Browsing by Author "Alrumayh, Abdullah Ali"

Now showing 1 - 1 of 1

Restricted
Artificial intelligence for the detection and longitudinal monitoring of cardiovascular diseases
(Saudi Digital Library, 2025) Alrumayh, Abdullah Ali; Peters, Nicholas; Bächtiger, Patrik
Heart failure (HF) remains a major global health burden, contributing to substantial morbidity, mortality, and healthcare utilisation. Despite advances in cardiovascular care, early detection, accurate risk stratification, and long-term monitoring remain key challenges. Digital health technologies, particularly artificial intelligence (AI)-enabled diagnostics, offer potential solutions. AI-enhanced electrocardiography (AI-ECG) has shown promise in detecting left ventricular dysfunction (LVEF ≤40%), with potential applications beyond simple disease classification. This PhD systematically evaluates a single, pre-existing AI-ECG algorithm, focusing on its longitudinal prognostic value, risk assessment capacity, feasibility for self-administered remote monitoring (RM), and detection accuracy across diverse cardiovascular populations, with the overarching aim of bridging the gap between AI innovation and clinical implementation. Prospective multicenter studies assessed AI-ECG across clinical pathways. In newly diagnosed HF with reduced ejection fraction (HFrEF), AI-ECG probability scores correlated with LVEF trajectory and recovery, with each 10% increase in score associated with a shorter time to 10% LVEF improvement (adjusted hazard ratio [aHR] 1.71; p<0.001), supporting its role as a digital biomarker. AI-ECG-predicted LV dysfunction independently predicted major adverse cardiovascular events and all-cause mortality (aHR 1.93 and 1.56), even in patients with preserved LVEF. RM feasibility was demonstrated over 12 months, with 2,600 patient-collected ECGs and a real-time signal quality indicator enhancing engagement and data quality. Detection reliability was highest when ECG and LVEF were concurrent (AUROC 0.77), declining at 30 days (AUROC 0.62; p=0.0425), with superior performance in severe dysfunction versus borderline cases. External validation confirmed robust detection in newly diagnosed HFrEF (AUROC 0.86). Ongoing studies aim to expand AI-ECG’s applicability in chronic and acute cardiovascular care, tracking function, predicting complications, and optimizing treatments. In conclusion, this PhD advances AI-ECG as a tool for HF detection, risk stratification, and RM. Future work should prioritize large-scale validation, explainability, and integration strategies to ensure seamless adoption in clinical workflows.
9 0