a lack of panoramic analysis of IgAN immune cell infiltration and algorithms that are more efficient and accurate for screening key pathogenic genes.

METHODS : RNA sequencing (RNA-seq) data sets on IgAN were downloaded from the Gene Expression Omnibus (GEO) database, including GSE93798, GSE35489, and GSE115857. The RNA-seq data set of kidney tissue as control samples were downloaded from the Genotype-Tissue Expression (GTEx) database. Three machine learning algorithms-weighted gene co-expression network analysis (WGCNA), least absolute shrinkage and selection operator (LASSO), and support vector machine-were used to identify the key pathogenic gene sets of the IgAN disease. The ssGSEA method was applied to calculate the immune cell infiltration (ICI) of IgAN samples, whereas the Spearman test was used for correlation analysis. The receiver operator characteristic curve (ROC) was used to evaluate the diagnostic efficacy of key genes. The correlation between the key genes and ICI was analyzed using the Spearman test.

RESULTS : A total of 177 genes were screened out as differentially expressed genes (DEGs) for IgAN, including 135 up-regulated genes and 42 down-regulated genes. The DEGs were significantly enriched in the inflammatory- or immune-related pathways (gene sets). Activating transcription factor 3 (AFT3), C-X-C Motif Chemokine Ligand 6 (CXCL6), and v-fos FBJ murine osteosarcoma viral oncogene homolog B (FOSB) were identified using WGCNA, support vector machine, and LASSO algorithms. These three genes revealed good diagnostic efficacy in the training and test cohorts. The CXCL6 expression positively correlated with activated B cells and memory B cells.

CONCLUSION : ATF3, FOSB, and CXCL6 genes were identified as potential biomarkers of IgAN. These three genes exhibited good diagnostic efficacy for IgAN. We described the landscape of immune cell infiltration for IgAN. Activated B cells and memory B cells were more highly expressed in the IgAN samples than in the control samples. CXCL6 seems crucial to the pathogenesis of IgAN and may induce IgAN by enriching immune cells. Our study may contribute to developing CXCL6 inhibitors that target B cells for IgAN therapy.

Chen Suzhi, Li Yongzhang, Wang Guangjian, Song Lei, Tan Jinchuan, Yang Fengwen

2023-Mar-20

ATF3, CXLC6, FOSB, Immunoglobulin a nephropathy, Machine learning algorithm

Surgery

A biomimetic nanoplatform for precise reprogramming of tumor-associated macrophages and NIR-II mediated antitumor immune activation.

In Acta biomaterialia ; h5-index 89.0

The therapeutic effects of photothermal therapy (PTT) are dependent on the photothermal conversion efficiency of photothermal agents (PTAs) in tumors and the subsequent activation of the antitumor immune system. However, the insufficient tumor accumulation of current PTAs and the inevitable recruitment of tumor-associated macrophages (TAMs) could further compromise the antitumor activities of PTT. To address these issues, a biomimetic photothermal nanoplatform Au@Fe-PM is developed for the targeted remodeling of TAMs, which promotes the antitumor immunity of PTT. Au nanorods with second near-infrared (NIR-II) absorptions are fabricated to serve as PTAs to induce immunogenic cell death in tumor cells. The ferric hydroxide shell coated on Au nanorods can release iron ions to repolarize M2-like TAMs into the tumoricidal M1 phenotype via P38 and STAT1-mediated signaling pathways. Moreover, the surface decoration of platelet membranes endows biomimetic nanoplatform with enhanced tumor targeting ability for precise tumor ablation and TAM regulation. Consequently, Au@Fe-PM under NIR-II laser irradiation exhibits significantly higher inhibitory effects in a poor immunogenic 4T1 tumor-bearing mouse model with a 50% complete remission rate compared to conventional PTT (0%). By simultaneously reversing the immunosuppressive tumor microenvironment, this biomimetic nanoplatform offers a promising strategy for enhancing the antitumor efficacy of PTT. STATEMENT OF SIGNIFICANCE: The therapeutic effects of current photothermal therapy (PTT) are hindered by the insufficient tumor accumulation of conventional photothermal agents and the recruitment of immunosuppressive tumor-associated macrophages (TAMs) after PTT. Herein, we report a biomimetic iron-based second near-infrared (NIR-II) photothermal nanoplatform (Au@Fe-PM) for targeted TAMs reprogramming and NIR-II mediated anti-tumor immunity. Au@Fe-PM can actively target the tumor site with the help of surface-decorated platelet membranes. Meanwhile, iron ions would be released from Au@Fe-PM in acidic lysosomes to reprogram TAMs into tumoricidal M1-like macrophages, which promotes the antitumor responses elicited by NIR-II PTT, thereby contributing to remarkable tumor inhibitory effects, with 50% higher complete remission rate than that of conventional PTT.

Ding Yuan, Qian Xiaohui, Lin Fenghao, Gao Bingqiang, Wang Weili, Yang Huang, Du Yang, Wang Weilin

2023-Mar-20

NIR-II photothermal therapy, antitumor immunity, biomimetic nanoplatform, immunogenic cell death, tumor-associated macrophages

Internal Medicine

Simplified urinary steroid profiling by LC-MS as diagnostic tool for malignancy in adrenocortical tumors.

In Clinica chimica acta; international journal of clinical chemistry

OBJECTIVES : Preoperative identification of malignant adrenal tumors is challenging. 24-h urinary steroid profiling by LC-MS/MS and machine learning has demonstrated high diagnostic power, but the unavailability of bioinformatic models for public use has limited its routine application. We here aimed to increase usability with a novel classification model for the differentiation of adrenocortical adenoma(ACA) and adrenocortical carcinoma(ACC).

METHODS : Eleven steroids (5-pregnenetriol, dehydroepiandrosterone, cortisone, cortisol, α-cortolone, tetrahydro-11-deoxycortisol, etiocholanolone, pregnenolone, pregnanetriol, pregnanediol, and 5-pregnenediol) were quantified by LC-MS/MS in 24-h urine samples from 352 patients with adrenal tumor (281 ACA,71 ACC). Random forest modelling and decision tree algorithms were applied in training (n=188) and test sets (n=80) and independently validated in 84 patients with paired 24-h and spot urine.

RESULTS : After examining different models, a decision tree using excretions of only 5-pregnenetriol and tetrahydro-11-deoxycortisol classified three groups with low, intermediate, and high risk for malignancy. 148/217 ACA were classified as being at low, 67 intermediate, and 2 high risk of malignancy. Conversely, none of the ACC demonstrated a low-risk profile leading to a negative predictive value of 100% for malignancy. In the independent validation cohort, the negative predictive value was again 100% in both 24-h urine and spot urine with a positive predictive value of 87.5% and 86.7%, respectively.

CONCLUSIONS : This simplified LC-MS/MS-based classification model using 24-h-urine provided excellent results for exclusion of ACC and can help to avoid unnecessary surgeries. Analysis of spot urine led to similarly satisfactory results suggesting that cumbersome 24-h urine collection might be dispensable after future validation.

Vogg Nora, Müller Tobias, Floren Andreas, Dandekar Thomas, Riester Anna, Dischinger Ulrich, Kurlbaum Max, Kroiss Matthias, Fassnacht Martin

2023-Mar-20

LC-MS/MS, adrenal tumors, adrenocortical carcinoma, mass spectrometry, steroid profiling

Radiology

Role of hippocampal subfields in neurodegenerative disease progression analyzed with a multi-scale attention-based network.

In NeuroImage. Clinical

BACKGROUND AND OBJECTIVE : Both Alzheimer's disease (AD) and Parkinson's disease (PD) are progressive neurodegenerative diseases. Early identification is very important for the prevention and intervention of their progress. Hippocampus plays a crucial role in cognition, in which there are correlations between atrophy of Hippocampal subfields and cognitive impairment in neurodegenerative diseases. Exploring biomarkers in the prediction of early cognitive impairment in AD and PD is significant for understanding the progress of neurodegenerative diseases.

METHODS : A multi-scale attention-based deep learning method is proposed to perform computer-aided diagnosis for neurodegenerative disease based on Hippocampal subfields. First, the two dimensional (2D) Hippocampal Mapping Image (HMI) is constructed and used as input of three branches of the following network. Second, the multi-scale module and attention module are integrated into the 2D residual network to improve the diversity of the extracted features and capture significance of various voxels for classification. Finally, the role of Hippocampal subfields in the progression of different neurodegenerative diseases is analyzed using the proposed method.

RESULTS : Classification experiments between normal control (NC), mild cognitive impairment (MCI), AD, PD with normal cognition (PD-NC) and PD with mild cognitive impairment (PD-MCI) are carried out using the proposed method. Experimental results show that subfields subiculum, presubiculum, CA1, and molecular layer are strongly correlated with cognitive impairment in AD and MCI, subfields GC-DG and fimbria are sensitive in detecting early stage of cognitive impairment in MCI, subfields CA3, CA4, GC-DG, and CA1 show significant atrophy in PD. For exploring the role of Hippocampal subfields in PD cognitive impairment, we find that left parasubiculum, left HATA and left presubiculum could be important biomarkers for predicting conversion from PD-NC to PD-MCI.

CONCLUSION : The proposed multi-scale attention-based network can effectively discover the correlation between subfields and neurodegenerative diseases. Experimental results are consistent with previous clinical studies, which will be useful for further exploring the role of Hippocampal subfields in neurodegenerative disease progression.

Xu Hongbo, Liu Yan, Wang Ling, Zeng Xiangzhu, Xu Yingying, Wang Zeng

2023-Mar-15

Alzheimer’s disease, Deep learning, Hippocampal subfields, Mild cognitive impairment, Parkinson disease

Radiology

Hyper-convolutions via implicit kernels for medical image analysis.

In Medical image analysis

The convolutional neural network (CNN) is one of the most commonly used architectures for computer vision tasks. The key building block of a CNN is the convolutional kernel that aggregates information from the pixel neighborhood and shares weights across all pixels. A standard CNN's capacity, and thus its performance, is directly related to the number of learnable kernel weights, which is determined by the number of channels and the kernel size (support). In this paper, we present the hyper-convolution, a novel building block that implicitly encodes the convolutional kernel using spatial coordinates. Unlike a regular convolutional kernel, whose weights are independently learned, hyper-convolution kernel weights are correlated through an encoder that maps spatial coordinates to their corresponding values. Hyper-convolutions decouple kernel size from the total number of learnable parameters, enabling a more flexible architecture design. We demonstrate in our experiments that replacing regular convolutions with hyper-convolutions can improve performance with less parameters, and increase robustness against noise. We provide our code here: https://github.com/tym002/Hyper-Convolution.

Ma Tianyu, Wang Alan Q, Dalca Adrian V, Sabuncu Mert R

2023-Mar-16

Convolutional Neural Networks, Deep Learning, Hyper-networks

General

Examination of physical activity development in early childhood: protocol for a longitudinal cohort study of mother-toddler dyads.

In BMC pediatrics ; h5-index 44.0

BACKGROUND : Physical activity (PA) development in toddlers (age 1 and 2 years) is not well understood, partly because of a lack of analytic tools for accelerometer-based data processing that can accurately evaluate PA among toddlers. This has led to a knowledge gap regarding how parenting practices around PA, mothers' PA level, mothers' parenting stress, and child developmental and behavioral problems influence PA development in early childhood.

METHODS : The Child and Mother Physical Activity Study is a longitudinal study to observe PA development in toddlerhood and examine the influence of personal and parental characteristics on PA development. The study is designed to refine and validate an accelerometer-based machine learning algorithm for toddler activity recognition (Aim 1), apply the algorithm to compare the trajectories of toddler PA levels in males and females age 1-3 years (Aim 2), and explore the association between gross motor development and PA development in toddlerhood, as well as how parenting practices around PA, mothers' PA, mothers' parenting stress, and child developmental and behavioral problems are associated with toddlerhood PA development (Exploratory Aims 3a-c).

DISCUSSION : This study will be one of the first to use longitudinal data to validate a machine learning activity recognition algorithm and apply the algorithm to quantify free-living ambulatory movement in toddlers. The study findings will help fill a significant methodological gap in toddler PA measurement and expand the body of knowledge on the factors influencing early childhood PA development.

Welch Sarah B, Honegger Kyle, O’Brien Megan, Capan Selin, Kwon Soyang

2023-Mar-20

Accelerometry, Child development, Machine learning, Parenting, Physical activity

General

dbCNV: deleteriousness-based model to predict pathogenicity of copy number variations.

In BMC genomics ; h5-index 78.0

BACKGROUND : Copy number variation (CNV) is a type of structural variation, which is a gain or loss event with abnormal changes in copy number. Methods to predict the pathogenicity of CNVs are required to realize the relationship between these variants and clinical phenotypes. ClassifyCNV, X-CNV, StrVCTVRE, etc. have been trained to predict the pathogenicity of CNVs, but few studies have been reported based on the deleterious significance of features.

RESULTS : From single nucleotide polymorphism (SNP), gene and region dimensions, we collected 79 informative features that quantitatively describe the characteristics of CNV, such as CNV length, the number of protein genes, the number of three prime untranslated region. Then, according to the deleterious significance, we formulated quantitative methods for features, which fall into two categories: the first is variable type, including maximum, minimum and mean; the second is attribute type, which is measured by numerical sum. We used Gradient Boosted Trees (GBT) algorithm to construct dbCNV, which can be used to predict pathogenicity for five-tier classification and binary classification of CNVs. We demonstrated that the distribution of most feature values was consistent with the deleterious significance. The five-tier classification model accuracy for 0.85 and 0.79 in loss and gain CNVs, which proved that it has high discrimination power in predicting the pathogenicity of five-tier classification CNVs. The binary model achieved area under curve (AUC) values of 0.96 and 0.81 in the validation set, respectively, in gain and loss CNVs.

CONCLUSION : The performance of the dbCNV suggest that functional deleteriousness-based model of CNV is a promising approach to support the classification prediction and to further understand the pathogenic mechanism.

Lv Kangqi, Chen Dayang, Xiong Dan, Tang Huamei, Ou Tong, Kan Lijuan, Zhang Xiuming

2023-Mar-20

Copy number variation, Machine learning, Pathogenicity, XGBoost

General

Robust classification using average correlations as features (ACF).

In BMC bioinformatics

MOTIVATION : In single-cell transcriptomics and other omics technologies, large fractions of missing values commonly occur. Researchers often either consider only those features that were measured for each instance of their dataset, thereby accepting severe loss of information, or use imputation which can lead to erroneous results. Pairwise metrics allow for imputation-free classification with minimal loss of data.

RESULTS : Using pairwise correlations as metric, state-of-the-art approaches to classification would include the K-nearest-neighbor- (KNN) and distribution-based-classification-classifier. Our novel method, termed average correlations as features (ACF), significantly outperforms those approaches by training tunable machine learning models on inter-class and intra-class correlations. Our approach is characterized in simulation studies and its classification performance is demonstrated on real-world datasets from single-cell RNA sequencing and bottom-up proteomics. Furthermore, we demonstrate that variants of our method offer superior flexibility and performance over KNN classifiers and can be used in conjunction with other machine learning methods. In summary, ACF is a flexible method that enables missing value tolerant classification with minimal loss of data.

Schumann Yannis, Neumann Julia E, Neumann Philipp

2023-Mar-20

Classification, Correlation, Machine learning, Missing values, scRNA-seq

General

Using artificial intelligence to support rapid, mixed-methods analysis: Developing an automated qualitative assistant (AQUA).

In Annals of family medicine

Context: Qualitative research - crucial for understanding human behavior - remains underutilized, in part due to the time and cost of annotating qualitative data (coding). Artificial intelligence (AI) has been suggested as a means to reduce those burdens. Older AI techniques (Latent Semantic Indexing / Latent Dirichlet Allocation (LSI/LDA)) have fallen short, in part because qualitative data is rife with idiom, non-standard expressions, and jargon. Objective: To develop an AI platform using updated techniques to augment qualitative data coding. Study Design and Analysis: We previously completed traditional qualitative analysis of a large dataset, with 11 qualitative categories and 72 subcategories (categories), and a final Cohen's kappa ≥ 0.65 as a measure of human inter-coder reliability (ICR) after coding. We built our Automated Qualitative Assistant (AQUA) using a semi-classical approach, replacing LSI/LDA with a graph-theoretic topic extraction and clustering method. AQUA was given the previously-identified qualitative categories and tasked with coding free-text data into those categories. Item coding was scored using cosine-similarity. Population Studied: Pennsylvanian adults. Instrument: Free-text responses to five open ended questions related to the COVID-19 pandemic (e.g. "What worries you most about the COVID-19 pandemic?"). Outcome Measures: AQUA's coding was compared to human coding using Cohen's kappa. This was done on all categories in aggregate, and also on category clusters to identify category groups amenable to AQUA support. AQUA's time to complete coding was compared to the time taken by the human coding team. Dataset: Five unlimited free-text survey answers from 538 responders. Results: AQUA's kappa for all categories was low (kappa~0.45), reflecting the challenge of automated analysis of diverse language. However, for several 3-category combinations (with less linguistic diversity), AQUA performed comparably to human coders, with an ICR kappa range of 0.62 to 0.72 based on test-train split. AQUA's analysis (including human interpretation) took approximately 5 hours, compared to approximately 30 person hours for traditional coding. Conclusions: AQUA enables qualitative researchers to identify categories amenable to automated coding, and to rapidly conduct that coding on the entirety of very large datasets. This saves time and money, and avoids limitations inherent in limiting qualitative analysis to limited samples of a given dataset.

Lennon Robert, Calo William, Miller Erin, Zgierska Aleksandra, Van Scoy Lauren, Fraleigh Robert

2022-Apr-01

General

Effective degradation of tetracycline via recyclable free-standing three-dimensional copper-based graphene as a persulfate catalyst.

In Environmental science and pollution research international

Water pollution by antibiotics is a serious and growing problem. Given this challenge, a free-standing three-dimensional (3D) reduced graphene oxide foam supported copper oxide nanoparticles (3D-rGO-Cu_xO) was synthesized using GO as a precursor and applied as an efficient persulfate activator for tetracycline (TC) degradation. The influences of Cu_xO mass, solution pH, persulfate dosage, and common anions on the TC degradation were investigated in detail. Analytical techniques indicated that the 3D-rGO-Cu_xO showed a cross-linking three-dimensional network structure, and Cu_xO particles with irregular shapes were uniformly loaded on graphene pore walls. The XPS and Auger spectra of Cu confirmed that Cu₂O was the main component in solid copper compounds. The addition of Cu_xO was vitally important for the activation of the oxidation system, and the removal rate reached 98% with a Cu_xO load of 7:1. The pH showed little influence on the activation effect on TC degradation. For common anions, Cl^- and CO₃^2- had little influence on the system, while humic acid had a great inhibitory effect. The EPR test and quenching experiment revealed that the active substances in the oxidative degradation process mainly include SO₄^-·, ·OH, ¹O₂, and reactive Cu(III). Additionally, the 3D-rGO-Cu_xO material proved highly stable according to the replicated test results and was promising for the remediation of antibiotic-contaminated water.

Zhao Chuanqi, Liang Liying, Shi Qin, Xia Hui, Li Chaofan, Ma Junguan

2023-Mar-21

3D-rGO-CuxO, Active radicals, Degradation, Persulfate activation, Tetracycline

General

Artificial intelligence based virtual screening study for competitive and allosteric inhibitors of the SARS-CoV-2 main protease.

In Journal of biomolecular structure & dynamics

SARS-CoV-2 is a highly contagious and dangerous coronavirus that first appeared in late 2019 causing COVID-19, a pandemic of acute respiratory illnesses that is still a threat to health and the general public safety. We performed deep docking studies of 800 M unique compounds in both the active and allosteric sites of the SARS-COV-2 Main Protease (M^pro) and the 15 M and 13 M virtual hits obtained were further taken for conventional docking and molecular dynamic (MD) studies. The best XP Glide docking scores obtained were -14.242 and -12.059 kcal/mol by CHEMBL591669 and the highest binding affinities were -10.5 kcal/mol (from 444215) and -11.2 kcal/mol (from NPC95421) for active and allosteric sites, respectively. Some hits can bind both sites making them a great area of concern. Re-docking of 8 random allosteric complexes in the active site shows a significant reduction in docking scores with a t-test P value of 2.532 × 10^-11 at 95% confidence. Some specific interactions have higher elevations in docking scores. MD studies on 15 complexes show that single-ligand systems are stable as compared to double-ligand systems, and the allosteric binders identified are shown to modulate the active site binding as evidenced by the changes in the interaction patterns and stability of ligands and active site residues. When an allosteric complex was docked to the second monomer to check for homodimer formation, the validated homodimer could not be re-established, further supporting the potential of the identified allosteric binders. These findings could be important in developing novel therapeutics against SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Charles Ssemuyiga, Edgar Mulumba Pius, Mahapatra Rajani Kanta

2023-Mar-21

Artificial intelligence, COVID-19, SARS-CoV-2 main protease, deep docking, molecular docking, molecular dynamics simulation, neural networks

Surgery

Nanotechnology and Artificial Intelligence: An Emerging Paradigm for Postoperative Patient Care.

In Aesthetic surgery journal ; h5-index 35.0

BACKGROUND : An increasing number of aesthetic surgery procedures are being performed in the office-based setting in an ambulatory fashion. Postoperative monitoring for these patients has historically been comprised of paid private-duty nurses measuring vital signs, encouraging ambulation, and monitoring overall comfort level. Recently, advancements in nanotechnology have permitted high-acuity data acquisition of multiple clinical parameters that can be transmitted to the surgeon's mobile device in a continuous fashion.

OBJECTIVE : To describe the authors early experience with this emerging artificial intelligence technology in the postoperative setting.

METHODS : Twenty-three consecutive patients underwent radiofrequency-assisted liposuction and Brazilian Butt Lift (BBL) surgery and placed in a monitoring garment postoperatively. The primary outcome was device usability, reflected by compliance with device and completeness of data collection.

RESULTS : Ninety-one percent of patients wore the device for greater than 12 hours a day in the first 48 hours. Only 39% were compliant with postoperative positioning. No postoperative events were detected.

CONCLUSIONS : The quality of data collected allow for detection of clinical derangements and can alert the surgeon in real time, prompting intervention such as medicine administration, position changes or presentation to the emergency room.

Del Vecchio Daniel, Stein Michael J, Dayan Erez, Marte Joseph, Theodorou Spero

2023-Mar-21

General

Development and validation of machine learning-based model for mortality prediction in patients with acute basilar artery occlusion receiving endovascular treatment: multicentric cohort analysis.

In Journal of neurointerventional surgery ; h5-index 49.0

BACKGROUND : Predicting mortality in stroke patients using information available before endovascular treatment (EVT) is an essential component for supporting clinical decision-making. Although the mortality rate of acute basilar artery occlusion (ABAO) after EVT has reached 40%, few studies have focused on predicting mortality in these individuals. Thus, we aimed to develop and validate a machine learning-based mortality prediction tool based on preoperative information for ABAO patients receiving EVT.

METHODS : The derivation cohort comprised patients from southern provinces of China in the BASILAR registry. The model (POSITIVE: Predicting mOrtality of baSilar artery occlusion patIents Treated wIth EVT) was trained and optimized using a fivefold cross-validation method in which hyperparameters were selected and fine-tuned. This model was retrospectively tested in patients from the northern provinces of China from the BASILAR registry. A prospective test of POSITIVE was performed on consecutive patients from two hospitals between January 2020 and June 2022.

RESULTS : Extreme gradient boosting was employed to construct the POSITIVE model, which achieved the best predictive performance among the eight machine learning algorithms and showed excellent discrimination (area under the curve (AUC) 0.83, 95% confidence interval (95% CI) 0.80 to 0.87) and calibration (Hosmer-Lemeshow test, P>0.05) in the development cohort. AUC yielded by the POSITIVE model for the retrospective test was 0.79 (95% CI 0.71 to 0.85), higher than that obtained by traditional models. Prospective comparisons showed that the POSITIVE model achieved the highest AUC (0.82, 95% CI 0.74 to 0.90) among all prediction models.

CONCLUSION : We developed a machine learning algorithm and retrospective and prospective testing with multicentric cohorts, which exhibited a solid predictive performance and may act as a convenient reference to guide decision-making for ABAO patients. The POSITIVE model is presented online for user-friendly access.

Liu Chang, Huang Jiacheng, Kong Weilin, Chen Liyuan, Song Jiaxing, Yang Jie, Li Fengli, Zi Wenjie

2023-Mar-21

Intervention, Stroke

General

Filopodia-like protrusions of adjacent somatic cells shape the developmental potential of oocytes.

In Life science alliance

The oocyte must grow and mature before fertilization, thanks to a close dialogue with the somatic cells that surround it. Part of this communication is through filopodia-like protrusions, called transzonal projections (TZPs), sent by the somatic cells to the oocyte membrane. To investigate the contribution of TZPs to oocyte quality, we impaired their structure by generating a full knockout mouse of the TZP structural component myosin-X (MYO10). Using spinning disk and super-resolution microscopy combined with a machine-learning approach to phenotype oocyte morphology, we show that the lack of Myo10 decreases TZP density during oocyte growth. Reduction in TZPs does not prevent oocyte growth but impairs oocyte-matrix integrity. Importantly, we reveal by transcriptomic analysis that gene expression is altered in TZP-deprived oocytes and that oocyte maturation and subsequent early embryonic development are partially affected, effectively reducing mouse fertility. We propose that TZPs play a role in the structural integrity of the germline-somatic complex, which is essential for regulating gene expression in the oocyte and thus its developmental potential.

Crozet Flora, Letort Gaëlle, Bulteau Rose, Da Silva Christelle, Eichmuller Adrien, Tortorelli Anna Francesca, Blévinal Joséphine, Belle Morgane, Dumont Julien, Piolot Tristan, Dauphin Aurélien, Coulpier Fanny, Chédotal Alain, Maître Jean-Léon, Verlhac Marie-Hélène, Clarke Hugh J, Terret Marie-Emilie

2023-Jun

General

Forecasting Risk of Future Rapid Glaucoma Worsening Using Early Visual Field, Optical Coherence Tomography, and Clinical Data.

In Ophthalmology. Glaucoma

PURPOSE : Assess whether we can forecast future rapid visual field (VF) worsening using deep learning models (DLMs) trained on early VF, OCT, and clinical data.

DESIGN : Retrospective cohort study.

SUBJECTS : 4,536 eyes from 2,962 patients. 263 (5.80%) of eyes underwent rapid VF worsening (MD slope <-1dB/yr across all VFs).

METHODS : We included eyes that met the following criteria: 1) followed for glaucoma or suspect status 2) had at least five longitudinal reliable VFs (VF_1, VF_2, VF_3, VF_4, VF₅) 3) had one reliable baseline Optical Coherence Tomography (OCT) scan (OCT₁) and one set of baseline clinical measurements (Clinical₁) at the time of VF₁. We designed a DLM to forecast future rapid VF worsening. The input consisted of spatially oriented total deviation values from VF₁ (including or not including VF₂ and VF₃ in some models) and retinal nerve fiber layer thickness values from the baseline OCT. We passed this VF/OCT stack into a vision transformer feature extractor, the output of which was concatenated with baseline clinical data before putting it through a linear classifier to predict that eye's risk of rapid VF worsening across the five VFs. We compared the performance of models with differing inputs by computing area under receiver operating curve (AUC) in the test set. Specifically, we trained models with the following inputs: Model V: VF₁; VC: VF₁+ Clinical₁; VO: VF₁+ OCT₁; VOC: VF₁+ Clinical₁+ OCT₁; V₂: VF₁ + VF₂; V₂OC: VF₁ + VF₂ + Clinical₁ + OCT₁; V₃: VF₁ + VF₂ + VF₃; V₃OC: VF₁ + VF₂ + VF₃ + Clinical₁ + OCT₁.

MAIN OUTCOME MEASURES : AUC of DLMs when forecasting rapidly worsening eyes.

RESULTS : Model V₃OC best forecasted rapid worsening with an AUC (95% CI) of 0.87 (0.77, 0.97). Remaining models in descending order of performance and their respective AUC [95% CI] were: Model V₃ (0.84 [0.74 to 0.95]), Model V₂OC (0.81 [0.70 to 0.92]), Model V₂ (0.81 [0.70 to 0.82]), Model VOC (0.77 [0.65, 0.88]), Model VO [0.75 [0.64, 0.88], Model VC (0.75 [0.63, 0.87]), Model V (0.74 [0.62, 0.86]).

CONCLUSION : DLMs can forecast future rapid glaucoma worsening with modest to high performance when trained using data from early in the disease course. Including baseline data from multiple modalities and subsequent visits improves performance beyond using VF data alone.

Herbert Patrick, Hou Kaihua, Bradley Chris, Hager Greg, Boland Michael V, Ramulu Pradeep, Unberath Mathias, Yohannan Jithin

2023-Mar-19

Deep Learning, Forecasting, Glaucoma

Dermatology

Artificial intelligence for the classification of pigmented skin lesions in populations with skin of colour: A systematic review.

In Dermatology (Basel, Switzerland)

Background While skin cancers are less prevalent in people with skin of color, they are more often diagnosed at later stages and have a poorer prognosis. The use of artificial intelligence (AI) models can potentially improve early detection of skin cancers, however the lack of skin color diversity in training datasets may only widen the pre-existing racial discrepancies in dermatology. Objective To systematically review the technique, quality, accuracy, and implications of studies using AI models trained or tested in populations with skin of color, for classification of pigmented skin lesions. Methods PubMed was used to identify any studies describing AI models for classification of pigmented skin lesions. Only studies that used training datasets with at least 10% of images from people with skin of color were eligible. Outcomes on study population, design of AI model, accuracy, and quality of the studies were reviewed. Results Twenty-two eligible articles were identified. Majority of studies were trained on datasets obtained from Chinese (7/22), Korean (5/22), and Japanese populations (3/22). Seven studies used diverse datasets containing Fitzpatrick skin type I-III in combination with at least 10% from Black American, Native American, Pacific Islander or Fitzpatrick IV-VI. AI models producing binary outcomes (e.g., benign vs malignant) reported an accuracy ranging from 70% to 99.7%. Accuracy of AI models reporting multiclass outcomes (e.g., specific lesion diagnosis) was lower, ranging from 43% to 93%. Reader studies, where dermatologists' classification is compared with AI model outcomes, reported similar accuracy in one study, higher AI accuracy in three studies, and higher clinician accuracy in two studies. A quality review revealed that dataset description and variety, benchmarking, public evaluation, and healthcare application were frequently not addressed. Conclusions While this review provides promising evidence of accurate AI models in skin of color populations, there are still large discrepancies remain in the number of AI models developed in populations with skin of color (particularly Fitzpatrick type IV-VI) and those with largely European ancestry. A lack of publicly available datasets from diverse populations is likely a contributing factor, as is the inadequate reporting of patient-level metadata relating to skin color in training datasets.

Liu Yuyang, Primiero Clare A, Kulkarni Vishnutheertha, Soyer H Peter, Betz-Stablein Brigid

2023-Mar-21

General

Lower body kinematics estimation during walking using an accelerometer.

In Journal of biomechanics

Measuring and predicting accurate joint angles are important to developing analytical tools to gauge users' progress. Such measurement is usually performed in laboratory settings, which is difficult and expensive. So, the aim of this study was continuous estimation of lower limb joint angles during walking using an accelerometer and random forest (RF). Thus, 73 subjects (26 women and 47 men) voluntarily participated in this study. The subjects walked at the slow, moderate, and fast speeds on a walkway, which was covered with 10 Vicon camera. Acceleration was used as input for a RF to estimate ankle, knee, and hip angles (in transverse, frontal, and sagittal planes). Pearson correlation coefficient (r) and Mean Square Error (MSE) were computed between the experimental and estimated data. Paired statistical parametric mapping (SPM) t-test was used to compare the experimental and estimated data throughout gait cycle. The results of this study showed that the MSE of joint angles between the experimental and estimated data ranged from 0.04 to 24.29 and r > 0.91. Moreover, the findings of SPM indicated that there was no significant difference between the experimental and estimated data of ankle, knee, and hip angles in all three planes throughout gait cycle. The results of our research developed a more accessible, portable procedure to quantifying lower limb joint angles by an accelerometer and RF. So, such wearable-based joint angles have the potential to be used in outside-laboratory settings to measure walking kinematics.

Mantashloo Zahed, Abbasi Ali, Tazji Mehdi Khaleghi, Pedram Mir Mohsen

2023-Mar-17

Accelerometer, Gait analysis, Joint angle, Machine learning, Random forest, Statistical parametric mapping

General

Metabolic and Inflammatory profiles define phenotypes with clinical relevance in female knee osteoarthritis patients with joint effusion.

In Rheumatology (Oxford, England)

OBJECTIVES : Osteoarthritis has been the subject of abundant research in the last years with limited translation to the clinical practice, probably due to the disease's high heterogeneity. In this study, we aimed to identify different phenotypes in Knee osteoarthritis (KOA) patients with joint effusion based on their metabolic and inflammatory profiles.

METHODS : A non-supervised strategy based on Statistical and Machine Learning methods was applied to 45 parameters measured on 168 female KOA patients with persistent joint effusion, consecutively recruited at our hospital after a monographic OA outpatient visit. Data comprised anthropometric and metabolic factors and a panel of systemic and local inflammatory markers. The resulting clusters were compared regarding their clinical, radiographic and ultrasound severity at baseline and their radiographic progression at two years.

RESULTS : Our analyses identified four KOA Inflammatory Phenotypes (KOIP): a group characterized by metabolic syndrome, probably driven by body fat and obesity, and by high local and systemic inflammation (KOIP-1); a metabolically healthy phenotype with mild overall inflammation (KOIP-2); a non-metabolic phenotype with high inflammation levels (KOIP-3) and; a metabolic phenotype with low inflammation and cardiovascular risk factors not associated with obesity (KOIP-4). Of interest, these groups exhibited differences regarding pain, functional disability and radiographic progression, pointing to a clinical relevance of the uncovered phenotypes.

CONCLUSION : Our results support the existence of different KOA phenotypes with clinical relevance and differing pathways regarding their pathophysiology and disease evolution, which entails implications in patients' stratification, treatment tailoring and the search of novel and personalized therapies.

Calvet Joan, García-Manrique María, Berenguer-Llergo Antoni, Orellana Cristóbal, Cirera Silvia Garcia, Llop Maria, Lencastre Carlos Galisteo, Arévalo Marta, Aymerich Cristina, Gómez Rafael, Giménez Néstor Albiñana, Gratacó Jordi

2023-Mar-21

Knee osteoarthritis, clinical severity, inflammatory, machine learning, metabolism, phenotype

General

Phenonaut; multiomics data integration for phenotypic space exploration.

In Bioinformatics (Oxford, England)

SUMMARY : Data integration workflows for multiomics data take many forms across academia and industry. Efforts with limited resources often encountered in academia can easily fall short of data integration best practices for processing and combining high content imaging, proteomics, metabolomics and other omics data. We present Phenonaut, a Python software package designed to address the data workflow needs of migration, control, integration, and auditability in the application of literature and proprietary techniques for data source and structure agnostic workflow creation.

AVAILABILITY AND IMPLEMENTATION : Source code: https://github.com/CarragherLab/phenonaut, Documentation: https://carragherlab.github.io/phenonaut, PyPI package: https://pypi.org/project/phenonaut/.

SUPPLEMENTARY INFORMATION : Supplementary data are available at Bioinformatics online.

Shave Steven, Dawson John C, Athar Abdullah M, Nguyen Cuong Q, Kasprowicz Richard, Carragher Neil O

2023-Mar-21

General

Racial difference in the association between non-alcoholic fatty liver disease and incident type 2 diabetes: findings from the CARDIA study.

In Diabetologia ; h5-index 79.0

AIMS/HYPOTHESIS : Type 2 diabetes and non-alcoholic fatty liver disease (NAFLD) are prevalent diseases of metabolic origin. We examined the association between NAFLD and the development of type 2 diabetes among non-Asian adults, and whether the association differs by race.

METHODS : We analysed data from the Coronary Artery Risk Development in Young Adults (CARDIA) study, a population-based prospective cohort study. Participants underwent non-contrast abdominal computed tomography (CT) at baseline (2010-2011) and assessment of glucose measures at the follow-up exam (2015-2016). NAFLD was defined as liver attenuation ≤51 Hounsfield units on CT images after exclusion for other liver fat causes. Race was self-reported. We used targeted maximum likelihood estimation (TMLE) with machine-learning algorithms to estimate difference in type 2 diabetes risk between the NAFLD and non-NAFLD groups.

RESULTS : Of the 1995 participants without type 2 diabetes at baseline (mean age±SD, 50.0±3.6 years; 59% women; 55.0% White and 45.0% Black), 21.7% of White and 16.8% of Black participants had NAFLD at baseline, and 3.7% of White and 8.0% of Black participants developed type 2 diabetes at follow up. After multivariable adjustment, risk difference for type 2 diabetes associated with NAFLD vs no NAFLD was 4.1% (95% CI 0.3%, 7.9%) among White participants and -1.9% (95% CI -5.7%, 2.0%) in Black participants.

CONCLUSIONS/INTERPRETATION : NAFLD was associated with a higher risk of type 2 diabetes among White participants but not among Black participants. This finding suggests that the effect of liver fat on impaired glucose metabolism may be smaller in Black than in White individuals.

Hatano Yu, VanWagner Lisa B, Carnethon Mercedes R, Bancks Michael P, Carson April P, Lloyd-Jones Donald M, Østbye Truls, Viera Anthony J, Yano Yuichiro

2023-Mar-21

Machine learning, Non-alcoholic fatty liver disease, Racial difference, Type 2 diabetes

General

Gender Differences in the Nonspecific and Health-Specific Use of Social Media Before and During the COVID-19 Pandemic: Trend Analysis Using HINTS 2017-2020 Data.

In Journal of health communication ; h5-index 36.0

The use of social media has changed since the outbreak of coronavirus disease 2019 (COVID-19). However, little is known about the gender disparity in social media use for nonspecific and health-specific issues before and during the COVID-19 pandemic. Based on a gender difference perspective, this study aimed to examine how the nonspecific and health-specific uses of social media changed in 2017-2020. The data came from the Health Information National Trends Survey Wave 5 Cycle 1-4. This study included 10,426 participants with complete data. Compared to 2017, there were higher levels of general use in 2019 and 2020, and an increased likelihood of health-related use in 2020 was reported among the general population. Female participants were more likely to be nonspecific and health-specific users than males. Moreover, the relationship of gender with general use increased in 2019 and 2020; however, concerning health-related use, it expanded in 2019 but narrowed in 2020. The COVID-19 global pandemic led to increased use of social media, especially for health-related issues among males. These findings further our understanding of the gender gap in health communication through social media, and contribute to targeted messaging to promote health and reduce disparities between different groups during the pandemic.

Ye Linglong, Chen Yang, Cai Yongming, Kao Yi-Wei, Wang Yuanxin, Chen Mingchih, Shia Ben-Chang, Qin Lei

2023-Mar-21

General

Combining Machine Learning with Physical Knowledge in Thermodynamic Modeling of Fluid Mixtures.

In Annual review of chemical and biomolecular engineering

Thermophysical properties of fluid mixtures are important in many fields of science and engineering. However, experimental data are scarce in this field, so prediction methods are vital. Different types of physical prediction methods are available, ranging from molecular models over equations of state to models of excess properties. These well-established methods are currently being complemented by new methods from the field of machine learning (ML). This review focuses on the rapidly developing interface between these two approaches and gives a structured overview of how physical modeling and ML can be combined to yield hybrid models. We illustrate the different options with examples from recent research and give an outlook on future developments. Expected final online publication date for the Annual Review of Chemical and Biomolecular Engineering, Volume 14 is June 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Jirasek Fabian, Hasse Hans

2023-Mar-21

General

Decoding study-independent mind-wandering from EEG using convolutional neural networks.

In Journal of neural engineering ; h5-index 52.0

Mind-wandering is a mental phenomenon where the internal thought process disengages from the external environment periodically. In the current study, we trained EEG classifiers using convolutional neural networks (CNN) to track mind-wandering across studies. &#xD;Approach: We transformed the input from raw EEG to band-frequency information (power), single-trial ERP (stERP) patterns, and connectivity matrices between channels (based on inter-site phase clustering, ISPC). We trained CNN models for each input type from each EEG channel as the input model for the meta-learner. To verify the generalizability, we used leave-N-participant-out cross-validations (N=6) and tested the meta-learner on the data from an independent study for across-study predictions.&#xD;Main results: The current results show limited generalizability across participants and tasks. Nevertheless, our meta-learner trained with the stERPs performed the best among the state-of-the-art neural networks. The mapping of each input model to the output of the meta-learner indicates the importance of each EEG channel.&#xD;Significance: Our study makes the first attempt to train study-independent mind-wandering classifiers. The results indicate that this remains challenging. The stacking neural network design we used allows an easy inspection of channel importance and feature maps. &#xD.

Jin Christina Yi, Borst Jelmer P, van Vugt Marieke

2023-Mar-21

EEG, classifier, convolutional neural network, generalizability, machine learning, meta-learner, mind-wandering

General

Modern Artificial Neural Networks: Is Evolution Cleverer?

In Neural computation

Machine learning tools, particularly artificial neural networks (ANN), have become ubiquitous in many scientific disciplines, and machine learning-based techniques flourish not only because of the expanding computational power and the increasing availability of labeled data sets but also because of the increasingly powerful training algorithms and refined topologies of ANN. Some refined topologies were initially motivated by neuronal network architectures found in the brain, such as convolutional ANN. Later topologies of neuronal networks departed from the biological substrate and began to be developed independently as the biological processing units are not well understood or are not transferable to in silico architectures. In the field of neuroscience, the advent of multichannel recordings has enabled recording the activity of many neurons simultaneously and characterizing complex network activity in biological neural networks (BNN). The unique opportunity to compare large neuronal network topologies, processing, and learning strategies with those that have been developed in state-of-the-art ANN has become a reality. The aim of this review is to introduce certain basic concepts of modern ANN, corresponding training algorithms, and biological counterparts. The selection of these modern ANN is prone to be biased (e.g., spiking neural networks are excluded) but may be sufficient for a concise overview.

Bahmer Andreas, Gupta Daya, Effenberger Felix

2023-Mar-16

General

Classification of Parkinson's disease with dementia using phase locking factor of event-related oscillations to visual and auditory stimuli.

In Journal of neural engineering ; h5-index 52.0

&#xD;In the last decades, machine learning (ML) approaches have been widely used to distinguish Parkinson's disease (PD) and many other neuropsychiatric diseases. They also speed up the clinicians and facilitate decision-making for several conditions with similar clinical symptoms. The current study attempts to detect PD with dementia (PDD) by Event-related Oscillations (EROs) during cognitive processing in two modalities, i.e. auditory and visual.&#xD;Approach:&#xD;The study was conducted to discriminate PDD from healthy controls (HC) using event-related phase-locking factors in slow frequency ranges (delta and theta) during visual and auditory cognitive tasks. 17 PDD and 19 HC were included in the study, and Linear Discriminant Analysis (LDA) was used as a classifier. During classification analysis, multiple settings were implemented by using different sets of channels (overall, fronto-central and temporo-parieto-occipital region), frequency bands (delta-theta combined, delta, theta, and low theta), and time of interests (0.1- 0.7 s, 0.1 - 0.5 s and 0.1 - 0.3 s for delta, delta-theta combined; 0.1- 0.4 s for theta and low theta) for spatial-spectral-temporal searchlight procedure.&#xD;Main results:&#xD;The classification performance results of the current study revealed that if visual stimuli are applied to PDD, the delta and theta phase-locking factor over fronto-central region have a remarkable contribution to detecting the disease, whereas if auditory stimuli are applied, the phase-locking factor in low theta over temporo-parieto-occipital and in a wider range of frequency (1-7 Hz) over the fronto-central region classify HC and PDD with better performances.&#xD;Significance:&#xD;These findings show that the delta and theta phase-locking factor of EROs during visual and auditory stimuli has valuable contributions to detecting PDD.&#xD.

Tülay Emine Elif, Yıldırım Ebru, Aktürk Tuba, Güntekin Bahar

2023-Mar-21

Classification, Delta, Theta, Inter-trial phase coherence, Linear Discriminant Analysis, “Parkinsons Disease with dementia”

Radiology

Combination Use of Compressed Sensing and Deep Learning for Shoulder Magnetic Resonance Imaging With Various Sequences.

In Journal of computer assisted tomography

OBJECTIVE : For compressed sensing (CS) to become widely used in routine magnetic resonance imaging (MRI), it is essential to improve image quality. This study aimed to evaluate the usefulness of combining CS and deep learning-based reconstruction (DLR) for various sequences in shoulder MRI.

METHODS : This retrospective study included 37 consecutive patients who underwent undersampled shoulder MRIs, including T1-weighted (T1WI), T2-weighted (T2WI), and fat-saturation T2-weighted (FS-T2WI) images. Images were reconstructed using the conventional wavelet-based denoising method (wavelet method) and a combination of wavelet and DLR-based denoising methods (hybrid-DLR method) for each sequence. The signal-to-noise ratio and contrast-to-noise ratio of the bone, muscle, and fat and the full width at half maximum of the shoulder joint were compared between the 2 image types. In addition, 2 board-certified radiologists scored the image noise, contrast, sharpness, artifacts, and overall image quality of the 2 image types on a 4-point scale.

RESULTS : The signal-to-noise ratios and contrast-to-noise ratios of the bone, muscle, and fat in T1WI, T2WI, and FS-T2WI obtained from the hybrid-DLR method were significantly higher than those of the conventional wavelet method (P < 0.001). However, there were no significant differences in the full width at half maximum of the shoulder joint in any of the sequences (P > 0.05). Furthermore, in all sequences, the mean scores of the image noise, sharpness, artifacts, and overall image quality were significantly higher in the hybrid-DLR method than in the wavelet method (P < 0.001), but there were no significant differences in contrast among the sequences (P > 0.05).

CONCLUSIONS : The DLR denoising method can improve the image quality of CS in T1-weighted images, T2-weighted images, and fat-saturation T2-weighted images of the shoulder compared with the wavelet denoising method alone.

Shiraishi Kaori, Nakaura Takeshi, Uetani Hiroyuki, Nagayama Yasunori, Kidoh Masafumi, Kobayashi Naoki, Morita Kosuke, Yamahita Yuichi, Miyamoto Takeshi, Hirai Toshinori

2023-Mar-09

General

Using artificial intelligence to support rapid, mixed-methods analysis: Developing an automated qualitative assistant (AQUA).

In Annals of family medicine

Context: Qualitative research - crucial for understanding human behavior - remains underutilized, in part due to the time and cost of annotating qualitative data (coding). Artificial intelligence (AI) has been suggested as a means to reduce those burdens. Older AI techniques (Latent Semantic Indexing / Latent Dirichlet Allocation (LSI/LDA)) have fallen short, in part because qualitative data is rife with idiom, non-standard expressions, and jargon. Objective: To develop an AI platform using updated techniques to augment qualitative data coding. Study Design and Analysis: We previously completed traditional qualitative analysis of a large dataset, with 11 qualitative categories and 72 subcategories (categories), and a final Cohen's kappa ≥ 0.65 as a measure of human inter-coder reliability (ICR) after coding. We built our Automated Qualitative Assistant (AQUA) using a semi-classical approach, replacing LSI/LDA with a graph-theoretic topic extraction and clustering method. AQUA was given the previously-identified qualitative categories and tasked with coding free-text data into those categories. Item coding was scored using cosine-similarity. Population Studied: Pennsylvanian adults. Instrument: Free-text responses to five open ended questions related to the COVID-19 pandemic (e.g. "What worries you most about the COVID-19 pandemic?"). Outcome Measures: AQUA's coding was compared to human coding using Cohen's kappa. This was done on all categories in aggregate, and also on category clusters to identify category groups amenable to AQUA support. AQUA's time to complete coding was compared to the time taken by the human coding team. Dataset: Five unlimited free-text survey answers from 538 responders. Results: AQUA's kappa for all categories was low (kappa~0.45), reflecting the challenge of automated analysis of diverse language. However, for several 3-category combinations (with less linguistic diversity), AQUA performed comparably to human coders, with an ICR kappa range of 0.62 to 0.72 based on test-train split. AQUA's analysis (including human interpretation) took approximately 5 hours, compared to approximately 30 person hours for traditional coding. Conclusions: AQUA enables qualitative researchers to identify categories amenable to automated coding, and to rapidly conduct that coding on the entirety of very large datasets. This saves time and money, and avoids limitations inherent in limiting qualitative analysis to limited samples of a given dataset.

Lennon Robert, Calo William, Miller Erin, Zgierska Aleksandra, Van Scoy Lauren, Fraleigh Robert

2022-Apr-01

General

The debate over understanding in AI's large language models.

In Proceedings of the National Academy of Sciences of the United States of America

We survey a current, heated debate in the artificial intelligence (AI) research community on whether large pretrained language models can be said to understand language-and the physical and social situations language encodes-in any humanlike sense. We describe arguments that have been made for and against such understanding and key questions for the broader sciences of intelligence that have arisen in light of these arguments. We contend that an extended science of intelligence can be developed that will provide insight into distinct modes of understanding, their strengths and limitations, and the challenge of integrating diverse forms of cognition.

Mitchell Melanie, Krakauer David C

2023-Mar-28

artificial intelligence, large language models, understanding

Internal Medicine

Association between urinary albumin creatinine ratio and cardiovascular disease.

In PloS one ; h5-index 176.0

INTRODUCTION : The association between microalbuminuria and cardiovascular disease (CVD) is accumulating in various patient populations. However, when stratified by sex, the relationship between microalbuminuria and CVD remains unclear.

METHOD : We obtained data from the 2011-2014 and 2019-2020 Korea National Health and Nutrition Examination Survey (KNHANES). Microalbuminuria was measured based on spot urine albumin-creatinine ratio (UACR). The Framingham risk score (FRS) model was implemented to evaluate the CVD risk. Linear and logistic regression models were used to identify the associations of microalbuminuria status with cardiometabolic predictors and CVD status determined by the FRS score.

RESULTS : Among 19,340 representative Korean participants, the (UACR) in Korean women and men with history of CVD was higher than in those without history of CVD. Among patients without history of CVD, multivariate regression analysis showed that a high UACR was related to older age, lower high-density lipoprotein cholesterol level, higher total cholesterol level, higher systolic blood pressure, higher prevalence of current smoking, higher prevalence of diabetes, and higher anti-hypertensive medication use in both women and men. The UACR showed a positive linear correlation with the Framingham risk score in both women and men.

CONCLUSION : The presence of microalbuminuria was significantly associated with the cardiometabolic risk factors and the increased risk of CVD evaluated by FRS model in both women and men in a nationally representative sample of Korea.

Kim Yoo Jin, Hwang Sang Won, Lee Taesic, Lee Jun Young, Uh Young

2023

Public Health

Prevalence of and factors associated with intimate partner violence victimhood among men who have sex with men in Guangzhou, China.

In Sexually transmitted diseases

BACKGROUND : Intimate partner violence (IPV) has been a concern among men who have sex with men (MSM), but less attention has been paid to the factors associated with this population in China.

AIMS : We investigate the prevalence of and factors associated with IPV victimhood among MSM in Guangzhou, China.

METHODS : MSM were recruited from May to November 2017, and data were collected using an anonymous electronic questionnaire. Chi-squared tests and non-conditional logistic regressions were used to explore the factors associated with IPV victimhood.

RESULTS : A total of 129 in 976 (13.22%) MSM reported experiencing IPV victimhood. Multivariable logistic regression analysis showed that individuals who had condomless anal intercourse (CAI, aOR = 1.47, 95%CI: 1.00-2.17) or had sex with a female partner (aOR = 1.81, 95%CI: 1.15-2.83) in the past six months were at a higher risk of IPV victimhood. Participants who had ever experienced child sexual abuse (CSA) were more likely to experience IPV (aOR = 1.97, 95%CI: 1.32-2.94). Individuals who used rush poppers before sex had a higher risk of IPV (aOR = 1.79, 95%CI: 1.21-2.63). In addition, ever having sex with a female sex partner (aOR = 1.65, 95%CI: 1.04-2.60), ever having used rush poppers before sex (aOR = 1.79, 95%CI: 1.22-2.64) in the past six months, and ever having experienced CSA (aOR = 2.01, 95%CI: 1.35-3.01) were associated with experiencing more types of IPV.

CONCLUSIONS : IPV victimhood was relatively common among MSM in Guangzhou, particularly among those who had CAI, experienced CSA, had sex with a female partner, used rush poppers before sex, and those with less education.

Lin Kaihao, Tan Zhimin, Li Jing, Cheng Weibin, Yang Yi, Jiang Hongbo

2023-Mar-22

Surgery

Expanding Cosmetic Plastic Surgery Research Using ChatGPT.

In Aesthetic surgery journal ; h5-index 35.0

BACKGROUND : In the past 3 months, OpenAI, a San Francisco based artificial intelligence (AI) research laboratory, has released ChatGPT, a conversation large language model (LLM). ChatGPT has the ability to answer user questions, admit to mistakes, and learn from users that are accessing the program.

OBJECTIVES : Due to the importance of producing evidence-based research in plastic surgery, the authors of this study wanted to determine how accurate ChatGPT could be in creating novel systematic review ideas that encompasses the diverse practice of cosmetic surgery.

METHODS : ChatGPT was given commands to produce 20 novel systematic review ideas for 12 different topics within cosmetic surgery. For each topic, the system was told to give 10 general and 10 specific ideas that were related to the concept. In order to determine the accuracy of ChatGPT, a literature review was conducted using Pubmed (National Institutes of Health, Bethesda, MD), CINAHL (EBSCO Industries, Birmingham, AL), EMBASE (Elsevier, Amsterdam, the Netherlands), and Cochrane (Wiley, Hoboken, NJ).

RESULTS : A total of 240 'novel' systematic review ideas were constructed by ChatGPT. We determined that the system had an overall accuracy of 55%. When topics were stratified by general and specific ideas, we found that ChatGPT was 35% accurate for general ideas and 75% accurate for specific ideas.

CONCLUSIONS : ChatGPT is an excellent tool that should be utilized by plastic surgeons. ChatGPT is versatile and has uses beyond research including patient consultation, patient support, and marketing. As advancements in AI continue to be made, it is important for plastic surgeons to consider the use of AI in their clinical practice.

Gupta Rohun, Park John B, Bisht Chirag, Herzog Isabel, Weisberger Joseph, Chao John, Chaiyasate Kongkrit, Lee Edward S

2023-Mar-21

General

Protein structure prediction with in-cell photo-crosslinking mass spectrometry and deep learning.

In Nature biotechnology ; h5-index 151.0

While AlphaFold2 can predict accurate protein structures from the primary sequence, challenges remain for proteins that undergo conformational changes or for which few homologous sequences are known. Here we introduce AlphaLink, a modified version of the AlphaFold2 algorithm that incorporates experimental distance restraint information into its network architecture. By employing sparse experimental contacts as anchor points, AlphaLink improves on the performance of AlphaFold2 in predicting challenging targets. We confirm this experimentally by using the noncanonical amino acid photo-leucine to obtain information on residue-residue contacts inside cells by crosslinking mass spectrometry. The program can predict distinct conformations of proteins on the basis of the distance restraints provided, demonstrating the value of experimental data in driving protein structure prediction. The noise-tolerant framework for integrating data in protein structure prediction presented here opens a path to accurate characterization of protein structures from in-cell data.

Stahl Kolja, Graziadei Andrea, Dau Therese, Brock Oliver, Rappsilber Juri

2023-Mar-20

General

Relationship Between Coronavirus Disease 2019 Vaccination Rates and Rare But Potentially Fatal Adverse Events: A Regression Discontinuity Analysis of Western Countries.

In Journal of Korean medical science

BACKGROUND : Owing to limited experience with the new vaccine platforms, discussion of vaccine safety is inevitable. However, media coverage of adverse events of special interest could influence the vaccination rate; thus, evaluating the outcomes of adverse events of special interest influencing vaccine administration is crucial.

METHODS : We conducted regression discontinuity in time analysis to calculate the local average treatment effect (LATE) using datasets from Our World in Data and Johns Hopkins University Center for Systems Science and Engineering. For the United States, the United Kingdom, and Europe, the cutoff points were April 23rd and June 23rd, April 7th, and the 14th week of 2021, respectively.

RESULTS : The LATE of the Advisory Committee on Immunization Practices (ACIP) meeting held on April 23rd was -0.249 for all vaccines, -0.133 (-0.189 to -0.076) for Pfizer, -0.064 (-0.115 to -0.012) for Moderna, and -0.038 (-0.047 to -0.030) for Johnson &amp; Johnson. Discontinuities were observed for all three types of vaccines in the United States. The June 23rd meeting of the ACIP (mRNA vaccines and myocarditis) did not convene any discontinuities. Furthermore, there was no significant drop in the weekly average vaccination rates in Europe following the European Medicines Agency (EMA) statement on April 7th. Conversely, there was a significant drop in the first-dose vaccination rates in the United Kingdom related to the EMA report. The first-dose vaccination rate for all vaccines changed by -0.104 (-0.176 to -0.032).

CONCLUSION : Although monitoring and reporting of adverse events of special interest are important, a careful approach towards public announcements is warranted.

Chae Seung Hoon, Park Hyung Jun, Radnaabaatar Munkhzul, Park Hojun, Jung Jaehun

2023-Mar-20

COVID-19, Regression Discontinuity Analysis, SARS-CoV-2

General

Sparks of Artificial General Intelligence: Early experiments with GPT-4

ArXiv Preprint

Artificial intelligence (AI) researchers have been developing and refining large language models (LLMs) that exhibit remarkable capabilities across a variety of domains and tasks, challenging our understanding of learning and cognition. The latest model developed by OpenAI, GPT-4, was trained using an unprecedented scale of compute and data. In this paper, we report on our investigation of an early version of GPT-4, when it was still in active development by OpenAI. We contend that (this early version of) GPT-4 is part of a new cohort of LLMs (along with ChatGPT and Google's PaLM for example) that exhibit more general intelligence than previous AI models. We discuss the rising capabilities and implications of these models. We demonstrate that, beyond its mastery of language, GPT-4 can solve novel and difficult tasks that span mathematics, coding, vision, medicine, law, psychology and more, without needing any special prompting. Moreover, in all of these tasks, GPT-4's performance is strikingly close to human-level performance, and often vastly surpasses prior models such as ChatGPT. Given the breadth and depth of GPT-4's capabilities, we believe that it could reasonably be viewed as an early (yet still incomplete) version of an artificial general intelligence (AGI) system. In our exploration of GPT-4, we put special emphasis on discovering its limitations, and we discuss the challenges ahead for advancing towards deeper and more comprehensive versions of AGI, including the possible need for pursuing a new paradigm that moves beyond next-word prediction. We conclude with reflections on societal influences of the recent technological leap and future research directions.

Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, Harsha Nori, Hamid Palangi, Marco Tulio Ribeiro, Yi Zhang

2023-03-22

General

Understanding Social Robots: Attribution of Intentional Agency to Artificial and Biological Bodies.

In Artificial life

Much research in robotic artificial intelligence (AI) and Artificial Life has focused on autonomous agents as an embodied and situated approach to AI. Such systems are commonly viewed as overcoming many of the philosophical problems associated with traditional computationalist AI and cognitive science, such as the grounding problem (Harnad) or the lack of intentionality (Searle), because they have the physical and sensorimotor grounding that traditional AI was argued to lack. Robot lawn mowers and self-driving cars, for example, more or less reliably avoid obstacles, approach charging stations, and so on-and therefore might be considered to have some form of artificial intentionality or intentional directedness. It should be noted, though, that the fact that robots share physical environments with people does not necessarily mean that they are situated in the same perceptual and social world as humans. For people encountering socially interactive systems, such as social robots or automated vehicles, this poses the nontrivial challenge to interpret them as intentional agents to understand and anticipate their behavior but also to keep in mind that the intentionality of artificial bodies is fundamentally different from their natural counterparts. This requires, on one hand, a "suspension of disbelief " but, on the other hand, also a capacity for the "suspension of belief." This dual nature of (attributed) artificial intentionality has been addressed only rather superficially in embodied AI and social robotics research. It is therefore argued that Bourgine and Varela's notion of Artificial Life as the practice of autonomous systems needs to be complemented with a practice of socially interactive autonomous systems, guided by a better understanding of the differences between artificial and biological bodies and their implications in the context of social interactions between people and technology.

Ziemke Tom

2023-Mar-16

Attribution, embodiment, grounding, human–robot interaction, intentionality, observer’s grounding problem, social robots

General

Artificial intelligence based virtual screening study for competitive and allosteric inhibitors of the SARS-CoV-2 main protease.

In Journal of biomolecular structure & dynamics

SARS-CoV-2 is a highly contagious and dangerous coronavirus that first appeared in late 2019 causing COVID-19, a pandemic of acute respiratory illnesses that is still a threat to health and the general public safety. We performed deep docking studies of 800 M unique compounds in both the active and allosteric sites of the SARS-COV-2 Main Protease (M^pro) and the 15 M and 13 M virtual hits obtained were further taken for conventional docking and molecular dynamic (MD) studies. The best XP Glide docking scores obtained were -14.242 and -12.059 kcal/mol by CHEMBL591669 and the highest binding affinities were -10.5 kcal/mol (from 444215) and -11.2 kcal/mol (from NPC95421) for active and allosteric sites, respectively. Some hits can bind both sites making them a great area of concern. Re-docking of 8 random allosteric complexes in the active site shows a significant reduction in docking scores with a t-test P value of 2.532 × 10^-11 at 95% confidence. Some specific interactions have higher elevations in docking scores. MD studies on 15 complexes show that single-ligand systems are stable as compared to double-ligand systems, and the allosteric binders identified are shown to modulate the active site binding as evidenced by the changes in the interaction patterns and stability of ligands and active site residues. When an allosteric complex was docked to the second monomer to check for homodimer formation, the validated homodimer could not be re-established, further supporting the potential of the identified allosteric binders. These findings could be important in developing novel therapeutics against SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Charles Ssemuyiga, Edgar Mulumba Pius, Mahapatra Rajani Kanta

2023-Mar-21

Artificial intelligence, COVID-19, SARS-CoV-2 main protease, deep docking, molecular docking, molecular dynamics simulation, neural networks

Surgery

CranioRate TM: An Image-Based, Deep-Phenotyping Analysis Toolset and Online Clinician Interface for Metopic Craniosynostosis.

In Plastic and reconstructive surgery ; h5-index 62.0

INTRODUCTION : The diagnosis and management of metopic craniosynostosis involves subjective decision-making at the point of care. The purpose of this work is to describe a quantitative severity metric and point-of-care user interface to aid clinicians in the management of metopic craniosynostosis and to provide a platform for future research through deep phenotyping.

METHODS : Two machine-learning algorithms were developed that quantify the severity of craniosynostosis - a supervised model specific to metopic craniosynostosis (Metopic Severity Score) and an unsupervised model used for cranial morphology in general (Cranial Morphology Deviation). CT imaging from multiple institutions were compiled to establish the spectrum of severity and a point-of-care tool was developed and validated.

RESULTS : Over the study period (2019-2021), 254 patients with metopic craniosynostosis and 92 control patients who underwent CT scan between the ages of 6 and 18 months were included. Scans were processed using an unsupervised machine-learning based dysmorphology quantification tool, CranioRate TM. The average Metopic severity score (MSS) for normal controls was 0.0±1.0 and for metopic synostosis was 4.9±2.3 (p<0.001). The average Cranial Morphology Deviation (CMD) for normal controls was 85.2±19.2 and for metopic synostosis was 189.9±43.4 (p<0.001). A point-of-care user interface (craniorate.org) has processed 46 CT images from 10 institutions.

CONCLUSION : The resulting quantification of severity using MSS and CMD has shown an improved capacity, relative to conventional measures, to automatically classify normal controls versus patients with metopic synostosis. We have mathematically described, in an objective and quantifiable manner, the distribution of phenotypes in metopic craniosynostosis.

Beiriger Justin W, Tao Wenzheng, Bruce Madeleine K, Anstadt Erin, Christensen Cameron, Smetona John, Whitaker Ross, Goldstein Jesse

2023-Mar-22

General

The Role of MicroRNAs in Predicting the Neurological Outcome of Patients with Subarachnoid Hemorrhage: A Meta-analysis.

In Cellular and molecular neurobiology

Subarachnoid hemorrhage (SAH) is a hemorrhagic cerebrovascular disease with an extremely poor prognosis. The molecular mechanism and biomarkers involved in neurological outcome after SAH still need to be explored. This study assessed the microRNA expression characteristics of SAH patients with different neurological outcomes by meta-analysis. Public databases were searched from database inception until December 2022. The study reported that microRNA expression data in SAH patients with different neurological outcomes were included in the analysis. The differential expression of miRNAs was evaluated by meta-analysis. Overrepresentation analysis was performed for the targeted genes of significant miRNAs. The XGBoost algorithm was used to assess the predictive ability for neurological outcomes with clinical characteristics and significantly expressed miRNAs. Seven studies were finally included in the meta-analysis. The results showed that the levels of miR-152-3p (SMD: - 0.230; 95% CI - 0.389, - 0.070; padj = 0.041), miR-221-3p (SMD: - 0.286; 95% CI - 0.446, - 0.127; padj = 0.007), and miR-34a-5p (SMD: - 0.227; 95% CI - 0.386, - 0.067; padj = 0.041) were significantly lower in SAH patients with good neurological outcomes than in those with poor neurological outcomes. The PI3K-AKT signaling pathway may have an important role in neurological recovery after SAH. Based on the XGBoost algorithm, the neurological outcome could be accurately predicted with clinical characteristics plus the three miRNAs. The expression levels of miR-152-3p, miR-221-3p, and miR-34a-5p were significantly lower in patients with good neurological outcomes than in those with poor outcomes. These miRNAs can serve as potential predictive biomarkers for neurological outcomes. The molecular mechanism and biomarkers involved in neurological outcome after SAH still need to be explored. Our study analyzed microRNA expression characteristics of SAH patients with different neurological outcomes by meta-analysis. After analyze studies reporting the microRNA expression data in SAH patients with different neurological outcomes, results show that the levels of miR-152-3p, miR-221-3p, and miR-34a-5p were significantly lower in SAH patients with good neurological outcomes than in those with poor neurological outcomes. The PI3K-AKT signaling pathway may have an important role in neurological recovery after SAH. Based on the XGBoost algorithm, the neurological outcome could be accurately predicted with clinical characteristics plus the three miRNAs.

Li Jian, Liu Wei, Anniwaer Ankaerjiang, Li Bo, Chen Yutang, Yu Zhaoxia, Yu Xiangyou

2023-Mar-21

Machine learning, Meta analysis, Subarachnoid hemorrhage, microRNAs

Surgery

Deep learning-based high-accuracy detection for lumbar and cervical degenerative disease on T2-weighted MR images.

In European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society

PURPOSE : To develop and validate a deep learning (DL) model for detecting lumbar degenerative disease in both sagittal and axial views of T2-weighted MRI and evaluate its generalized performance in detecting cervical degenerative disease.

METHODS : T2-weighted MRI scans of 804 patients with symptoms of lumbar degenerative disease were retrospectively collected from three hospitals. The training dataset (n = 456) and internal validation dataset (n = 134) were randomly selected from the center I. Two external validation datasets comprising 100 and 114 patients were from center II and center III, respectively. A DL model based on 3D ResNet18 and transformer architecture was proposed to detect lumbar degenerative disease. In addition, a cervical MR image dataset comprising 200 patients from an independent hospital was used to evaluate the generalized performance of the DL model. The diagnostic performance was assessed by the free-response receiver operating characteristic (fROC) curve and precision-recall (PR) curve. Precision, recall, and F1-score were used to measure the DL model.

RESULTS : A total of 2497 three-dimension retrogression annotations were labeled for training (n = 1157) and multicenter validation (n = 1340). The DL model showed excellent detection efficiency in the internal validation dataset, with F1-score achieving 0.971 and 0.903 on the sagittal and axial MR images, respectively. Good performance was also observed in the external validation dataset I (F1-score, 0.768 on sagittal MR images and 0.837 on axial MR images) and external validation dataset II (F1-score, 0.787 on sagittal MR images and 0.770 on axial MR images). Furthermore, the robustness of the DL model was demonstrated via transfer learning and generalized performance evaluation on the external cervical dataset, with the F1-score yielding 0.931 and 0.919 on the sagittal and axial MR images, respectively.

CONCLUSION : The proposed DL model can automatically detect lumbar and cervical degenerative disease on T2-weighted MR images with good performance, robustness, and feasibility in clinical practice.

Yi Wei, Zhao Jingwei, Tang Wen, Yin Hongkun, Yu Lifeng, Wang Yaohui, Tian Wei

2023-Mar-21

Deep learning, Degenerative disc disease, Magnet resonance imaging, Spine

Public Health

Hyperselective neurectomy of thoracodorsal nerve for treatment of the shoulder spasticity: anatomical study and preliminary clinical results.

In Acta neurochirurgica ; h5-index 35.0

BACKGROUND : Hyperselective neurectomy is a reliable treatment for spasticity. This research was designed to quantify the surgical parameters of hyperselective neurectomy of thoracodorsal nerve for shoulder spasticity through anatomical studies, as well as to retrospectively assess patients who underwent this procedure to provide an objective basis for clinical practice.

METHODS : On nine embalmed adult cadavers (18 shoulders), we dissected and observed the branching patterns of thoracodorsal nerve, counted the number of nerve branches, measured the distribution of branch origin point, and determined the length of the surgical incision. Next, we selected five patients who underwent this procedure for shoulder spasticity and retrospectively evaluated (ethic committee: 2022-37) their shoulder function with active/passive range of motion (AROM/PROM) and modified Ashworth scale (MAS).

RESULTS : The anatomical study revealed that the main trunk of thoracodorsal nerve sends out one to three medial branches, with the pattern of only one medial branch being the most common (61.1%); there were significant variations in the branch numbers and nerve distributions; the location of thoracodorsal nerve branches' entry points into the muscle varied from 27.2 to 67.8% of the length of the arm. Clinical follow-up data showed significant improvement in shoulder mobility in all patients. AROM of shoulder abduction increased by 39.4° and PROM increased by 64.2° (P < 0.05). AROM and PROM of shoulder flexion increased by 36.6° and 54.4°, respectively (P < 0.05). In addition, the MAS of shoulder abduction (1.8) and flexion (1.2) was both significantly reduced in all patients (P < 0.05).

CONCLUSION : Hyperselective neurectomy of thoracodorsal nerve is effective and stable in the treatment of shoulder spasticity. Intraoperative attention is required to the numbers of the medial branch of thoracodorsal nerve. We recommend an incision in the mid-axillary line that extends from 25 to 70% of the arm length to fully expose each branch.

Lin Weishan, Li Tie, Qi Wenjun, Shen Yundong, Xu Wendong

2023-Mar-21

Hyperselective neurectomy, Latissimus dorsi, Shoulder joint, Spasticity, Thoracodorsal nerve

General

Identifying geographic atrophy.

In Current opinion in ophthalmology

PURPOSE OF REVIEW : Age-related macular degeneration (AMD) is one of the leading causes of blindness and can progress to geographic atrophy (GA) in late stages of disease. This review article highlights recent literature which assists in the accurate and timely identification of GA, and monitoring of GA progression.

RECENT FINDINGS : Technology for diagnosing and monitoring GA has made significant advances in recent years, particularly regarding the use of optical coherence tomography (OCT). Identification of imaging features which may herald the development of GA or its progression is critical. Deep learning applications for OCT in AMD have shown promising growth over the past several years, but more prospective studies are needed to demonstrate generalizability and clinical utility.

SUMMARY : Identification of GA and of risk factors for GA development or progression is essential when counseling AMD patients and discussing prognosis. With new therapies on the horizon for the treatment of GA, identification of risk factors for the development and progression of GA will become critical in determining the patients who would be appropriate candidates for new targeted therapies.

Clevenger Leanne, Rachitskaya Aleksandra

2023-Mar-20

Radiology

Comparison of deep learning-based reconstruction of PROPELLER Shoulder MRI with conventional reconstruction.

In Skeletal radiology

OBJECTIVE : To compare the image quality and agreement among conventional and accelerated periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) MRI with both conventional reconstruction (CR) and deep learning-based reconstruction (DLR) methods for evaluation of shoulder.

MATERIALS AND METHODS : We included patients who underwent conventional (acquisition time, 8 min) and accelerated (acquisition time, 4 min and 24 s; 45% reduction) PROPELLER shoulder MRI using both CR and DLR methods between February 2021 and February 2022 on a 3 T MRI system. Quantitative evaluation was performed by calculating the signal-to-noise ratio (SNR). Two musculoskeletal radiologists compared the image quality using conventional sequence with CR as the reference standard. Interobserver agreement between image sets for evaluating shoulder was analyzed using weighted/unweighted kappa statistics.

RESULTS : Ninety-two patients with 100 shoulder MRI scans were included. Conventional sequence with DLR had the highest SNR (P < .001), followed by accelerated sequence with DLR, conventional sequence with CR, and accelerated sequence with CR. Comparison of image quality by both readers revealed that conventional sequence with DLR (P = .003 and P < .001) and accelerated sequence with DLR (P = .016 and P < .001) had better image quality than the conventional sequence with CR. Interobserver agreement was substantial to almost perfect for detecting shoulder abnormalities (κ = 0.600-0.884). Agreement between the image sets was substantial to almost perfect (κ = 0.691-1).

CONCLUSION : Accelerated PROPELLER with DLR showed even better image quality than conventional PROPELLER with CR and interobserver agreement for shoulder pathologies comparable to that of conventional PROPELLER with CR, despite the shorter scan time.

Hahn Seok, Yi Jisook, Lee Ho-Joon, Lee Yedaun, Lee Joonsung, Wang Xinzeng, Fung Maggie

2023-Mar-21

Acceleration, Deep learning, Magnetic resonance imaging, Shoulder

Pathology

Effective and Efficient Active Learning for Deep Learning Based Tissue Image Analysis.

In Bioinformatics (Oxford, England)

MOTIVATION : Deep learning attained excellent results in Digital Pathology recently. A challenge with its use is that high quality, representative training data sets are required to build robust models. Data annotation in the domain is labor intensive and demands substantial time commitment from expert pathologists. Active Learning (AL) is a strategy to minimize annotation. The goal is to select samples from the pool of unlabeled data for annotation that improves model accuracy. However, AL is a very compute demanding approach. The benefits for model learning may vary according to the strategy used, and it may be hard for a domain specialist to fine tune the solution without an integrated interface.

RESULTS : We developed a framework that includes a friendly user interface along with run-time optimizations to reduce annotation and execution time in AL in digital pathology. Our solution implements several AL strategies along with our Diversity-Aware Data Acquisition (DADA) acquisition function, which enforces data diversity to improve the prediction performance of a model. In this work, we employed a model simplification strategy (Network Auto-Reduction (NAR)) that significantly improves AL execution time when coupled with DADA. NAR produces less compute demanding models, which replace the target models during the AL process to reduce processing demands. An evaluation with a Tumor-Infiltrating Lymphocytes (TILs) classification application shows that: (i) DADA attains superior performance compared to state-of-the-art AL strategies for different Convolutional Neural Networks (CNNs), (ii) NAR improves the AL execution time by up to 4.3 ×, and (iii) target models trained with patches/data selected by the NAR reduced versions achieve similar or superior classification quality to using target CNNs for data selection.

AVAILABILITY : Source code: https://github.com/alsmeirelles/DADA.

SUPPLEMENTARY INFORMATION : Supplementary data are available at Bioinformatics online.

Meirelles André L S, Kurc Tahsin, Kong Jun, Ferreira Renato, Saltz Joel, Teodoro George

2023-Mar-21

Radiology

Technology and Tool Development for BACPAC: Qualitative and Quantitative Analysis of Accelerated Lumbar Spine MRI with Deep-Learning Based MR Image Reconstruction at 3T.

In Pain medicine (Malden, Mass.)

OBJECTIVES : To evaluate whether combining fast acquisitions with deep-learning reconstruction can provide diagnostically useful images and quantitative assessment comparable to standard-of-care acquisitions for lumbar spine MRI.

METHODS : Eighteen patients were imaged with both standard protocol and fast protocol using reduced signal averages, each protocol including sagittal fat-suppressed T2-weighted, sagittal T1-weighted, and axial T2-weighted 2D fast spin-echo sequences. Fast-acquisition data was additionally reconstructed using vendor-supplied deep-learning reconstruction with three different noise reduction factors. For qualitative analysis, standard images as well as fast images with and without deep-learning reconstruction were graded by three radiologists on five different categories. For quantitative analysis, convolutional neural networks were applied to sagittal T1-weighted images to segment intervertebral discs and vertebral bodies, and disc heights and vertebral body volumes were derived.

RESULTS : Based on noninferiority testing on qualitative scores, fast images without deep-learning reconstruction were inferior to standard images for most categories. However, deep-learning reconstruction improved the average scores, and noninferiority was observed over 24 out of 45 comparisons (all with sagittal T2-weighted images while 4/5 comparisons with sagittal T1-weighted and axial T2-weighted images). Interobserver variability increased with 50 and 75% noise reduction factors. Deep-learning reconstructed fast images with 50% and 75% noise reduction factors had comparable disc heights and vertebral body volumes to standard images (r2 ≥ 0.86 for disc heights and r2 ≥ 0.98 for vertebral body volumes).

CONCLUSIONS : This study demonstrated that deep-learning-reconstructed fast-acquisition images have the potential to provide noninferior image quality and comparable quantitative assessment to standard clinical images.

Han Misung, Bahroos Emma, Hess Madeline E, Chin Cynthia T, Gao Kenneth T, Shin David D, Villanueva-Meyer Javier E, Link Thomas M, Pedoia Valentina, Majumdar Sharmila

2023-Mar-21

Clinical MRI, Deep Learning Reconstruction, Fast Acquisition, Lower Back Pain, Lumbar Spine MRI, Segmentation

Public Health

Multiomic signatures of body mass index identify heterogeneous health phenotypes and responses to a lifestyle intervention.

In Nature medicine ; h5-index 170.0

Multiomic profiling can reveal population heterogeneity for both health and disease states. Obesity drives a myriad of metabolic perturbations and is a risk factor for multiple chronic diseases. Here we report an atlas of cross-sectional and longitudinal changes in 1,111 blood analytes associated with variation in body mass index (BMI), as well as multiomic associations with host polygenic risk scores and gut microbiome composition, from a cohort of 1,277 individuals enrolled in a wellness program (Arivale). Machine learning model predictions of BMI from blood multiomics captured heterogeneous phenotypic states of host metabolism and gut microbiome composition better than BMI, which was also validated in an external cohort (TwinsUK). Moreover, longitudinal analyses identified variable BMI trajectories for different omics measures in response to a healthy lifestyle intervention; metabolomics-inferred BMI decreased to a greater extent than actual BMI, whereas proteomics-inferred BMI exhibited greater resistance to change. Our analyses further identified blood analyte-analyte associations that were modified by metabolomics-inferred BMI and partially reversed in individuals with metabolic obesity during the intervention. Taken together, our findings provide a blood atlas of the molecular perturbations associated with changes in obesity status, serving as a resource to quantify metabolic health for predictive and preventive medicine.

Watanabe Kengo, Wilmanski Tomasz, Diener Christian, Earls John C, Zimmer Anat, Lincoln Briana, Hadlock Jennifer J, Lovejoy Jennifer C, Gibbons Sean M, Magis Andrew T, Hood Leroy, Price Nathan D, Rappaport Noa

2023-Mar-20

Public Health

Acceptability and Effectiveness of COVID-19 Contact Tracing Applications: A Case Study in Saudi Arabia of the Tawakkalna Application.

In Cureus

Background Contact tracing applications were introduced during the COVID-19 pandemic to mitigate the spread of the infection in several countries. In Saudi Arabia, the Tawakkalna application was developed. The Tawakkalna application is a mobile health solution aimed to track infection cases, save lives, and reduce the burden on health facilities. This study aims to explore the public's attitude to and acceptance levels of the Tawakkalna application and to evaluate its effectiveness regarding privacy and security. The main objective of this study is to investigate the user acceptability of contact tracing applications and explore the safety and privacy effectiveness of the COVID-19 contact tracing application, the Tawakkalna application. In addition, the study analyzes factors associated with acceptance levels and compares the results obtained to similar studies in other countries using similar applications. Methodology This study used a valid and reliable online survey that was used in similar studies conducted in other countries to assess the acceptability of the application. The survey was conducted from September to November 2021, and the final dataset included 205 participants. To investigate the privacy and security performance of the Tawakkalna application, we followed the investigation method used by similar research that investigated 28 contact tracing applications across Europe. Results Out of the 205 participants, 84.87% were in favor of the opt-in voluntary installation of the Tawakkalna application, and 49.75% of the participants were in favor of the opt-out automatic installation. Individuals' trust in the government had a huge impact on acceptance, with 60.98% of the participants supporting the application because they believed that the Tawakkalna application would help them stay healthy during the COVID-19 pandemic. Overall, 49% of the participants supporting the application also agreed to the de-identification of their collected data and providing it for research. The Tawakkalna application ranked at the top compared to other contact tracing applications regarding privacy and security. Conclusions The Tawakkalna application developed by the Saudi Data and Artificial Intelligence Authority was a response to the COVID-19 pandemic, which is considered the biggest public health crisis in recent times. The Saudi Arabian government gained the population's acceptance through effective endorsement and the spread of educational content through media channels. By complying with privacy policies, the Tawakkalna application is an effective tool to combat public health infectious diseases.

Dawood Safia, AlKadi Khulud

2023-Feb

acceptability, contact tracing, covid-19, mhealth, permission, privacy, privilege, security, tawakkalna

Oncology

ExBEHRT: Extended Transformer for Electronic Health Records to Predict Disease Subtypes & Progressions

ArXiv Preprint

In this study, we introduce ExBEHRT, an extended version of BEHRT (BERT applied to electronic health records), and apply different algorithms to interpret its results. While BEHRT considers only diagnoses and patient age, we extend the feature space to several multimodal records, namely demographics, clinical characteristics, vital signs, smoking status, diagnoses, procedures, medications, and laboratory tests, by applying a novel method to unify the frequencies and temporal dimensions of the different features. We show that additional features significantly improve model performance for various downstream tasks in different diseases. To ensure robustness, we interpret model predictions using an adaptation of expected gradients, which has not been previously applied to transformers with EHR data and provides more granular interpretations than previous approaches such as feature and token importances. Furthermore, by clustering the model representations of oncology patients, we show that the model has an implicit understanding of the disease and is able to classify patients with the same cancer type into different risk groups. Given the additional features and interpretability, ExBEHRT can help make informed decisions about disease trajectories, diagnoses, and risk factors of various diseases.

Maurice Rupp, Oriane Peter, Thirupathi Pattipaka

2023-03-22

Surgery

Early Triage of Critically Ill Adult Patients With Mushroom Poisoning: Machine Learning Approach.

In JMIR formative research

BACKGROUND : Early triage of patients with mushroom poisoning is essential for administering precise treatment and reducing mortality. To our knowledge, there has been no established method to triage patients with mushroom poisoning based on clinical data.

OBJECTIVE : The purpose of this work was to construct a triage system to identify patients with mushroom poisoning based on clinical indicators using several machine learning approaches and to assess the prediction accuracy of these strategies.

METHODS : In all, 567 patients were collected from 5 primary care hospitals and facilities in Enshi, Hubei Province, China, and divided into 2 groups; 322 patients from 2 hospitals were used as the training cohort, and 245 patients from 3 hospitals were used as the test cohort. Four machine learning algorithms were used to construct the triage model for patients with mushroom poisoning. Performance was assessed using the area under the receiver operating characteristic curve (AUC), decision curve, sensitivity, specificity, and other representative statistics. Feature contributions were evaluated using Shapley additive explanations.

RESULTS : Among several machine learning algorithms, extreme gradient boosting (XGBoost) showed the best discriminative ability in 5-fold cross-validation (AUC=0.83, 95% CI 0.77-0.90) and the test set (AUC=0.90, 95% CI 0.83-0.96). In the test set, the XGBoost model had a sensitivity of 0.93 (95% CI 0.81-0.99) and a specificity of 0.79 (95% CI 0.73-0.85), whereas the physicians' assessment had a sensitivity of 0.86 (95% CI 0.72-0.95) and a specificity of 0.66 (95% CI 0.59-0.73).

CONCLUSIONS : The 14-factor XGBoost model for the early triage of mushroom poisoning can rapidly and accurately identify critically ill patients and will possibly serve as an important basis for the selection of treatment options and referral of patients, potentially reducing patient mortality and improving clinical outcomes.

Liu Yuxuan, Lyu Xiaoguang, Yang Bo, Fang Zhixiang, Hu Dejun, Shi Lei, Wu Bisheng, Tian Yong, Zhang Enli, Yang YuanChao

2023-Mar-21

XGBoost, extreme gradient boosting, machine learning, model, mushroom poisoning, triage

Internal Medicine

Vitamin D in atherosclerosis and cardiovascular events.

In European heart journal ; h5-index 154.0

Both experimental and clinical findings linking vitamin D to cardiovascular (CV) risk have prompted consideration of its supplementation to improve overall health. Yet several meta-analyses do not provide support for the clinical effectiveness of this strategy. Meanwhile, the understanding of the roles of vitamin D in the pathophysiology of CV diseases has evolved. Specifically, recent work has revealed some non-classical pleiotropic effects of vitamin D, increasing the complexity of vitamin D signalling. Within particular microenvironments (e.g. dysfunctional adipose tissue and atherosclerotic plaque), vitamin D can act locally at cellular level through intracrine/autocrine/paracrine feedforward and feedback circuits. Within atherosclerotic tissues, 'local' vitamin D levels may influence relevant systemic consequences independently of its circulating pool. Moreover, vitamin D links closely to other signalling pathways of CV relevance including those driving cellular senescence, ageing, and age-related diseases-among them CV conditions. This review updates knowledge on vitamin D biology aiming to clarify the widening gap between experimental and clinical evidence. It highlights the potential reverse causation confounding correlation between vitamin D status and CV health, and the need to consider novel pathophysiological concepts in the design of future clinical trials that explore the effects of vitamin D on atherosclerosis and risk of CV events.

Carbone Federico, Liberale Luca, Libby Peter, Montecucco Fabrizio

2023-Mar-21

Adipose tissue, Atherosclerosis, Inflammation, Senescence, Vitamin D, Vitamin D receptor

General

A Standardized Clinical Data Harmonization Pipeline for Scalable AI Application Deployment (FHIR-DHP): Validation and Usability Study.

In JMIR medical informatics ; h5-index 23.0

BACKGROUND : Increasing digitalization in the medical domain gives rise to large amounts of health care data, which has the potential to expand clinical knowledge and transform patient care if leveraged through artificial intelligence (AI). Yet, big data and AI oftentimes cannot unlock their full potential at scale, owing to nonstandardized data formats, lack of technical and semantic data interoperability, and limited cooperation between stakeholders in the health care system. Despite the existence of standardized data formats for the medical domain, such as Fast Healthcare Interoperability Resources (FHIR), their prevalence and usability for AI remain limited.

OBJECTIVE : In this paper, we developed a data harmonization pipeline (DHP) for clinical data sets relying on the common FHIR data standard.

METHODS : We validated the performance and usability of our FHIR-DHP with data from the Medical Information Mart for Intensive Care IV database.

RESULTS : We present the FHIR-DHP workflow in respect of the transformation of "raw" hospital records into a harmonized, AI-friendly data representation. The pipeline consists of the following 5 key preprocessing steps: querying of data from hospital database, FHIR mapping, syntactic validation, transfer of harmonized data into the patient-model database, and export of data in an AI-friendly format for further medical applications. A detailed example of FHIR-DHP execution was presented for clinical diagnoses records.

CONCLUSIONS : Our approach enables the scalable and needs-driven data modeling of large and heterogenous clinical data sets. The FHIR-DHP is a pivotal step toward increasing cooperation, interoperability, and quality of patient care in the clinical routine and for medical research.

Williams Elena, Kienast Manuel, Medawar Evelyn, Reinelt Janis, Merola Alberto, Klopfenstein Sophie Anne Ines, Flint Anne Rike, Heeren Patrick, Poncette Akira-Sebastian, Balzer Felix, Beimes Julian, von Bünau Paul, Chromik Jonas, Arnrich Bert, Scherf Nico, Niehaus Sebastian

2023-Mar-21

AI, AI application, FHIR, MIMIC IV, artificial intelligence, care, care unit, cooperation, data, data interoperability, data standardization pipeline, deployment, diagnosis, fast healthcare interoperability resources, medical information mart for intensive care, medical research, patient care, usability

Public Health

Heterogeneity in the response to n-3 polyunsaturated fatty acids.

In Current opinion in clinical nutrition and metabolic care

PURPOSE OF REVIEW : A central goal in the study of long chain n-3 polyunsaturated fatty acids (PUFA) is to translate findings from the basic sciences to the population level to improve human health and prevent chronic diseases. A tenet of this vision is to think in terms of precision medicine and nutrition, that is, stratification of individuals into differing groups that will have different needs across the lifespan for n-3 PUFAs. Therefore, there is a critical need to identify the sources of heterogeneity in the human population in the dietary response to n-3 PUFA intervention.

RECENT FINDINGS : We briefly review key sources of heterogeneity in the response to intake of long chain n-3 PUFAs. These include background diet, host genome, composition of the gut microbiome, and sex. We also discuss the need to integrate data from newer rodent models (e.g. population-based approaches), multi -omics, and analyses of big data using machine learning and data-driven cluster analyses.

SUMMARY : Accounting for vast heterogeneity in the human population, particularly with the use of big data integrated with preclinical evidence, will drive the next generation of precision nutrition studies and randomized clinical trials with long-chain n-3 PUFAs.

Shaikh Saame Raza, Bazinet Richard P

2023-Mar-21

Radiology

AI Cardiac MRI Scar Analysis Aids Prediction of Major Arrhythmic Events in the Multicenter DERIVATE Registry.

In Radiology ; h5-index 91.0

Background Scar burden with late gadolinium enhancement (LGE) cardiac MRI (CMR) predicts arrhythmic events in patients with postinfarction in single-center studies. However, LGE analysis requires experienced human observers, is time consuming, and introduces variability. Purpose To test whether postinfarct scar with LGE CMR can be quantified fully automatically by machines and to compare the ability of LGE CMR scar analyzed by humans and machines to predict arrhythmic events. Materials and Methods This study is a retrospective analysis of the multicenter, multivendor CarDiac MagnEtic Resonance for Primary Prevention Implantable CardioVerter DebrillAtor ThErapy (DERIVATE) registry. Patients with chronic heart failure, echocardiographic left ventricular ejection fraction (LVEF) of less than 50%, and LGE CMR were recruited (from January 2015 through December 2020). In the current study, only patients with ischemic cardiomyopathy were included. Quantification of total, dense, and nondense scars was carried out by two experienced readers or a Ternaus network, trained and tested with LGE images of 515 and 246 patients, respectively. Univariable and multivariable Cox analyses were used to assess patient and cardiac characteristics associated with a major adverse cardiac event (MACE). Area under the receiver operating characteristic curve (AUC) was used to compare model performances. Results In 761 patients (mean age, 65 years ± 11, 671 men), 83 MACEs occurred. With use of the testing group, univariable Cox-analysis found New York Heart Association class, left ventricle volume and/or function parameters (by echocardiography or CMR), guideline criterion (LVEF of ≤35% and New York Heart Association class II or III), and LGE scar analyzed by humans or the machine-learning algorithm as predictors of MACE. Machine-based dense or total scar conferred incremental value over the guideline criterion for the association with MACE (AUC: 0.68 vs 0.63, P = .02 and AUC: 0.67 vs 0.63, P = .01, respectively). Modeling with competing risks yielded for dense and total scar (AUC: 0.67 vs 0.61, P = .01 and AUC: 0.66 vs 0.61, P = .005, respectively). Conclusion In this analysis of the multicenter CarDiac MagnEtic Resonance for Primary Prevention Implantable CardioVerter DebrillAtor ThErapy (DERIVATE) registry, fully automatic machine learning-based late gadolinium enhancement analysis reliably quantifies myocardial scar mass and improves the current prediction model that uses guideline-based risk criteria for implantable cardioverter defibrillator implantation. ClinicalTrials.gov registration no.: NCT03352648 Published under a CC BY 4.0 license. Supplemental material is available for this article.

Ghanbari Fahime, Joyce Thomas, Lorenzoni Valentina, Guaricci Andrea I, Pavon Anna-Giulia, Fusini Laura, Andreini Daniele, Rabbat Mark G, Aquaro Giovanni Donato, Abete Raffaele, Bogaert Jan, Camastra Giovanni, Carigi Samuela, Carrabba Nazario, Casavecchia Grazia, Censi Stefano, Cicala Gloria, De Cecco Carlo N, De Lazzari Manuel, Di Giovine Gabriella, Di Roma Mauro, Focardi Marta, Gaibazzi Nicola, Gismondi Annalaura, Gravina Matteo, Lanzillo Chiara, Lombardi Massimo, Lozano-Torres Jordi, Masi Ambra, Moro Claudio, Muscogiuri Giuseppe, Nese Alberto, Pradella Silvia, Sbarbati Stefano, Schoepf U Joseph, Valentini Adele, Crelier Gérard, Masci Pier Giorgio, Pontone Gianluca, Kozerke Sebastian, Schwitter Juerg

2023-Mar-21

General

Paradoxical decrease of imitation performance with age in children.

In British journal of psychology (London, England : 1953)

Imitation development was studied in a cross-sectional design involving 174 primary-school children (aged 6-10), focusing on the effect of actions' complexity and error analysis to infer the underlying cognitive processes. Participants had to imitate the model's actions as if they were in front of a mirror ('specularly'). Complexity varied across three levels: movements of a single limb; arm and leg of the same body side; or arm and leg of opposite body sides. While the overall error rate decreased with age, this was not true of all error categories. The rate of 'side' errors (using a limb of the wrong body side) paradoxically increased with age (from 9 years). However, with increasing age, the error rate also became less sensitive to the complexity of the action. This pattern is consistent with the hypothesis that older children have the working memory (WM) resources and the body knowledge necessary to imitate 'anatomically', which leads to additional side errors. Younger children might be paradoxically free from such interference because their WM and/or body knowledge are insufficient for anatomical imitation. Yet, their limited WM resources would prevent them from successfully managing the conflict between spatial codes involved in complex actions (e.g. moving the left arm and the right leg). We also found evidence that action side and content might be stored in separate short-term memory (STM) systems: increasing the number of sides to be encoded only affected side retrieval, but not content retrieval; symmetrically, increasing the content (number of movements) of the action only affected content retrieval, but not side retrieval. In conclusion, results suggest that anatomical imitation might interfere with specular imitation at age 9 and that STM storages for side and content of actions are separate.

Ottoboni Giovanni, Toraldo Alessio, Proietti Riccardo, Cangelosi Angelo, Tessari Alessia

2023-Mar-21

body knowledge, children, development, double dissociation, imitation, meaningless action, movement complexity, working memory

Internal Medicine

Machine learning to predict major bleeding during anticoagulation for venous thromboembolism: possibilities and limitations.

In British journal of haematology ; h5-index 64.0

Predictive tools for major bleeding (MB) using machine learning (ML) might be advantageous over traditional methods. We used data from the Registro Informatizado de Enfermedad TromboEmbólica (RIETE) to develop ML algorithms to identify patients with venous thromboembolism (VTE) at increased risk of MB during the first 3 months of anticoagulation. A total of 55 baseline variables were used as predictors. New data prospectively collected from the RIETE were used for further validation. The RIETE and VTE-BLEED scores were used for comparisons. External validation was performed with the COMMAND-VTE database. Learning was carried out with data from 49 587 patients, of whom 873 (1.8%) had MB. The best performing ML method was XGBoost. In the prospective validation cohort the sensitivity, specificity, positive predictive value and F1 score were: 33.2%, 93%, 10%, and 15.4% respectively. F1 value for the RIETE and VTE-BLEED scores were 8.6% and 6.4% respectively. In the external validation cohort the metrics were 10.3%, 87.6%, 3.5% and 5.2% respectively. In that cohort, the F1 value for the RIETE score was 17.3% and for the VTE-BLEED score 9.75%. The performance of the XGBoost algorithm was better than that from the RIETE and VTE-BLEED scores only in the prospective validation cohort, but not in the external validation cohort.

Mora Damián, Mateo Jorge, Nieto José A, Bikdeli Behnood, Yamashita Yugo, Barco Stefano, Jimenez David, Demelo-Rodriguez Pablo, Rosa Vladimir, Yoo Hugo Hyung Bok, Sadeghipour Parham, Monreal Manuel

2023-Mar-21

haemorrhage, machine learning, outcomes, pulmonary embolism, venous thrombosis

Cardiology

Convolution Neural Network Algorithm for Shockable Arrhythmia Classification Within a Digitally Connected Automated External Defibrillator.

In Journal of the American Heart Association ; h5-index 70.0

Background Diagnosis of shockable rhythms leading to defibrillation remains integral to improving out-of-hospital cardiac arrest outcomes. New machine learning techniques have emerged to diagnose arrhythmias on ECGs. In out-of-hospital cardiac arrest, an algorithm within an automated external defibrillator is the major determinant to deliver defibrillation. This study developed and validated the performance of a convolution neural network (CNN) to diagnose shockable arrhythmias within a novel, miniaturized automated external defibrillator. Methods and Results There were 26 464 single-lead ECGs that comprised the study data set. ECGs of 7-s duration were retrospectively adjudicated by 3 physician readers (N=18 total readers). After exclusions (N=1582), ECGs were divided into training (N=23 156), validation (N=721), and test data sets (N=1005). CNN performance to diagnose shockable and nonshockable rhythms was reported with area under the receiver operating characteristic curve analysis, F1, and sensitivity and specificity calculations. The duration for the CNN to output was reported with the algorithm running within the automated external defibrillator. Internal and external validation analyses included CNN performance among arrhythmias, often mistaken for shockable rhythms, and performance among ECGs modified with noise to mimic artifacts. The CNN algorithm achieved an area under the receiver operating characteristic curve of 0.995 (95% CI, 0.990-1.0), sensitivity of 98%, and specificity of 100% to diagnose shockable rhythms. The F1 scores were 0.990 and 0.995 for shockable and nonshockable rhythms, respectively. After input of a 7-s ECG, the CNN generated an output in 383±29 ms (total time of 7.383 s). The CNN outperformed adjudicators in classifying atrial arrhythmias as nonshockable (specificity of 99.3%-98.1%) and was robust against noise artifacts (area under the receiver operating characteristic curve range, 0.871-0.999). Conclusions We demonstrate high diagnostic performance of a CNN algorithm for shockable and nonshockable rhythm arrhythmia classifications within a digitally connected automated external defibrillator. Registration URL: https://clinicaltrials.gov/ct2/show/NCT03662802; Unique identifier: NCT03662802.

Shen Christine P, Freed Benjamin C, Walter David P, Perry James C, Barakat Amr F, Elashery Ahmad Ramy A, Shah Kevin S, Kutty Shelby, McGillion Michael, Ng Fu Siong, Khedraki Rola, Nayak Keshav R, Rogers John D, Bhavnani Sanjeev P

2023-Mar-21

ECG, automated external defibrillator, convolution neural network, machine learning, ventricular arrhythmias

Surgery

Artificial Intelligence-enabled Decision Support in Surgery: State-of-the-art and Future Directions.

In Annals of surgery ; h5-index 104.0

OBJECTIVE : To summarize state-of-the-art artificial intelligence-enabled decision support in surgery and to quantify deficiencies in scientific rigor and reporting.

SUMMARY BACKGROUND DATA : To positively affect surgical care, decision-support models must exceed current reporting guideline requirements by performing external and real-time validation, enrolling adequate sample sizes, reporting model precision, assessing performance across vulnerable populations, and achieving clinical implementation; the degree to which published models meet these criteria is unknown.

METHODS : Embase, PubMed, and MEDLINE databases were searched from their inception to September 21, 2022 for articles describing artificial intelligence-enabled decision support in surgery that uses preoperative or intraoperative data elements to predict complications within 90 days of surgery. Scientific rigor and reporting criteria were assessed and reported according to PRISMA-ScR guidelines.

RESULTS : Sample size ranged from 163-2,882,526, with 8/36 articles (22.2%) featuring sample sizes of less than 2,000; seven of these eight articles (87.5%) had below-average (<0.83) area under the receiver operating characteristic (AUROC) or accuracy. Overall, 29 articles (80.6%) performed internal validation only, five (13.8%) performed external validation, and two (5.6%) performed real-time validation. Twenty-three articles (63.9%) reported precision. No articles reported performance across sociodemographic categories. Thirteen articles (36.1%) presented a framework that could be used for clinical implementation; none assessed clinical implementation efficacy.

CONCLUSIONS : Artificial intelligence-enabled decision support in surgery is limited by reliance on internal validation, small sample sizes that risk overfitting and sacrifice predictive performance, and failure to report confidence intervals, precision, equity analyses, and clinical implementation. Researchers should strive to improve scientific quality.

Loftus Tyler J, Altieri Maria S, Balch Jeremy A, Abbott Kenneth L, Choi Jeff, Marwaha Jayson S, Hashimoto Daniel A, Brat Gabriel A, Raftopoulos Yannis, Evans Heather L, Jackson Gretchen P, Walsh Danielle S, Tignanelli Christopher J

2023-Mar-21

General

Gender Differences in the Nonspecific and Health-Specific Use of Social Media Before and During the COVID-19 Pandemic: Trend Analysis Using HINTS 2017-2020 Data.

In Journal of health communication ; h5-index 36.0

The use of social media has changed since the outbreak of coronavirus disease 2019 (COVID-19). However, little is known about the gender disparity in social media use for nonspecific and health-specific issues before and during the COVID-19 pandemic. Based on a gender difference perspective, this study aimed to examine how the nonspecific and health-specific uses of social media changed in 2017-2020. The data came from the Health Information National Trends Survey Wave 5 Cycle 1-4. This study included 10,426 participants with complete data. Compared to 2017, there were higher levels of general use in 2019 and 2020, and an increased likelihood of health-related use in 2020 was reported among the general population. Female participants were more likely to be nonspecific and health-specific users than males. Moreover, the relationship of gender with general use increased in 2019 and 2020; however, concerning health-related use, it expanded in 2019 but narrowed in 2020. The COVID-19 global pandemic led to increased use of social media, especially for health-related issues among males. These findings further our understanding of the gender gap in health communication through social media, and contribute to targeted messaging to promote health and reduce disparities between different groups during the pandemic.

Ye Linglong, Chen Yang, Cai Yongming, Kao Yi-Wei, Wang Yuanxin, Chen Mingchih, Shia Ben-Chang, Qin Lei

2023-Mar-21

General

Exploring the impact mechanism of low-carbon multivariate coupling system in Chinese typical cities based on machine learning.

In Scientific reports ; h5-index 158.0

Low-carbon city construction is one of the key issues that must be addressed for China to achieve high-quality economic development and meet the Sustainable Development Goals. This study creates a comprehensive evaluation index system of low-carbon city multivariate system based on carbon emission data from 30 typical Chinese cities from 2006 to 2017 and evaluates and analyzes the trend of city low-carbon levels using the CRITIC-TOPSIS technique and MK method. Meanwhile, the influence mechanism of the multi-coupled system is investigated using the coupling coordination degree model and random forest algorithm.The results show that there are 8 cities with a significant increasing trend of low-carbon level, 19 cities with no significant monotonic change trend, and 3 cities with a decreasing trend of low-carbon level. By analyzing the coupling coordination degree, we found that the coupling coordination degree between low-carbon level and economic development in most cities tends to increase year by year, from the initial antagonistic effect to a good coordination development trend, which confirms the "inverted U-shaped" relationship between economy and carbon emission. In addition, industrial pollutant emissions, foreign direct investment, and economic output are the core drivers of low-carbon levels in cities.

Yang Haonan, Chen Liang, Huang Huan, Tang Panyu, Xie Hua, Wang Chu

2023-Mar-20

Public Health

Examining thematic and emotional differences across Twitter, Reddit, and YouTube: The case of COVID-19 vaccine side effects.

In Computers in human behavior ; h5-index 125.0

Social media discourse has become a key data source for understanding the public's perception of, and sentiments during a public health crisis. However, given the different niches which platforms occupy in terms of information exchange, reliance on a single platform would provide an incomplete picture of public opinions. Based on the schema theory, this study suggests a 'social media platform schema' to indicate users' different expectations based on previous usages of platform and argues that a platform's distinct characteristics foster distinct platform schema and, in turn, distinct nature of information. We analyzed COVID-19 vaccine side effect-related discussions from Twitter, Reddit, and YouTube, each of which represents a different type of the platform, and found thematic and emotional differences across platforms. Thematic analysis using k-means clustering algorithm identified seven clusters in each platform. To computationally group and contrast thematic clusters across platforms, we employed modularity analysis using the Louvain algorithm to determine a semantic network structure based on themes. We also observed differences in emotional contexts across platforms. Theoretical and public health implications are then discussed.

Kwon Soyeon, Park Albert

2023-Jul

Consumer health information, Schema theory, Social media, Social network analysis, Unsupervised machine learning

General

Synthetic Health-related Longitudinal Data with Mixed-type Variables Generated using Diffusion Models

ArXiv Preprint

This paper presents a novel approach to simulating electronic health records (EHRs) using diffusion probabilistic models (DPMs). Specifically, we demonstrate the effectiveness of DPMs in synthesising longitudinal EHRs that capture mixed-type variables, including numeric, binary, and categorical variables. To our knowledge, this represents the first use of DPMs for this purpose. We compared our DPM-simulated datasets to previous state-of-the-art results based on generative adversarial networks (GANs) for two clinical applications: acute hypotension and human immunodeficiency virus (ART for HIV). Given the lack of similar previous studies in DPMs, a core component of our work involves exploring the advantages and caveats of employing DPMs across a wide range of aspects. In addition to assessing the realism of the synthetic datasets, we also trained reinforcement learning (RL) agents on the synthetic data to evaluate their utility for supporting the development of downstream machine learning models. Finally, we estimated that our DPM-simulated datasets are secure and posed a low patient exposure risk for public access.

Nicholas I-Hsien Kuo, Louisa Jorm, Sebastiano Barbieri

2023-03-22

Public Health

Gut feelings: associations of emotions and emotion regulation with the gut microbiome in women.

In Psychological medicine ; h5-index 82.0

BACKGROUND : Accumulating evidence suggests that positive and negative emotions, as well as emotion regulation, play key roles in human health and disease. Recent work has shown the gut microbiome is important in modulating mental and physical health through the gut-brain axis. Yet, its association with emotions and emotion regulation are understudied. Here we examined whether positive and negative emotions, as well as two emotion regulation strategies (i.e. cognitive reappraisal and suppression), were associated with the gut microbiome composition and functional pathways in healthy women.

METHODS : Participants were from the Mind-Body Study (N = 206, mean age = 61), a sub-study of the Nurses' Health Study II cohort. In 2013, participants completed measures of emotion-related factors. Two pairs of stool samples were collected, 6 months apart, 3 months after emotion-related factors measures were completed. Analyses examined associations of emotion-related factors with gut microbial diversity, overall microbiome structure, and specific species/pathways and adjusted for relevant covariates.

RESULTS : Alpha diversity was negatively associated with suppression. In multivariate analysis, positive emotions were inversely associated with the relative abundance of Firmicutes bacterium CAG 94 and Ruminococcaceae bacterium D16, while negative emotions were directly correlated with the relative abundance of these same species. At the metabolic pathway level, negative emotions were inversely related to the biosynthesis of pantothenate, coenzyme A, and adenosine.

CONCLUSIONS : These findings offer human evidence supporting linkages of emotions and related regulatory processes with the gut microbiome and highlight the importance of incorporating the gut microbiome in our understanding of emotion-related factors and their associations with physical health.

Ke Shanlin, Guimond Anne-Josee, Tworoger Shelley S, Huang Tianyi, Chan Andrew T, Liu Yang-Yu, Kubzansky Laura D

2023-Mar-21

Emotion regulation, emotions, gut microbiome, gut–brain axis

Radiology

Functional and structural alterations of dorsal attention network in preclinical and early-stage Alzheimer's disease.

In CNS neuroscience & therapeutics

OBJECTIVES : Subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI) are known as the preclinical and early stage of Alzheimer's disease (AD). The dorsal attention network (DAN) is mainly responsible for the "top-down" attention process. However, previous studies mainly focused on single functional modality and limited structure. This study aimed to investigate the multimodal alterations of DAN in SCD and aMCI to assess their diagnostic value in preclinical and early-stage AD.

METHODS : Resting-state functional magnetic resonance imaging (MRI) was carried out to measure the fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), and functional connectivity (FC). Structural MRI was used to calculate the gray matter volume (GMV) and cortical thickness. Moreover, receiver-operating characteristic (ROC) analysis was used to distinguish these alterations in SCD and aMCI.

RESULTS : The SCD and aMCI groups showed both decreased ReHo in the right middle temporal gyrus (MTG) and decreased GMV compared to healthy controls (HCs). Especially in the SCD group, there were increased fALFF and increased ReHo in the left inferior occipital gyrus (IOG), decreased fALFF and increased FC in the left inferior parietal lobule (IPL), and reduced cortical thickness in the right inferior temporal gyrus (ITG). Furthermore, functional and structural alterations in the SCD and aMCI groups were closely related to episodic memory (EM), executive function (EF), and information processing speed (IPS). The combination of multiple indicators of DAN had a high accuracy in differentiating clinical stages.

CONCLUSIONS : Our current study demonstrated functional and structural alterations of DAN in SCD and aMCI, especially in the MTG, IPL, and SPL. Furthermore, cognitive performance was closely related to these significant alterations. Our study further suggested that the combined multiple indicators of DAN could be acted as the latent neuroimaging markers of preclinical and early-stage AD for their high diagnostic value.

Wu Huimin, Song Yu, Yang Xinyi, Chen Shanshan, Ge Honglin, Yan Zheng, Qi Wenzhang, Yuan Qianqian, Liang Xuhong, Lin Xingjian, Chen Jiu

2023-Mar-21

amnestic mild cognitive impairment, dorsal attention network, resting-state functional magnetic resonance imaging, structural magnetic resonance imaging, subjective cognitive decline

General

Relationship Between Coronavirus Disease 2019 Vaccination Rates and Rare But Potentially Fatal Adverse Events: A Regression Discontinuity Analysis of Western Countries.

In Journal of Korean medical science

BACKGROUND : Owing to limited experience with the new vaccine platforms, discussion of vaccine safety is inevitable. However, media coverage of adverse events of special interest could influence the vaccination rate; thus, evaluating the outcomes of adverse events of special interest influencing vaccine administration is crucial.

METHODS : We conducted regression discontinuity in time analysis to calculate the local average treatment effect (LATE) using datasets from Our World in Data and Johns Hopkins University Center for Systems Science and Engineering. For the United States, the United Kingdom, and Europe, the cutoff points were April 23rd and June 23rd, April 7th, and the 14th week of 2021, respectively.

RESULTS : The LATE of the Advisory Committee on Immunization Practices (ACIP) meeting held on April 23rd was -0.249 for all vaccines, -0.133 (-0.189 to -0.076) for Pfizer, -0.064 (-0.115 to -0.012) for Moderna, and -0.038 (-0.047 to -0.030) for Johnson &amp; Johnson. Discontinuities were observed for all three types of vaccines in the United States. The June 23rd meeting of the ACIP (mRNA vaccines and myocarditis) did not convene any discontinuities. Furthermore, there was no significant drop in the weekly average vaccination rates in Europe following the European Medicines Agency (EMA) statement on April 7th. Conversely, there was a significant drop in the first-dose vaccination rates in the United Kingdom related to the EMA report. The first-dose vaccination rate for all vaccines changed by -0.104 (-0.176 to -0.032).

CONCLUSION : Although monitoring and reporting of adverse events of special interest are important, a careful approach towards public announcements is warranted.

Chae Seung Hoon, Park Hyung Jun, Radnaabaatar Munkhzul, Park Hojun, Jung Jaehun

2023-Mar-20

COVID-19, Regression Discontinuity Analysis, SARS-CoV-2

General

Identifying Disease of Interest With Deep Learning Using Diagnosis Code.

In Journal of Korean medical science

BACKGROUND : Autoencoder (AE) is one of the deep learning techniques that uses an artificial neural network to reconstruct its input data in the output layer. We constructed a novel supervised AE model and tested its performance in the prediction of a co-existence of the disease of interest only using diagnostic codes.

METHODS : Diagnostic codes of one million randomly sampled patients listed in the Korean National Health Information Database in 2019 were used to train, validate, and test the prediction model. The first used AE solely for a feature engineering tool for an input of a classifier. Supervised Multi-Layer Perceptron (sMLP) was added to train a classifier to predict a binary level with latent representation as an input (AE + sMLP). The second model simultaneously updated the parameters in the AE and the connected MLP classifier during the learning process (End-to-End Supervised AE [EEsAE]). We tested the performances of these two models against baseline models, eXtreme Gradient Boosting (XGB) and naïve Bayes, in the prediction of co-existing gastric cancer diagnosis.

RESULTS : The proposed EEsAE model yielded the highest F1-score and highest area under the curve (0.86). The EEsAE and AE + sMLP gave the highest recalls. XGB yielded the highest precision. Ablation study revealed that iron deficiency anemia, gastroesophageal reflux disease, essential hypertension, gastric ulcers, benign prostate hyperplasia, and shoulder lesion were the top 6 most influential diagnoses on performance.

CONCLUSION : A novel EEsAE model showed promising performance in the prediction of a disease of interest.

Cho Yoon-Sik, Kim Eunsun, Stafford Patrick L, Oh Min-Hwan, Kwon Younghoon

2023-Mar-20

Deep Learning, Diagnosis Code, Gastric Cancer, Machine Learning, Prediction

Dermatology

[Progress in research of risk prediction model for chronic kidney disease].

In Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi

Chronic kidney disease (CKD) is an important global public health problem that greatly threatens population health. Application of risk prediction model is a crucial way for the primary prevention of CKD, which can stratify the risk for developing CKD and identify high-risk individuals for more intensive interventions. By now, more than twenty risk prediction models for CKD have been developed worldwide. There are also four domestic risk prediction models developed for Chinese population. However, none of these models have been recommended in clinical guidelines yet. The existing risk prediction models have some limitations in terms of outcome definition, predictors, strategies for handling missing data, and model derivation. In the future, the applications of emerging biomarkers and polygenic risk scores as well as advances in machine learning methods will provide more possibilities for the further improvement of the model.

Zeng Z Q, Yang S C, Yu C Q, Zhang L X, Lyu J, Li L M

2023-Mar-10

Pathology

Relationship between the deep features of the full-scan pathological map of mucinous gastric carcinoma and related genes based on deep learning.

In Heliyon

BACKGROUND : Long-term differential expression of disease-associated genes is a crucial driver of pathological changes in mucinous gastric carcinoma. Therefore, there should be a correlation between depth features extracted from pathology-based full-scan images using deep learning and disease-associated gene expression. This study tried to provides preliminary evidence that long-term differentially expressed (disease-associated) genes lead to subtle changes in disease pathology by exploring their correlation, and offer a new ideas for precise analysis of pathomics and combined analysis of pathomics and genomics.

METHODS : Full pathological scans, gene sequencing data, and clinical data of patients with mucinous gastric carcinoma were downloaded from TCGA data. The VGG-16 network architecture was used to construct a binary classification model to explore the potential of VGG-16 applications and extract the deep features of the pathology-based full-scan map. Differential gene expression analysis was performed and a protein-protein interaction network was constructed to screen disease-related core genes. Differential, Lasso regression, and extensive correlation analyses were used to screen for valuable deep features. Finally, a correlation analysis was used to determine whether there was a correlation between valuable deep features and disease-related core genes.

RESULT : The accuracy of the binary classification model was 0.775 ± 0.129. A total of 24 disease-related core genes were screened, including ASPM, AURKA, AURKB, BUB1, BUB1B, CCNA2, CCNB1, CCNB2, CDCA8, CDK1, CENPF, DLGAP5, KIF11, KIF20A, KIF2C, KIF4A, MELK, PBK, RRM2, TOP2A, TPX2, TTK, UBE2C, and ZWINT. In addition, differential, Lasso regression, and extensive correlation analyses were used to screen eight valuable deep features, including features 51, 106, 109, 118, 257, 282, 326, and 487. Finally, the results of the correlation analysis suggested that valuable deep features were either positively or negatively correlated with core gene expression.

CONCLUSION : The preliminary results of this study support our hypotheses. Deep learning may be an important bridge for the joint analysis of pathomics and genomics and provides preliminary evidence for long-term abnormal expression of genes leading to subtle changes in pathology.

Li Ding, Li Xiaoyuan, Li Shifang, Qi Mengmeng, Sun Xiaowei, Hu Guojie

2023-Mar

Bioinformatics, Deep learning, Genomics, Mucinous gastric carcinoma, Pathomics

General

A deep learning approach for lane marking detection applying encode-decode instant segmentation network.

In Heliyon

A lot of people suffer from disability and death due to unintentional road accidents, which also result in the loss of a significant amount of financial assets. Several essential features of Advanced Driver Assistance Systems (ADAS) are being incorporated into vehicles by researchers to prevent road accidents. Lane marking detection (LMD) is a fundamental ADAS technology that helps the vehicle to keep its position in the lane. The current study employs Deep Learning (DL) methodologies and has several research constraints due to various problems. Researchers sometimes encounter difficulties in LMD due to environmental factors such as the variation of lights, obstacles, shadows, and curve lanes. To address these limitations, this study presents the Encode-Decode Instant Segmentation Network (EDIS-Net) as a DL methodology for detecting lane marking under various environmental situations with reliable accuracy. The framework is based on the E-Net architecture and incorporates combined cross-entropy and discriminative losses. The encoding segment was split into binary and instant segmentation to extract information about the lane pixels and the pixel position. DenselyBased Spatial Clustering of Application with Noise (DBSCAN) is employed to connect the predicted lane pixels and to get the final output. The system was trained with augmented data from the Tusimple dataset and then tested on three datasets: Tusimple, CalTech, and a local dataset. On the Tusimple dataset, the model achieved 97.39% accuracy. Furthermore, it has an average accuracy of 97.07% and 96.23% on the CalTech and local datasets, respectively. On the testing dataset, the EDIS-Net exhibited promising results compared to existing LMD approaches. Since the proposed framework performs better on the testing datasets, it can be argued that the model can recognize lane marking confidently in various scenarios. This study presents a novel EDIS-Net technique for efficient lane marking detection. It also includes the model's performance verification by testing in three different public datasets.

Al Mamun Abdullah, Em Poh Ping, Hossen Md Jakir, Jahan Busrat, Tahabilder Anik

2023-Mar

ADAS, CalTech, Deep learning, Lane markings detection, Segmentation, Tusimple

Public Health

Acceptability and Effectiveness of COVID-19 Contact Tracing Applications: A Case Study in Saudi Arabia of the Tawakkalna Application.

In Cureus

Background Contact tracing applications were introduced during the COVID-19 pandemic to mitigate the spread of the infection in several countries. In Saudi Arabia, the Tawakkalna application was developed. The Tawakkalna application is a mobile health solution aimed to track infection cases, save lives, and reduce the burden on health facilities. This study aims to explore the public's attitude to and acceptance levels of the Tawakkalna application and to evaluate its effectiveness regarding privacy and security. The main objective of this study is to investigate the user acceptability of contact tracing applications and explore the safety and privacy effectiveness of the COVID-19 contact tracing application, the Tawakkalna application. In addition, the study analyzes factors associated with acceptance levels and compares the results obtained to similar studies in other countries using similar applications. Methodology This study used a valid and reliable online survey that was used in similar studies conducted in other countries to assess the acceptability of the application. The survey was conducted from September to November 2021, and the final dataset included 205 participants. To investigate the privacy and security performance of the Tawakkalna application, we followed the investigation method used by similar research that investigated 28 contact tracing applications across Europe. Results Out of the 205 participants, 84.87% were in favor of the opt-in voluntary installation of the Tawakkalna application, and 49.75% of the participants were in favor of the opt-out automatic installation. Individuals' trust in the government had a huge impact on acceptance, with 60.98% of the participants supporting the application because they believed that the Tawakkalna application would help them stay healthy during the COVID-19 pandemic. Overall, 49% of the participants supporting the application also agreed to the de-identification of their collected data and providing it for research. The Tawakkalna application ranked at the top compared to other contact tracing applications regarding privacy and security. Conclusions The Tawakkalna application developed by the Saudi Data and Artificial Intelligence Authority was a response to the COVID-19 pandemic, which is considered the biggest public health crisis in recent times. The Saudi Arabian government gained the population's acceptance through effective endorsement and the spread of educational content through media channels. By complying with privacy policies, the Tawakkalna application is an effective tool to combat public health infectious diseases.

Dawood Safia, AlKadi Khulud

2023-Feb

acceptability, contact tracing, covid-19, mhealth, permission, privacy, privilege, security, tawakkalna

Public Health

Racial Equity in Healthcare Machine Learning: Illustrating Bias in Models With Minimal Bias Mitigation.

In Cureus

Background and objective While the potential of machine learning (ML) in healthcare to positively impact human health continues to grow, the potential for inequity in these methods must be assessed. In this study, we aimed to evaluate the presence of racial bias when five of the most common ML algorithms are used to create models with minimal processing to reduce racial bias. Methods By utilizing a CDC public database, we constructed models for the prediction of healthcare access (binary variable). Using area under the curve (AUC) as our performance metric, we calculated race-specific performance comparisons for each ML algorithm. We bootstrapped our entire analysis 20 times to produce confidence intervals for our AUC performance metrics. Results With the exception of only a few cases, we found that the performance for the White group was, in general, significantly higher than that of the other racial groups across all ML algorithms. Additionally, we found that the most accurate algorithm in our modeling was Extreme Gradient Boosting (XGBoost) followed by random forest, naive Bayes, support vector machine (SVM), and k-nearest neighbors (KNN). Conclusion Our study illustrates the predictive perils of incorporating minimal racial bias mitigation in ML models, resulting in predictive disparities by race. This is particularly concerning in the setting of evidence for limited bias mitigation in healthcare-related ML. There needs to be more conversation, research, and guidelines surrounding methods for racial bias assessment and mitigation in healthcare-related ML models, both those currently used and those in development.

Barton Michael, Hamza Mahmoud, Guevel Borna

2023-Feb

data science, health equity, healthcare technology, machine learning, racial bias

Public Health

Examining thematic and emotional differences across Twitter, Reddit, and YouTube: The case of COVID-19 vaccine side effects.

In Computers in human behavior ; h5-index 125.0

Social media discourse has become a key data source for understanding the public's perception of, and sentiments during a public health crisis. However, given the different niches which platforms occupy in terms of information exchange, reliance on a single platform would provide an incomplete picture of public opinions. Based on the schema theory, this study suggests a 'social media platform schema' to indicate users' different expectations based on previous usages of platform and argues that a platform's distinct characteristics foster distinct platform schema and, in turn, distinct nature of information. We analyzed COVID-19 vaccine side effect-related discussions from Twitter, Reddit, and YouTube, each of which represents a different type of the platform, and found thematic and emotional differences across platforms. Thematic analysis using k-means clustering algorithm identified seven clusters in each platform. To computationally group and contrast thematic clusters across platforms, we employed modularity analysis using the Louvain algorithm to determine a semantic network structure based on themes. We also observed differences in emotional contexts across platforms. Theoretical and public health implications are then discussed.

Kwon Soyeon, Park Albert

2023-Jul

Consumer health information, Schema theory, Social media, Social network analysis, Unsupervised machine learning

General

A study on Shine-Muscat grape detection at maturity based on deep learning.

In Scientific reports ; h5-index 158.0

The efficient detection of grapes is a crucial technology for fruit-picking robots. To better identify grapes from branch shading that is similar to the fruit color and improve the detection accuracy of green grapes due to cluster adhesion, this study proposes a Shine-Muscat Grape Detection Model (S-MGDM) based on improved YOLOv3 for the ripening stage. DenseNet is fused in the backbone feature extraction network to extract richer underlying grape information; depth-separable convolution, CBAM, and SPPNet are added in the multi-scale detection module to increase the perceptual field of grape targets and reduce the model computation; meanwhile, PANet is combined with FPN to promote inter-network information flow and iteratively extract grape features. In addition, the CIOU regression loss function is used and the prior frame size is modified by the k-means algorithm to improve the accuracy of detection. The improved detection model achieves an AP value of 96.73% and an F1 value of 91% on the test set, which are 3.87% and 3% higher than the original network model, respectively; the average detection speed under GPU reaches 26.95 frames/s, which is 6.49 frames/s higher than the original model. The comparison results with several mainstream detection algorithms such as SSD and YOLO series show that the method has excellent detection accuracy and good real-time performance, which is an important reference value for the problem of accurate identification of Shine-Muscat grapes at maturity.

Wei Xinjie, Xie Fuxiang, Wang Kai, Song Jian, Bai Yang

2023-Mar-20

General

CBCovid19EC: A dataset complete blood count and PCR test for COVID-19 detection in Ecuadorian population.

In Data in brief

In this work, we present the complete blood count data and PCR test results of a population of Ecuadorians from different provinces, primarily residing in the Andean region, especially in Quito. PCR was the standard test to detect Covid-19 during the pandemic since 2020. The data were obtained between March 1st and August 12th, 2021. Segurilab and Previne Salud laboratories performed the tests. The dataset contains about 400 clinical cases. Each patient agreed to participate in the study by sharing the results of their PCR (reverse transcription polymerase chain reaction) tests and CBC (complete blood count). CBC test measured several components and features of the blood, including red blood cells, white blood cells, hemoglobin, hematocrit, and platelets. The shared data are intended to provide researchers with input to analyze various events associated with the diagnosis of Covid-19 linked to potential diseases identified in the components measured in the CBC test. These data are helpful for pattern analysis of blood components in modeling prediction and clustering problems. The components measured in the complete blood count and CRP together can be helpful for the analysis of different medical conditions using machine learning algorithms.

Ordoñez-Avila R, Parraga-Alava J, Hormaza J Meza, Vaca-Cárdenas L, Portmann E, Terán L, Dorn M

2023-Apr

Ecuador, Hematological data, Machine learning, SARS-Cov-2

Radiology

MEDIMP: Medical Images and Prompts for renal transplant representation learning

ArXiv Preprint

Renal transplantation emerges as the most effective solution for end-stage renal disease. Occurring from complex causes, a substantial risk of transplant chronic dysfunction persists and may lead to graft loss. Medical imaging plays a substantial role in renal transplant monitoring in clinical practice. However, graft supervision is multi-disciplinary, notably joining nephrology, urology, and radiology, while identifying robust biomarkers from such high-dimensional and complex data for prognosis is challenging. In this work, taking inspiration from the recent success of Large Language Models (LLMs), we propose MEDIMP -- Medical Images and Prompts -- a model to learn meaningful multi-modal representations of renal transplant Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE MRI) by incorporating structural clinicobiological data after translating them into text prompts. MEDIMP is based on contrastive learning from joint text-image paired embeddings to perform this challenging task. Moreover, we propose a framework that generates medical prompts using automatic textual data augmentations from LLMs. Our goal is to learn meaningful manifolds of renal transplant DCE MRI, interesting for the prognosis of the transplant or patient status (2, 3, and 4 years after the transplant), fully exploiting the available multi-modal data in the most efficient way. Extensive experiments and comparisons with other renal transplant representation learning methods with limited data prove the effectiveness of MEDIMP in a relevant clinical setting, giving new directions toward medical prompts. Our code is available at https://github.com/leomlck/MEDIMP.

Leo Milecki, Vicky Kalogeiton, Sylvain Bodard, Dany Anglicheau, Jean-Michel Correas, Marc-Olivier Timsit, Maria Vakalopoulou

2023-03-22

Public Health

Splicing signature database development to delineate cancer pathways using literature mining and transcriptome machine learning.

In Computational and structural biotechnology journal

Alternative splicing (AS) events modulate certain pathways and phenotypic plasticity in cancer. Although previous studies have computationally analyzed splicing events, it is still a challenge to uncover biological functions induced by reliable AS events from tremendous candidates. To provide essential splicing event signatures to assess pathway regulation, we developed a database by collecting two datasets: (i) reported literature and (ii) cancer transcriptome profile. The former includes knowledge-based splicing signatures collected from 63,229 PubMed abstracts using natural language processing, extracted for 202 pathways. The latter is the machine learning-based splicing signatures identified from pan-cancer transcriptome for 16 cancer types and 42 pathways. We established six different learning models to classify pathway activities from splicing profiles as a learning dataset. Top-ranked AS events by learning model feature importance became the signature for each pathway. To validate our learning results, we performed evaluations by (i) performance metrics, (ii) differential AS sets acquired from external datasets, and (iii) our knowledge-based signatures. The area under the receiver operating characteristic values of the learning models did not exhibit any drastic difference. However, random-forest distinctly presented the best performance to compare with the AS sets identified from external datasets and our knowledge-based signatures. Therefore, we used the signatures obtained from the random-forest model. Our database provided the clinical characteristics of the AS signatures, including survival test, molecular subtype, and tumor microenvironment. The regulation by splicing factors was additionally investigated. Our database for developed signatures supported retrieval and visualization system.

Lee Kyubin, Hyung Daejin, Cho Soo Young, Yu Namhee, Hong Sewha, Kim Jihyun, Kim Sunshin, Han Ji-Youn, Park Charny

2023

AS, Alternative splicing, AUCPR, the area under the precision-recall curve, AUROC, the area under the receiver operating characteristic, Alternative splicing, DAS, differential alternative splicing, Database, EMT, epithelial mesenchymal transition, Gene signature, ML, machine learning, Machine-learning, NER, named entity recognition, NLP, natural language process, PCA, principal component analysis, PSI, percent spliced in index, RF, random-forest, SF, splicing factor, TCGA, The Cancer Genome Atlas, Text-mining, Tumor transcriptome

General

A dataset on the physiological state and behavior of drivers in conditionally automated driving.

In Data in brief

This dataset contains data of 346 drivers collected during six experiments conducted in a fixed-base driving simulator. Five studies simulated conditionally automated driving (L3-SAE), and the other one simulated manual driving (L0-SAE). The dataset includes physiological data (electrocardiogram (ECG), electrodermal activity (EDA), and respiration (RESP)), driving and behavioral data (reaction time, steering wheel angle, …), performance data of non-driving-related tasks, and questionnaire responses. Among them, measures from standardized questionnaires were collected, either to control the experimental manipulation of the driver's state, or to measure constructs related to human factors and driving safety (drowsiness, mental workload, affective state, situation awareness, situational trust, user experience). In the provided dataset, some raw data have been processed, notably physiological data from which physiological indicators (or features) have been calculated. The latter can be used as input for machine learning models to predict various states (sleep deprivation, high mental workload, ...) that may be critical for driver safety. Subjective self-reported measures can also be used as ground truth to apply regression techniques. Besides that, statistical analyses can be performed using the dataset, in particular to analyze the situational awareness or the takeover quality of drivers, in different states and different driving scenarios. Overall, this dataset contributes to better understanding and consideration of the driver's state and behavior in conditionally automated driving. In addition, this dataset stimulates and inspires research in the fields of physiological/affective computing and human factors in transportation, and allows companies from the automotive industry to better design adapted human-vehicle interfaces for safe use of automated vehicles on the roads.

Meteier Quentin, Capallera Marine, de Salis Emmanuel, Angelini Leonardo, Carrino Stefano, Widmer Marino, Abou Khaled Omar, Mugellini Elena, Sonderegger Andreas

2023-Apr

Conditionally automated driving, Driver state, Electrocardiogram (ECG), Electrodermal activity (EDA), Physiology, Respiration, Situation awareness (SA), Takeover quality

General

CBCovid19EC: A dataset complete blood count and PCR test for COVID-19 detection in Ecuadorian population.

In Data in brief

In this work, we present the complete blood count data and PCR test results of a population of Ecuadorians from different provinces, primarily residing in the Andean region, especially in Quito. PCR was the standard test to detect Covid-19 during the pandemic since 2020. The data were obtained between March 1st and August 12th, 2021. Segurilab and Previne Salud laboratories performed the tests. The dataset contains about 400 clinical cases. Each patient agreed to participate in the study by sharing the results of their PCR (reverse transcription polymerase chain reaction) tests and CBC (complete blood count). CBC test measured several components and features of the blood, including red blood cells, white blood cells, hemoglobin, hematocrit, and platelets. The shared data are intended to provide researchers with input to analyze various events associated with the diagnosis of Covid-19 linked to potential diseases identified in the components measured in the CBC test. These data are helpful for pattern analysis of blood components in modeling prediction and clustering problems. The components measured in the complete blood count and CRP together can be helpful for the analysis of different medical conditions using machine learning algorithms.

Ordoñez-Avila R, Parraga-Alava J, Hormaza J Meza, Vaca-Cárdenas L, Portmann E, Terán L, Dorn M

2023-Apr

Ecuador, Hematological data, Machine learning, SARS-Cov-2

General

Image dataset of urine test results on petri dishes for deep learning classification.

In Data in brief

Recent advancements in image analysis and interpretation technologies using computer vision techniques have shown potential for novel applications in clinical microbiology laboratories to support task automation aiming for faster and more reliable diagnostics. Deep learning models can be a valuable tool in the screening process, helping technicians spend less time classifying no-growth results and quickly separating the categories of tests that deserve further analysis. In this context, creating datasets with correctly classified images is fundamental for developing and improving such models. Therefore, a dataset of urine test Petri dishes images was collected following a standardized process, with controlled conditions of positioning and lighting. Image acquisition was conducted by applying a hardware chamber equipped with a led lightning source and a smartphone camera with 12 MP resolution. A software application was developed to support image classification and handling. Experienced microbiologists classified the images according to the positive, negative, and uncertain test results. The resulting dataset contains a total of 1500 images and can support the development of deep learning algorithms to classify urine exams according to their microbial growth.

da Silva Gabriel Rodrigues, Rosmaninho Igor Batista, Zancul Eduardo, de Oliveira Vanessa Rita, Francisco Gabriela Rodrigues, Dos Santos Nathamy Fernanda, de Mello Macêdo Karin, da Silva Amauri José, de Lima Érika Knabben, Lemo Mara Elisa Borsato, Maldonado Alessandra, Moura Maria Emilia G, da Silva Flávia Helena, Guimarães Gustavo Stuani

2023-Apr

Computational Vision, Image Classification, Petri Dish, Urine Test Classification

General

Using machine learning prediction models for quality control: a case study from the automotive industry.

In Computational management science

This paper studies a prediction problem using time series data and machine learning algorithms. The case study is related to the quality control of bumper beams in the automotive industry. These parts are milled during the production process, and the locations of the milled holes are subject to strict tolerance limits. Machine learning models are used to predict the location of milled holes in the next beam. By doing so, tolerance violations are detected at an early stage, and the production flow can be improved. A standard neural network, a long short term memory network (LSTM), and random forest algorithms are implemented and trained with historical data, including a time series of previous product measurements. Experiments indicate that all models have similar predictive capabilities with a slight dominance for the LSTM and random forest. The results show that some holes can be predicted with good quality, and the predictions can be used to improve the quality control process. However, other holes show poor results and support the claim that real data problems are challenged by inappropriate information or a lack of relevant information.

Msakni Mohamed Kais, Risan Anders, Schütz Peter

2023

Manufacturing, Neural network, Quality control, Random forest

Ophthalmology

Latest Trends in Retinopathy of Prematurity: Research on Risk Factors, Diagnostic Methods and Therapies.

In International journal of general medicine

Retinopathy of prematurity (ROP) is a vasoproliferative disorder with an imminent risk of blindness, in cases where early diagnosis and treatment are not performed. The doctors' constant motivation to give these fragile beings a chance at life with optimal visual acuity has never stopped, since Terry first described this condition. Thus, throughout time, several specific advancements have been made in the management of ROP. Apart from the most known risk factors, this narrative review brings to light the latest research about new potential risk factors, such as: proteinuria, insulin-like growth factor 1 (IGF-1) and blood transfusions. Digital imaging has revolutionized the management of retinal pathologies, and it is more and more used in identifying and staging ROP, particularly in the disadvantaged regions by the means of telescreening. Moreover, optical coherence tomography (OCT) and automated diagnostic tools based on deep learning offer new perspectives on the ROP diagnosis. The new therapeutical trend based on the use of anti-VEGF agents is increasingly used in the treatment of ROP patients, and recent research sustains the theory according to which these agents do not interfere with the neurodevelopment of premature babies.

Bujoreanu Bezman Laura, Tiutiuca Carmen, Totolici Geanina, Carneciu Nicoleta, Bujoreanu Florin Ciprian, Ciortea Diana Andreea, Niculet Elena, Fulga Ana, Alexandru Anamaria Madalina, Stan Daniela Jicman, Nechita Aurel

2023

anti-VEGF, artificial intelligence, optical coherence tomography, retinopathy of prematurity, risk factors, telescreening

Radiology

Assessment of a combined musculoskeletal and chest deep learning-based detection solution in an emergency setting.

In European journal of radiology open

RATIONALE AND OBJECTIVES : Triage and diagnostic deep learning-based support solutions have started to take hold in everyday emergency radiology practice with the hope of alleviating workflows. Although previous works had proven that artificial intelligence (AI) may increase radiologist and/or emergency physician reading performances, they were restricted to finding, bodypart and/or age subgroups, without evaluating a routine emergency workflow composed of chest and musculoskeletal adult and pediatric cases. We aimed at evaluating a multiple musculoskeletal and chest radiographic findings deep learning-based commercial solution on an adult and pediatric emergency workflow, focusing on discrepancies between emergency and radiology physicians.

MATERIAL AND METHODS : This retrospective, monocentric and observational study included 1772 patients who underwent an emergency radiograph between July and October 2020, excluding spine, skull and plain abdomen procedures. Emergency and radiology reports, obtained without AI as part of the clinical workflow, were collected and discordant cases were reviewed to obtain the radiology reference standard. Case-level AI outputs and emergency reports were compared to the reference standard. DeLong and Wald tests were used to compare ROC-AUC and Sensitivity/Specificity, respectively.

RESULTS : Results showed an overall AI ROC-AUC of 0.954 with no difference across age or body part subgroups. Real-life emergency physicians' sensitivity was 93.7 %, not significantly different to the AI model (P = 0.105), however in 172/1772 (9.7 %) cases misdiagnosed by emergency physicians. In this subset, AI accuracy was 90.1 %.

CONCLUSION : This study highlighted that multiple findings AI solution for emergency radiographs is efficient and complementary to emergency physicians, and could help reduce misdiagnosis in the absence of immediate radiological expertize.

Parpaleix Alexandre, Parsy Clémence, Cordari Marina, Mejdoubi Mehdi

2023

Add-on, Chest, Deep learning, Emergency, Musculoskeletal, Xray

General

Chemical rules for optimization of chemical mutagenicity via matched molecular pairs analysis and machine learning methods.

In Journal of cheminformatics

Chemical mutagenicity is a serious issue that needs to be addressed in early drug discovery. Over a long period of time, medicinal chemists have manually summarized a series of empirical rules for the optimization of chemical mutagenicity. However, given the rising amount of data, it is getting more difficult for medicinal chemists to identify more comprehensive chemical rules behind the biochemical data. Herein, we integrated a large Ames mutagenicity data set with 8576 compounds to derive mutagenicity transformation rules for reversing Ames mutagenicity via matched molecular pairs analysis. A well-trained consensus model with a reasonable applicability domain was constructed, which showed favorable performance in the external validation set with an accuracy of 0.815. The model was used to assess the generalizability and validity of these mutagenicity transformation rules. The results demonstrated that these rules were of great value and could provide inspiration for the structural modifications of compounds with potential mutagenic effects. We also found that the local chemical environment of the attachment points of rules was critical for successful transformation. To facilitate the use of these mutagenicity transformation rules, we integrated them into ADMETopt2 ( http://lmmd.ecust.edu.cn/admetsar2/admetopt2/ ), a free web server for optimization of chemical ADMET properties. The above-mentioned approach would be extended to the optimization of other toxicity endpoints.

Lou Chaofeng, Yang Hongbin, Deng Hua, Huang Mengting, Li Weihua, Liu Guixia, Lee Philip W, Tang Yun

2023-Mar-20

Consensus model, Lead optimization, Machine learning, Matched molecular pairs analysis, Mutagenicity optimization

Surgery

Transcriptomes of the tumor-adjacent normal tissues are more informative than tumors in predicting recurrence in colorectal cancer patients.

In Journal of translational medicine

BACKGROUND : Previous investigations of transcriptomic signatures of cancer patient survival and post-therapy relapse have focused on tumor tissue. In contrast, here we show that in colorectal cancer (CRC) transcriptomes derived from normal tissues adjacent to tumors (NATs) are better predictors of relapse.

RESULTS : Using the transcriptomes of paired tumor and NAT specimens from 80 Korean CRC patients retrospectively determined to be in recurrence or nonrecurrence states, we found that, when comparing recurrent with nonrecurrent samples, NATs exhibit a greater number of differentially expressed genes (DEGs) than tumors. Training two prognostic elastic net-based machine learning models-NAT-based and tumor-based in our Samsung Medical Center (SMC) cohort, we found that NAT-based model performed better in predicting the survival when the model was applied to the tumor-derived transcriptomes of an independent cohort of 450 COAD patients in TCGA. Furthermore, compositions of tumor-infiltrating immune cells in NATs were found to have better prognostic capability than in tumors. We also confirmed through Cox regression analysis that in both SMC-CRC as well as in TCGA-COAD cohorts, a greater proportion of genes exhibited significant hazard ratio when NAT-derived transcriptome was used compared to when tumor-derived transcriptome was used.

CONCLUSIONS : Taken together, our results strongly suggest that NAT-derived transcriptomes and immune cell composition of CRC are better predictors of patient survival and tumor recurrence than the primary tumor.

Kim Jinho, Kim Hyunjung, Lee Min-Seok, Lee Heetak, Kim Yeon Jeong, Lee Woo Yong, Yun Seong Hyeon, Kim Hee Cheol, Hong Hye Kyung, Hannenhalli Sridhar, Cho Yong Beom, Park Donghyun, Choi Sun Shim

2023-Mar-21

Colorectal cancer, Elastic net-based machine learning, Normal tissues adjacent to tumors, Recurrence, Tumor-infiltrating immune cells