BACKGROUND: This study develops a deep learning-based automated lesion segmentation model for whole-body 3DF-fluorodeoxyglucose (FDG)-Position emission tomography (PET) with computed tomography (CT) images agnostic to disease location and site.
PURPOSE: We aims to assessed and compare four deep learning(DL) models using non-contrast-enhanced magnetic resonance imaging(MRI) to differentiate benign from malignant ovarian tumors, considering diagnostic efficacy and associated development costs...
Journal of mental health (Abingdon, England)
Mar 20, 2025
BACKGROUND: Adolescents with depression are at heightened risk of suicide, with a distinct sex difference in suicidal behaviour observed. This study explores the sex-specific factors influencing suicide attempts among Chinese adolescents with depress...
Concerns about the generalizability of machine learning models in mental health arise, partly due to sampling effects and data disparities between research cohorts and real-world populations. We aimed to investigate whether a machine learning model t...
Clinical chemistry and laboratory medicine
Mar 19, 2025
OBJECTIVES: Human tear analysis holds promise for biomarker discovery, but its clinical utility is hindered by the lack of standardized reference values, limiting interindividual comparisons. This study aimed at developing a protocol for normalizing ...
Research with controlled or crossover designs in animal-assisted therapy have largely used control groups receiving no treatment or treatment as usual, which can potentially inflate the effects of these interventions. It is therefore not always clear...
INTRODUCTION: Autistic people experience higher risk of suicidal ideation (SI) and suicide attempts (SA) compared to non-autistic people, yet there is limited understanding of complex, multilevel factors that drive this disparity. Further, determinan...
Diagnostic and interventional radiology (Ankara, Turkey)
Mar 17, 2025
PURPOSE: Established methods for bone age assessment (BAA), such as the Greulich and Pyle atlas, suffer from variability due to population differences and observer discrepancies. Although automated BAA offers speed and consistency, limited research e...
International journal of oral and maxillofacial surgery
Mar 17, 2025
The aim of this study was to use machine learning (ML) to classify sagittal and vertical skeletal discrepancies in three-dimensional (3D) facial scans, as well as to evaluate shape variability. 3D facial scans from 435 pre-orthodontic patients were s...
Revista da Associacao Medica Brasileira (1992)
Mar 17, 2025
OBJECTIVE: Obesity is a global health problem. The aim is to analyze the effectiveness of machine learning models in predicting obesity classes and to determine which model performs best in obesity classification.
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