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Prediction of poststroke independent walking using machine learning: a retrospective study.
BMC neurology
Sep 10, 2024
BACKGROUND: Accurately predicting the walking independence of stroke patients is important. Our objective was to determine and compare the performance of logistic regression (LR) and three machine learning models (eXtreme Gradient Boosting (XGBoost),...
Predictive etiological classification of acute ischemic stroke through interpretable machine learning algorithms: a multicenter, prospective cohort study.
BMC medical research methodology
Sep 10, 2024
BACKGROUND: The prognosis, recurrence rates, and secondary prevention strategies varied significantly among different subtypes of acute ischemic stroke (AIS). Machine learning (ML) techniques can uncover intricate, non-linear relationships within med...
Comorbidity-based framework for Alzheimer's disease classification using graph neural networks.
Scientific reports
Sep 10, 2024
Alzheimer's disease (AD), the most prevalent form of dementia, requires early prediction for timely intervention. Current deep learning approaches, particularly those using traditional neural networks, face challenges such as handling high-dimensiona...
Subject-level spinal osteoporotic fracture prediction combining deep learning vertebral outputs and limited demographic data.
Archives of osteoporosis
Sep 10, 2024
UNLABELLED: Automated screening for vertebral fractures could improve outcomes. We achieved an AUC-ROC = 0.968 for the prediction of moderate to severe fracture using a GAM with age and three maximal vertebral body scores of fracture from a convoluti...
Efficacy of compressed sensing and deep learning reconstruction for adult female pelvic MRI at 1.5 T.
European radiology experimental
Sep 10, 2024
BACKGROUND: We aimed to determine the capabilities of compressed sensing (CS) and deep learning reconstruction (DLR) with those of conventional parallel imaging (PI) for improving image quality while reducing examination time on female pelvic 1.5-T m...
Predicting Late Gadolinium Enhancement of Acute Myocardial Infarction in Contrast-Free Cardiac Cine MRI Using Deep Generative Learning.
Circulation. Cardiovascular imaging
Sep 10, 2024
BACKGROUND: Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) is a standard technique for diagnosing myocardial infarction (MI), which, however, poses risks due to gadolinium contrast usage. Techniques enabling MI assessment based on...
Artificial intelligence system for identification of overlooked lung metastasis in abdominopelvic computed tomography scans of patients with malignancy.
Diagnostic and interventional radiology (Ankara, Turkey)
Sep 9, 2024
PURPOSE: This study aimed to evaluate whether an artificial intelligence (AI) system can identify basal lung metastatic nodules examined using abdominopelvic computed tomography (CT) that were initially overlooked by radiologists.
Machine learning methods to discover hidden patterns in well-being and resilience for healthy aging.
Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing
Sep 9, 2024
BACKGROUND: A whole person approach to healthy aging can provide insight into social factors that may be critical. Digital technologies, such as mobile health (mHealth) applications, hold promise to provide novel insights for healthy aging and the ab...
Artificial intelligence-based quantification of pulmonary HRCT (AIqpHRCT) for the evaluation of interstitial lung disease in patients with inflammatory rheumatic diseases.
Rheumatology international
Sep 9, 2024
High-resolution computed tomography (HRCT) is important for diagnosing interstitial lung disease (ILD) in inflammatory rheumatic disease (IRD) patients. However, visual ILD assessment via HRCT often has high inter-reader variability. Artificial intel...
Prediction of depressive symptoms severity based on sleep quality, anxiety, and gray matter volume: a generalizable machine learning approach across three datasets.
EBioMedicine
Sep 9, 2024
BACKGROUND: Depressive symptoms are rising in the general population, but their associated factors are unclear. Although the link between sleep disturbances and depressive symptoms severity (DSS) is reported, the predictive role of sleep on DSS and t...
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