Exploring the clinical significance of employing deep learning methodologies on ultrasound images for the development of an automated model to accurately identify pleomorphic adenomas and Warthin tumors in salivary glands. A retrospective study was c...
RATIONALE AND OBJECTIVES: Adolescent major depressive disorder (MDD) is a serious mental health condition that has been linked to abnormal functional connectivity (FC) patterns within the brain. However, whether FC could be used as a potential biomar...
PURPOSE: This study aimed to measure the impact of a community-based lifestyle modification intervention program on the Health-Related Quality of Life (HRQoL) of adults with prediabetes in two Latin American cities.
BACKGROUND: Ovarian cancer (OC), owing to its substantial heterogeneity and high invasiveness, has historically been devoid of precise, individualized treatment options. This study aimed to establish integrated consensus subtypes of OC using differen...
In recent years, machine learning-based handwriting analysis has emerged as a valuable tool for supporting the early diagnosis of Alzheimer's disease and predicting its progression. Traditional approaches represent handwriting tasks using a single fe...
This paper introduces a novel convolutional neural network model with an attention mechanism to advance Alzheimer disease (AD) classification using Magnetic Resonance Imaging (MRI). The model architecture is meticulously crafted to enhance feature ex...
OBJECTIVE: The study developed an intelligent online evaluation system for mediolateral episiotomy, which incorporated machine learning algorithms and integrated maternal physiological data collected during delivery.
BACKGROUND: Osteoporosis has become a significant public health concern that necessitates the application of appropriate techniques to calculate disease risk. Traditional methods, such as logistic regression,have been widely used to identify risk fac...
Although discrimination is typically believed to occur from well-defined categories like ethnicity, disability, and sex, studies have found that discrimination persists in minimal conditions lacking such categories. Participants have been found to pr...
BACKGROUND: Arteriovenous fistula stenosis is a common complication in hemodialysis patients, yet effective predictive tools are lacking. This study aims to develop an interpretable machine learning model for stenosis risk prediction.
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