AIMC Topic: Longitudinal Studies
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Assessments of lung nodules by an artificial intelligence chatbot using longitudinal CT images.
Cell reports. Medicine
Mar 4, 2025
Large language models have shown efficacy across multiple medical tasks. However, their value in the assessment of longitudinal follow-up computed tomography (CT) images of patients with lung nodules is unclear. In this study, we evaluate the ability...
Biomarker and clinical data-based predictor tool (MAUXI) for ultrafiltration failure and cardiovascular outcome in peritoneal dialysis patients: a retrospective and longitudinal study.
BMJ health & care informatics
Feb 27, 2025
OBJECTIVES: To develop a machine learning-based software as a medical device to predict the endurance and outcomes of peritoneal dialysis (PD) patients in real time using effluent-measured biomarkers of the mesothelial-to-mesenchymal transition (MMT)...
Prediction of sarcopenia at different time intervals: an interpretable machine learning analysis of modifiable factors.
BMC geriatrics
Feb 27, 2025
OBJECTIVES: This study aims to develop sarcopenia risk prediction models for Chinese older adults at different time intervals and to identify and compare modifiable factors contributing to sarcopenia development.
Feature-Based Audiogram Value Estimator (FAVE): Estimating Numerical Thresholds from Scanned Images of Handwritten Audiograms.
Journal of medical systems
Feb 27, 2025
Hearing loss is a public health concern that affects millions of people globally. Clinically, a person's hearing sensitivity is often measured using pure-tone audiometry and visualized as a pure-tone audiogram, a plot of hearing sensitivity as a func...
Urban and rural disparities in stroke prediction using machine learning among Chinese older adults.
Scientific reports
Feb 25, 2025
Stroke is a significant health concern in China. Differences in stroke risk between rural and urban areas have been highlighted in prior research. However, there is a scarcity of studies on urban-rural differences in predicting stroke. This study aim...
Using machine learning to predict poor adherence to antiretroviral therapy among adolescents with HIV in low resource settings.
AIDS (London, England)
Feb 24, 2025
OBJECTIVES: Achieving optimal adherence to antiretroviral therapy (ART) and viral suppression is still insufficient for attaining the UNAIDS 95-95-95 target of 2030, especially among adolescents with HIV (AWHIV). This study sought to develop a model ...
Deep learning to quantify the pace of brain aging in relation to neurocognitive changes.
Proceedings of the National Academy of Sciences of the United States of America
Feb 24, 2025
Brain age (BA), distinct from chronological age (CA), can be estimated from MRIs to evaluate neuroanatomic aging in cognitively normal (CN) individuals. BA, however, is a cross-sectional measure that summarizes cumulative neuroanatomic aging since bi...
Learning-based inference of longitudinal image changes: Applications in embryo development, wound healing, and aging brain.
Proceedings of the National Academy of Sciences of the United States of America
Feb 20, 2025
Longitudinal imaging data are routinely acquired for health studies and patient monitoring. A central goal in longitudinal studies is tracking relevant change over time. Traditional methods remove nuisance variation with custom pipelines to focus on ...
Predicting PTSD development with early post-trauma assessments: a proof-of-concept for a concise tree-based classification method.
European journal of psychotraumatology
Feb 18, 2025
Approximately 70% of individuals globally experience at least one traumatic event in their lifetimes, potentially leading to posttraumatic stress disorder (PTSD). Understanding the development of PTSD and devising effective prevention and treatment ...
Prediction of early-onset bipolar using electronic health records.
Journal of child psychology and psychiatry, and allied disciplines
Feb 18, 2025
BACKGROUND: Early identification of bipolar disorder (BD) provides an important opportunity for timely intervention. In this study, we aimed to develop machine learning models using large-scale electronic health record (EHR) data including clinical n...
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