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Emerging Artificial Intelligence Tools for the Screening of Structural and Valvular Heart Disease
Yasmine Abbaoui1,2, Alexis Nolin-Lapalme1,2, Julianne Morisset1,2
1Faculty of Medicine, University of Montreal, Montreal, QC, Canada.
Insights
Artificial intelligence (AI) shows high accuracy in diagnosing structural heart disease (SHD) and valvular heart disease (VHD) using medical scans. While AI offers earlier detection and better accessibility, more outcome data is needed for widespread clinical use.
Area of Science:
- Cardiology
- Medical Imaging
- Artificial Intelligence
Background:
- Structural heart disease (SHD) and valvular heart disease (VHD) affect heart valves, chambers, walls, and muscles.
- Current diagnostic methods for SHD and VHD have limitations in accessibility and predictive accuracy.
Purpose of the Study:
- To review recent advancements in AI-guided tools for screening SHD and VHD.
- To discuss the challenges and opportunities of implementing these AI tools in clinical practice.
Main Methods:
- Review of AI-guided models trained on electrocardiograms (ECGs), chest X-rays, and coronary artery calcium scans.
- Analysis of AI model accuracy in diagnosing SHD, heart failure, low ejection fraction, and VHD.
Main Results:
- AI-guided models demonstrate high accuracy in diagnosing various cardiac conditions, including SHD and VHD.
- Some AI models offer improved explainability by highlighting predictive signals.
- AI screening has the potential for earlier diagnosis, enhanced accuracy, and improved accessibility for SHD and VHD.
Conclusions:
- AI tools show promise for earlier and more accurate screening of structural and valvular heart disease.
- Further clinical outcome data is necessary to support broad deployment of AI in SHD and VHD screening.
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.
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