Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

General General

Application of wavelet theory to enhance the performance of machine learning techniques in estimating water quality parameters (case study: Gao-Ping River).

In Water science and technology : a journal of the International Association on Water Pollution Research

There are several methods for modeling water quality parameters, with data-based methods being the focus of research in recent decades. The current study aims to simulate water quality parameters using modern artificial intelligence techniques, to enhance the performance of machine learning techniques using wavelet theory, and to compare these techniques to other widely used machine learning techniques. EC, Cl, Mg, and TDS water quality parameters were modeled using artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). The study area in the present research is Gao-ping River in Taiwan. In the training state, using hybrid models with wavelet transform improved the accuracy of ANN models from 8.1 to 22.5% and from 25.7 to 55.3% in the testing state. In addition, wavelet transforms increased the ANFIS model's accuracy in the training state from 6.7 to 18.4% and in the testing state from 9.9 to 50%. Using wavelet transform improves the accuracy of machine learning model results. Also, the WANFIS (Wavelet-ANFIS) model was superior to the WANN (Wavelet-ANN) model, resulting in more precise modeling for all four water quality parameters.

Chen Tzu-Chia

2023-Mar

General General

Evaluation of boundaries between mood and psychosis disorder using dynamic functional network connectivity (dFNC) via deep learning classification.

In Human brain mapping

The validity and reliability of diagnoses in psychiatry is a challenging topic in mental health. The current mental health categorization is based primarily on symptoms and clinical course and is not biologically validated. Among multiple ongoing efforts, neurological observations alongside clinical evaluations are considered to be potential solutions to address diagnostic problems. The Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP) has published multiple papers attempting to reclassify psychotic illnesses based on biological rather than symptomatic measures. However, the effort to investigate the relationship between this new categorization approach and other neuroimaging techniques, including resting-state fMRI data, is still limited. This study focused on investigating the relationship between different psychotic disorders categorization methods and resting-state fMRI-based measures called dynamic functional network connectivity (dFNC) using state-of-the-art artificial intelligence (AI) approaches. We applied our method to 613 subjects, including individuals with psychosis and healthy controls, which were classified using both the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) and the B-SNIP biomarker-based (Biotype) approach. Statistical group differences and cross-validated classifiers were performed within each framework to assess how different categories. Results highlight interesting differences in occupancy in both DSM-IV and Biotype categorizations compared to healthy individuals, which are distributed across specific transient connectivity states. Biotypes tended to show less distinctiveness in occupancy level and included fewer cellwise differences. Classification accuracy obtained by DSM-IV and Biotype categories were both well above chance. Results provided new insights and highlighted the benefits of both DSM-IV and biology-based categories while also emphasizing the importance of future work in this direction, including employing further data types.

Rokham Hooman, Falakshahi Haleh, Fu Zening, Pearlson Godfrey, Calhoun Vince D

2023-Mar-15

classification, deep learning, dynamic functional connectivity, machine learning, psychosis disorders, resting-state functional MRI

Radiology Radiology

MRI-based deep learning techniques for the prediction of isocitrate dehydrogenase and 1p/19q status in grade 2-4 adult gliomas.

In Journal of medical imaging and radiation oncology

Molecular biomarkers are becoming increasingly important in the classification of intracranial gliomas. While tissue sampling remains the gold standard, there is growing interest in the use of deep learning (DL) techniques to predict these markers. This narrative review with a systematic approach identifies and synthesises the current published data on DL techniques using conventional MRI sequences for predicting isocitrate dehydrogenase (IDH) and 1p/19q-codeletion status in World Health Organisation grade 2-4 gliomas. Three databases were searched for relevant studies. In all, 13 studies met the inclusion criteria after exclusions. Key results, limitations and discrepancies between studies were synthesised. High accuracy has been reported in some studies, but the existing literature has several limitations, including generally small cohort sizes, a paucity of studies with independent testing cohorts and a lack of studies assessing IDH and 1p/19q together. While DL shows promise as a non-invasive means of predicting glioma genotype, addressing these limitations in future research will be important for facilitating clinical translation.

Kalaroopan Dinusha, Lasocki Arian

2023-Mar-15

1p/19q, glioma, isocitrate dehydrogenase, magnetic resonance imaging, radiogenomics

Radiology Radiology

Heterogeneous brain dynamic functional connectivity patterns in first-episode drug-naive patients with major depressive disorder.

In Human brain mapping

It remains challenging to identify depression accurately due to its biological heterogeneity. As people suffering from depression are associated with functional brain network alterations, we investigated subtypes of patients with first-episode drug-naive (FEDN) depression based on brain network characteristics. This study included data from 91 FEDN patients and 91 matched healthy individuals obtained from the International Big-Data Center for Depression Research. Twenty large-scale functional connectivity networks were computed using group information guided independent component analysis. A multivariate unsupervised normative modeling method was used to identify subtypes of FEDN and their associated networks, focusing on individual-level variability among the patients for quantifying deviations of their brain networks from the normative range. Two patient subtypes were identified with distinctive abnormal functional network patterns, consisting of 10 informative connectivity networks, including the default mode network and frontoparietal network. 16% of patients belonged to subtype I with larger extreme deviations from the normal range and shorter illness duration, while 84% belonged to subtype II with weaker extreme deviations and longer illness duration. Moreover, the structural changes in subtype II patients were more complex than the subtype I patients. Compared with healthy controls, both increased and decreased gray matter (GM) abnormalities were identified in widely distributed brain regions in subtype II patients. In contrast, most abnormalities were decreased GM in subtype I. The informative functional network connectivity patterns gleaned from the imaging data can facilitate the accurate identification of FEDN-MDD subtypes and their associated neurobiological heterogeneity.

Jing Rixing, Lin Xiao, Ding Zengbo, Chang Suhua, Shi Le, Liu Lin, Wang Qiandong, Si Juanning, Yu Mingxin, Zhuo Chuanjun, Shi Jie, Li Peng, Fan Yong, Lu Lin

2023-Mar-15

dynamic functional connectivity pattern, first-episode drug-naive with major depressive disorder, heterogeneity, machine learning, normative model

General General

Artificial Intelligence in Medical Imaging for Cholangiocarcinoma Diagnosis: A Systematic Review with Scientometric Analysis.

In Journal of gastroenterology and hepatology ; h5-index 51.0

INTRODUCTION : Artificial intelligence (AI), by means of computer vision in machine learning, is a promising tool for cholangiocarcinoma (CCA) diagnosis. The aim of this study was to provide a comprehensive overview of AI in medical imaging for CCA diagnosis.

METHODS : A systematic review with scientometric analysis was conducted to analyze and visualize the state-of-the-art of medical imaging to diagnosis CCA.

RESULTS : Fifty relevant articles, published by 232 authors and affiliated with 68 organizations and 10 countries, were reviewed in depth. The country with the highest number of publications was China, followed by the United States. Collaboration was noted for 51 (22.0%) of the 232 authors forming 5 clusters. Deep learning algorithms with convolutional neural networks (CNN) were the most frequently used classifiers. The highest performance metrics were observed with CNN-cholangioscopy for diagnosis of extrahepatic CCA (accuracy 94.9%; sensitivity 94.7%; and specificity 92.1%). However, some of the values for CNN in CT imaging for diagnosis of intrahepatic CCA were low (AUC 0.72 and sensitivity 44%).

CONCLUSION : Our results suggest that there is increasing evidence to support the role of AI in the diagnosis of CCA. CNN-based computer vision of cholangioscopy images appears to be the most promising modality for extrahepatic CCA diagnosis. Our social network analysis highlighted an Asian and American predominance in the research relational network of AI in CCA diagnosis. This discrepancy presents an opportunity for coordination and increased collaboration, especially with institutions located in high CCA burdened countries.

Njei Basile, Kanmounye Ulrick Sidney, Seto Nancy, McCarty Thomas R, Mohan Babu P, Fozo Lydia, Navaneethan Udayakumar

2023-Mar-14

Artificial Intelligence, Cholangiocarcinoma, Imaging, Scientometrics, Systematic Review

General General

Soft Electronics for Health Monitoring Assisted by Machine Learning.

In Nano-micro letters

Due to the development of the novel materials, the past two decades have witnessed the rapid advances of soft electronics. The soft electronics have huge potential in the physical sign monitoring and health care. One of the important advantages of soft electronics is forming good interface with skin, which can increase the user scale and improve the signal quality. Therefore, it is easy to build the specific dataset, which is important to improve the performance of machine learning algorithm. At the same time, with the assistance of machine learning algorithm, the soft electronics have become more and more intelligent to realize real-time analysis and diagnosis. The soft electronics and machining learning algorithms complement each other very well. It is indubitable that the soft electronics will bring us to a healthier and more intelligent world in the near future. Therefore, in this review, we will give a careful introduction about the new soft material, physiological signal detected by soft devices, and the soft devices assisted by machine learning algorithm. Some soft materials will be discussed such as two-dimensional material, carbon nanotube, nanowire, nanomesh, and hydrogel. Then, soft sensors will be discussed according to the physiological signal types (pulse, respiration, human motion, intraocular pressure, phonation, etc.). After that, the soft electronics assisted by various algorithms will be reviewed, including some classical algorithms and powerful neural network algorithms. Especially, the soft device assisted by neural network will be introduced carefully. Finally, the outlook, challenge, and conclusion of soft system powered by machine learning algorithm will be discussed.

Qiao Yancong, Luo Jinan, Cui Tianrui, Liu Haidong, Tang Hao, Zeng Yingfen, Liu Chang, Li Yuanfang, Jian Jinming, Wu Jingzhi, Tian He, Yang Yi, Ren Tian-Ling, Zhou Jianhua

2023-Mar-15

Machine learning algorithm, Physiological signal monitoring, Soft electronics, Soft materials