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Transformers for Neuroimage Segmentation: Scoping Review.
J Med Internet Res
January 2025
Department of Computer Science and Software Engineering, United Arab Emirates University, Al Ain, United Arab Emirates.
Background: Neuroimaging segmentation is increasingly important for diagnosing and planning treatments for neurological diseases. Manual segmentation is time-consuming, apart from being prone to human error and variability. Transformers are a promising deep learning approach for automated medical image segmentation.
View Article and Find Full Text PDFEstimating the Prevalence of Schizophrenia in the General Population of Japan Using an Artificial Neural Network-Based Schizophrenia Classifier: Web-Based Cross-Sectional Survey.
JMIR Form Res
January 2025
Department of Public Health, Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake, 470-1192, Japan, 81 562-93-2476, 81 562-93-3079.
Background: Estimating the prevalence of schizophrenia in the general population remains a challenge worldwide, as well as in Japan. Few studies have estimated schizophrenia prevalence in the Japanese population and have often relied on reports from hospitals and self-reported physician diagnoses or typical schizophrenia symptoms. These approaches are likely to underestimate the true prevalence owing to stigma, poor insight, or lack of access to health care among respondents.
View Article and Find Full Text PDFClassification-based pathway analysis using GPNet with novel P-value computation.
Brief Bioinform
November 2024
Center for Artificial Intelligence Research, Wake Forest University School of Medicine, Winston-Salem, NC 27101, United States.
Pathway analysis plays a critical role in bioinformatics, enabling researchers to identify biological pathways associated with various conditions by analyzing gene expression data. However, the rise of large, multi-center datasets has highlighted limitations in traditional methods like Over-Representation Analysis (ORA) and Functional Class Scoring (FCS), which struggle with low signal-to-noise ratios (SNR) and large sample sizes. To tackle these challenges, we use a deep learning-based classification method, Gene PointNet, and a novel $P$-value computation approach leveraging the confusion matrix to address pathway analysis tasks.
View Article and Find Full Text PDFRevolutionizing colorectal cancer detection: A breakthrough in microbiome data analysis.
PLoS One
January 2025
Department of Computer Science, Faculty of Computing, Federal University of Lafia, Lafia, Nasarawa State, Nigeria.
The emergence of Next Generation Sequencing (NGS) technology has catalyzed a paradigm shift in clinical diagnostics and personalized medicine, enabling unprecedented access to high-throughput microbiome data. However, the inherent high dimensionality, noise, and variability of microbiome data present substantial obstacles to conventional statistical methods and machine learning techniques. Even the promising deep learning (DL) methods are not immune to these challenges.
View Article and Find Full Text PDFThe impact of specialised gastroenterology services for pelvic radiation disease (PRD): Results from the prospective multi-centre EAGLE study.
PLoS One
January 2025
Marie Curie Research Centre, Division of Population Medicine, School of Medicine, Cardiff University, Cardiff, United Kingdom.
To undertake a mixed-methodology implementation study to improve the well-being of men with gastrointestinal late effects following radical radiotherapy for prostate cancer. All men completed a validated screening tool for late bowel effects (ALERT-B) and the Gastrointestinal Symptom Rating Score (GSRS); men with a positive score on ALERT-B were offered management following a peer reviewed algorithm for pelvic radiation disease (PRD). Health-related quality of life (HRQoL) at baseline, 6 and 12 months; and healthcare resource usage (HRU) and patient, support-giver, staff experience and acceptability of staff training (qualitative analysis) were assessed.
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