AI-guided models trained on ECGs, chest X-rays, and coronary artery calcium scans demonstrate high accuracy in diagnosing structural and valvular heart disease.
Do AI-guided tools improve the screening and diagnosis of structural and valvular heart disease?
AI-guided models show high accuracy for screening structural and valvular heart disease using routine tests like ECGs and chest X-rays, though clinical outcome data is still needed.
PURPOSE OF REVIEW: Structural heart disease (SHD) encompasses diseases involving the heart valves, chambers, walls, and muscles. Current diagnostic methods have limited accessibility and predictive value. This review aims to present recent advances in artificial intelligence (AI)-guided tools in the screening of SHD and valvular heart disease (VHD), and to present challenges and opportunities for their use in clinical practice. RECENT FINDINGS: AI-guided models trained on ECGs, chest X-rays, and coronary artery calcium scans have a high accuracy in the diagnosis of SHD, heart failure, low left ventricular ejection fraction, and VHD. Some of these models can highlight the signals that influence their predictions, improving explainability. The use of AI in screening for SHD and VHD could lead to earlier diagnosis, enhanced accuracy, and better accessibility. However, outcome data on earlier diagnosis using these tools is required before broad deployment.
Abbaoui et al. (2026) conducted a review in Structural and Valvular Heart Disease. Artificial intelligence (AI)-guided tools was evaluated. AI-guided models trained on ECGs, chest X-rays, and coronary artery calcium scans demonstrate high accuracy in diagnosing structural and valvular heart disease.