Emerging Artificial Intelligence Tools for the Screening of Structural and Valvular Heart Disease
Clinical Snapshot
PICO Framework
| P — Population | Patients requiring screening for structural heart disease (SHD) and valvular heart disease (VHD) |
| I — Intervention | AI-guided diagnostic tools using ECGs, chest X-rays, and coronary artery calcium scans |
| C — Comparator | Current standard diagnostic methods |
| O — Outcomes | Diagnostic accuracy, accessibility, predictive value for SHD and VHD detection |
Bottom Line
This narrative review examines emerging AI tools for screening structural and valvular heart disease using ECGs, chest X-rays, and coronary calcium scans. While the authors report high diagnostic accuracy for these technologies, this review lacks the systematic methodology needed for robust evidence synthesis. No quantitative data, meta-analysis, or formal quality assessment is provided. The authors appropriately acknowledge that outcome data demonstrating clinical benefit is required before widespread implementation. For Australian clinicians, while these tools show promise for improving diagnostic accessibility, particularly in underserved areas, regulatory approval and evidence of clinical utility remain prerequisites for adoption. Current evidence is insufficient to recommend practice change.
Key Findings
P Value: Not reported
Effect Size: Not quantified - described as 'high accuracy'
Primary Outcome: High diagnostic accuracy for AI tools in detecting SHD and VHD
Nnt Or Sensitivity: Sensitivity and specificity values not provided
Confidence Interval: Not reported
Clinical Application
Potentially high given use of readily available imaging modalities (ECG, chest X-ray) Could enhance cardiovascular screening in rural/remote areas where specialist access is limited, but TGA approval and PBS funding pathways unclear for AI diagnostic tools Patients requiring cardiovascular screening, particularly in resource-limited settings
Abstract
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.
References
- 1.Abbaoui, Y., Nolin-Lapalme, A., Morisset, J., El Adib, I., Genereux, P., Poterucha, T. J., Elias, P., Yao, X., & Avram, R. (2026). Emerging artificial intelligence tools for the screening of structural and valvular heart disease. Current Heart Failure Reports. https://doi.org/10.1161/CIR.0000000000001201
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