TL;DR
ML models trained on conventional echocardiographic parameters retained moderate predictive ability for reduced GLS upon external validation, and performance was generally preserved in the external validation cohort.
Objective Conventional left ventricular ejection fraction (LVEF) often fails to detect early sepsis-induced myocardial dysfunction (SIMD). This study aimed to evaluate the prognostic utility of speckle-tracking echocardiography (STE)-derived global longitudinal strain (GLS) and to develop an internally validated clinic...
Objective To develop and externally validate an interpretable machine-learning framework for estimating ultrasound-defined carotid plaque vulnerability among patients with type 2 diabetes mellitus (T2DM) and established carotid plaque. Methods In total, 884 T2DM patients with carotid atherosclerotic plaques from two me...
Background: Machine learning (ML) has emerged as a promising approach for preoperative cardiovascular risk prediction; however, the generalizability of ML models across institutions remains uncertain. Moreover, comprehensive head-to-head comparisons between ML algorithms and established clinical risk scores for predict...
This study successfully established a comprehensive prediction model incorporating clinical and imaging variables, which can accurately identify DMD patients at risk for short-term LVEF decline, and demonstrated excellent discrimination.
An interpretable XGBoost-based ML model that facilitates early risk stratification and targeted interventions for CSVD is validated, readily transferable to resource-limited settings and on embedding the nomogram into electronic-health-record decision support.
Aims To characterize cardiac magnetic resonance (CMR)-derived phenotypes in a population-based cohort and to evaluate their incremental prognostic value for major adverse cardiovascular events (MACE) beyond traditional risk factors. Methods Participants from the UK Biobank imaging cohort without prior cardiovascular di...
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