Coronary artery disease (CAD) remains a leading cause of morbidity and mortality worldwide. Echocardiography is widely available and provides real-time structural and functional assessment, but diagnostic accuracy is limited by operator dependency. Machine learning (ML) and deep learning (DL) approaches have emerged as promising tools to enhance echocardiographic interpretation. However, evidence remains fragmented, and no comprehensive synthesis exists focusing exclusively on ML-enhanced echocardiography for CAD detection. This protocol outlines a scoping review to map the available evidence.
This scoping review seeks to thoroughly map and synthesise the evidence on HIV viral rebound among people living with HIV receiving antiretroviral medication (ART) in Africa. The review will be conducted using the Joanna Briggs Institute (JBI) methodology for scoping reviews and…
Background: Timely identification of Educational Support Needs (NEAE / Special Educational Needs) during early developmental stages (ages 3 to 12) is decisive for preventing learning gaps and optimizing inclusive school pathways. However, traditional support models operate predom…
This prospective update will evaluate peer-reviewed studies in which machine-learning or related data-driven inference methods prioritize therapeutic targets for human disease and the prioritized targets undergo independent experimental validation. The update will use expanded mu…
This repository contains a 12-file subset of the CHB-MIT Scalp EEG Database (PhysioNet) used to evaluate adaptive noise filtering algorithms and machine learning classification for ambulatory EEG signal processing. The dataset includes 12 pre-packaged '.edf' files spanning 6 subj…
Background: Clinical handover (handoff) is the structured transfer of patient information, professional responsibility, and accountability between healthcare providers during transitions of care. Ineffective handovers contribute substantially to communication failures, medical er…
This project contains supplementary materials for a systematic review of paired train-on-synthetic–test-on-real evaluations of clinical machine learning models trained on synthetic electronic health records.