Artificial intelligence and machine learning models improve diagnostic accuracy for infective endocarditis across clinical scoring, echocardiography, and biomarker profiling compared to conventional methods.
Do artificial intelligence and machine learning applications improve the diagnosis and risk stratification of infective endocarditis compared to conventional clinical pathways?
Artificial intelligence and machine learning offer promising diagnostic adjuncts for infective endocarditis by improving risk stratification and imaging interpretation, though prospective validation is required before clinical integration.
Background: Infective endocarditis (IE) is a life-threatening infection of the cardiac endocardium affecting one or more heart valves, with in-hospital mortality of 15–30% and universally fatal if untreated. 1,2 IE may be acute, most commonly caused by Staphylococcus aureus with rapid valvular destruction, or subacute, typically caused by viridans streptococci in patients with pre-existing valvular disease, congenital heart defects, or prosthetic valves. 1 Diagnosis is guided by the 2023 Duke-ISCVID criteria, yet significant diagnostic gaps persist, particularly in prosthetic valve endocarditis (PVE). 3 Artificial intelligence (AI) and machine learning (ML) offer transformative potential to address these limitations across multiple diagnostic domains. Methods: A narrative review of literature published between 2019 and 2025 was conducted using PubMed, Scopus, and EMBASE. Results: ML-based models including SABIER and SYSUPMIE outperform conventional clinical scoring. 4,5 AI-enhanced echocardiography and FDG-PET/CT improve sensitivity and specificity for vegetation detection and PVE. 6,7 ML-identified biomarkers IL-15 and CCL4 predict IE mortality with 91% accuracy. 8 Large language models (LLMs) demonstrate early promise in clinical decision support. 9 Conclusion: AI is a promising diagnostic adjunct in IE. Prospective validation and regulatory frameworks are essential before routine clinical integration.
Fathima Samreen (Wed,) conducted a review in Infective endocarditis. Artificial intelligence and machine learning vs. Conventional diagnostic pathways and clinical scoring was evaluated. Artificial intelligence and machine learning models improve diagnostic accuracy for infective endocarditis across clinical scoring, echocardiography, and biomarker profiling compared to conventional methods.