AIMC Topic: Humans

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Addressing underestimation and explanation of retinal fundus photo-based cardiovascular disease risk score: Algorithm development and validation.

Computers in biology and medicine
OBJECTIVE: To resolve the underestimation problem and investigate the mechanism of the AI model which employed to predict cardiovascular disease (CVD) risk scores from retinal fundus photos.

Harnessing machine learning for predicting successful weaning from mechanical ventilation: A systematic review.

Australian critical care : official journal of the Confederation of Australian Critical Care Nurses
BACKGROUND: Machine learning (ML) models represent advanced computational approaches with increasing application in predicting successful weaning from mechanical ventilation (MV). Whilst ML itself has a long history, its application to MV weaning out...

Dynamic HRV Monitoring and Machine Learning Predict NYHA Improvements in Acute Heart Failure Patients.

Computers in biology and medicine
Heart failure (HF) is marked by significant morbidity, mortality, and readmission rates, highlighting a critical need for accurate assessment of treatment efficacy. The New York Heart Association (NYHA) classification, while standard, falls short in ...

Predicting the anticancer activity of indole derivatives: A novel GP-tree-based QSAR model optimized by ALO with insights from molecular docking and decision-making methods.

Computers in biology and medicine
Indole derivatives have demonstrated significant potential as anticancer agents; however, the complexity of their structure-activity relationships and the high dimensionality of molecular descriptors present challenges in the drug discovery process. ...

An explainable non-invasive hybrid machine learning framework for accurate prediction of thyroid-stimulating hormone levels.

Computers in biology and medicine
Machine learning models, including thyroid biomarkers, are increasingly utilized in healthcare for biomarker prediction. These models offer the potential to enhance disease diagnosis through data-driven approaches relying on non-invasive techniques. ...

FEGGNN: Feature-Enhanced Gated Graph Neural Network for robust few-shot skin disease classification.

Computers in biology and medicine
Accurate and timely classification of skin diseases is essential for effective dermatological diagnosis. However, the limited availability of annotated images, particularly for rare or novel conditions, poses a significant challenge. Although few-sho...

Deep Radon Prior: A fully unsupervised framework for sparse-view CT reconstruction.

Computers in biology and medicine
BACKGROUND: Sparse-view computed tomography (CT) substantially reduces radiation exposure but often introduces severe artifacts that compromise image fidelity. Recent advances in deep learning for solving inverse problems have shown considerable prom...

Artificial intelligence in stroke rehabilitation: From acute care to long-term recovery.

Neuroscience
Stroke is a leading cause of disability worldwide, driving the need for advanced rehabilitation strategies. The integration of Artificial Intelligence (AI) into stroke rehabilitation presents significant advancements across the continuum of care, fro...

Development and validation of radiomics and deep transfer learning models to assess cognitive impairment in patients with cerebral small vessel disease.

Neuroscience
Cognitive impairment in cerebral small vessel disease (CSVD) progresses subtly but carries significant clinical consequences, necessitating effective diagnostic tools. This study developed and validated predictive models for CSVD-related cognitive im...