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General General

NeuroSuites: An online platform for running neuroscience, statistical, and machine learning tools.

In Frontiers in neuroinformatics

Nowadays, an enormous amount of high dimensional data is available in the field of neuroscience. Handling these data is complex and requires the use of efficient tools to transform them into useful knowledge. In this work we present NeuroSuites, an easy-access web platform with its own architecture. We compare our platform with other software currently available, highlighting its main strengths. Thanks to its defined architecture, it is able to handle large-scale problems common in some neuroscience fields. NeuroSuites has different neuroscience-oriented applications and tools to integrate statistical data analysis and machine learning algorithms commonly used in this field. As future work, we want to further expand the list of available software tools as well as improve the platform interface according to user demands.

Moreno-Rodríguez José Luis, Larrañaga Pedro, Bielza Concha

2023

Bayesian networks, machine learning, neuroscience, statistical analysis, supervised classification, unsupervised classification, web application

Radiology Radiology

Deep-learning based quantification model for hip bone marrow edema and synovitis in patients with spondyloarthritis based on magnetic resonance images.

In Frontiers in physiology

Objectives: Hip inflammation is one of the most common complications in patients with spondyloarthritis (SpA). Herein, we employed use of a deep learning-based magnetic resonance imaging (MRI) evaluation model to identify irregular and multiple inflammatory lesions of the hip. Methods: All of the SpA patients were enrolled at the Xijing Hospital. The erythrocyte sediment rate (ESR), C-reactive protein (CRP), hip function Harris score, and disease activity were evaluated by clinicians. Manual MRI annotations including bone marrow edema (BME) and effusion/synovitis, and a hip MRI scoring system (HIMRISS) assessment was performed by experienced musculoskeletal radiologists. The segmentation accuracies of four deep learning models, including U-Net, UNet++, Attention-Unet, and HRNet, were compared using five-fold cross-validation. The clinical agreement of U-Net was evaluated with clinical symptoms and HIMRISS results. Results: A total of 1945 MRI slices of STIR/T2WI sequences were obtained from 195 SpA patients with hip involvement. After the five-fold cross-validation, U-Net achieved an average segmentation accuracy of 88.48% for the femoral head and 69.36% for inflammatory lesions, which are higher than those obtained by the other three models. The UNet-score, which was calculated based on the same MRI slices as HIMRISS, was significantly correlated with the HIMRISS scores and disease activity indexes (p values <0.05). Conclusion: This deep-learning based automatic MRI evaluation model could achieve similar quantification performance as an expert radiologist, and it has the potential to improve the accuracy and efficiency of clinical diagnosis for SpA patients with hip involvement.

Zheng Yan, Bai Chao, Zhang Kui, Han Qing, Guan Qingbiao, Liu Ying, Zheng Zhaohui, Xia Yong, Zhu Ping

2023

deep learning, hip, magnetic resonance imaging, spondyloarthritis, synovitis

Public Health Public Health

Computerization of the Work of General Practitioners: Mixed Methods Survey of Final-Year Medical Students in Ireland.

In JMIR medical education

BACKGROUND : The potential for digital health technologies, including machine learning (ML)-enabled tools, to disrupt the medical profession is the subject of ongoing debate within biomedical informatics.

OBJECTIVE : We aimed to describe the opinions of final-year medical students in Ireland regarding the potential of future technology to replace or work alongside general practitioners (GPs) in performing key tasks.

METHODS : Between March 2019 and April 2020, using a convenience sample, we conducted a mixed methods paper-based survey of final-year medical students. The survey was administered at 4 out of 7 medical schools in Ireland across each of the 4 provinces in the country. Quantitative data were analyzed using descriptive statistics and nonparametric tests. We used thematic content analysis to investigate free-text responses.

RESULTS : In total, 43.1% (252/585) of the final-year students at 3 medical schools responded, and data collection at 1 medical school was terminated due to disruptions associated with the COVID-19 pandemic. With regard to forecasting the potential impact of artificial intelligence (AI)/ML on primary care 25 years from now, around half (127/246, 51.6%) of all surveyed students believed the work of GPs will change minimally or not at all. Notably, students who did not intend to enter primary care predicted that AI/ML will have a great impact on the work of GPs.

CONCLUSIONS : We caution that without a firm curricular foundation on advances in AI/ML, students may rely on extreme perspectives involving self-preserving optimism biases that demote the impact of advances in technology on primary care on the one hand and technohype on the other. Ultimately, these biases may lead to negative consequences in health care. Improvements in medical education could help prepare tomorrow's doctors to optimize and lead the ethical and evidence-based implementation of AI/ML-enabled tools in medicine for enhancing the care of tomorrow's patients.

Blease Charlotte, Kharko Anna, Bernstein Michael, Bradley Colin, Houston Muiris, Walsh Ian, D Mandl Kenneth

2023-Mar-20

COVID-19, artificial intelligence, biomedical, design, digital health, general practitioners, machine learning, medical education, medical professional, medical students, survey, technology, tool

Surgery Surgery

Improving Resource Utilization for Arthroplasty Care by Leveraging Machine Learning and Optimization: A Systematic Review.

In Arthroplasty today

BACKGROUND : There is a growing demand for total joint arthroplasty (TJA) surgery. The applications of machine learning (ML), mathematical optimization, and computer simulation have the potential to improve efficiency of TJA care delivery through outcome prediction and surgical scheduling optimization, easing the burden on health-care systems. The purpose of this study was to evaluate strategies using advances in analytics and computational modeling that may improve planning and the overall efficiency of TJA care.

METHODS : A systematic review including MEDLINE, Embase, and IEEE Xplore databases was completed from inception to October 3, 2022, for identification of studies generating ML models for TJA length of stay, duration of surgery, and hospital readmission prediction. A scoping review of optimization strategies in elective surgical scheduling was also conducted.

RESULTS : Twenty studies were included for evaluating ML predictions and 17 in the scoping review of scheduling optimization. Among studies generating linear or logistic control models alongside ML models, only 1 found a control model to outperform its ML counterpart. Furthermore, neural networks performed superior to or at the same level as conventional ML models in all but 1 study. Implementation of mathematical and simulation strategies improved the optimization efficiency when compared to traditional scheduling methods at the operational level.

CONCLUSIONS : High-performing predictive ML-based models have been developed for TJA, as have mathematical strategies for elective surgical scheduling optimization. By leveraging artificial intelligence for outcome prediction and surgical optimization, there exist greater opportunities for improved resource utilization and cost-savings in TJA than when using traditional modeling and scheduling methods.

Entezari Bahar, Koucheki Robert, Abbas Aazad, Toor Jay, Wolfstadt Jesse I, Ravi Bheeshma, Whyne Cari, Lex Johnathan R

2023-Apr

Artificial intelligence, Optimization, Predictive modeling, Surgical scheduling, Total hip arthroplasty, Total knee arthroplasty

General General

Interpretation of Depression Detection Models via Feature Selection Methods.

In IEEE transactions on affective computing

Given the prevalence of depression worldwide and its major impact on society, several studies employed artificial intelligence modelling to automatically detect and assess depression. However, interpretation of these models and cues are rarely discussed in detail in the AI community, but have received increased attention lately. In this study, we aim to analyse the commonly selected features using a proposed framework of several feature selection methods and their effect on the classification results, which will provide an interpretation of the depression detection model. The developed framework aggregates and selects the most promising features for modelling depression detection from 38 feature selection algorithms of different categories. Using three real-world depression datasets, 902 behavioural cues were extracted from speech behaviour, speech prosody, eye movement and head pose. To verify the generalisability of the proposed framework, we applied the entire process to depression datasets individually and when combined. The results from the proposed framework showed that speech behaviour features (e.g. pauses) are the most distinctive features of the depression detection model. From the speech prosody modality, the strongest feature groups were F0, HNR, formants, and MFCC, while for the eye activity modality they were left-right eye movement and gaze direction, and for the head modality it was yaw head movement. Modelling depression detection using the selected features (even though there are only 9 features) outperformed using all features in all the individual and combined datasets. Our feature selection framework did not only provide an interpretation of the model, but was also able to produce a higher accuracy of depression detection with a small number of features in varied datasets. This could help to reduce the processing time needed to extract features and creating the model.

Alghowinem Sharifa, Gedeon Tom, Goecke Roland, Cohn Jeffrey F, Parker Gordon

2023

datasets generalisation, depression detection, feature selection, multimodal analysis

General General

Precision recruitment for high-risk participants in a COVID-19 cohort study.

In Contemporary clinical trials communications

BACKGROUND : Studies for developing diagnostics and treatments for infectious diseases usually require observing the onset of infection during the study period. However, when the infection base rate incidence is low, the cohort size required to measure an effect becomes large, and recruitment becomes costly and prolonged. We developed a model for reducing recruiting time and resources in a COVID-19 detection study by targeting recruitment to high-risk individuals.

METHODS : We conducted an observational longitudinal cohort study at individual sites throughout the U.S., enrolling adults who were members of an online health and research platform. Through direct and longitudinal connection with research participants, we applied machine learning techniques to compute individual risk scores from individually permissioned data about socioeconomic and behavioral data, in combination with predicted local prevalence data. The modeled risk scores were then used to target candidates for enrollment in a hypothetical COVID-19 detection study. The main outcome measure was the incidence rate of COVID-19 according to the risk model compared with incidence rates in actual vaccine trials.

RESULTS : When we used risk scores from 66,040 participants to recruit a balanced cohort of participants for a COVID-19 detection study, we obtained a 4- to 7-fold greater COVID-19 infection incidence rate compared with similar real-world study cohorts.

CONCLUSION : This risk model offers the possibility of reducing costs, increasing the power of analyses, and shortening study periods by targeting for recruitment participants at higher risk.

Mezlini Aziz M, Caddigan Eamon, Shapiro Allison, Ramirez Ernesto, Kondow-McConaghy Helena M, Yang Justin, DeMarco Kerry, Naraghi-Arani Pejman, Foschini Luca

2023-Jun

CDC, Centers for Disease Control and Prevention, COVID-19, Clinical trials, GAMs, generalized additive models, Risk modeling