Building stronger evidence for AI’s place in cardiology, this week brought several examples of how AI can improve ECG use cases and automate Echo for new disease detection methods.
Starting with AI improving ECG for coronary revascularization, a deep learning ECG model outperformed both clinician-led ECG and troponin T testing in predicting which ED patients needed coronary revascularization.
- The model was trained on 145k U.S. cases and achieved an AUROC of 0.91 vs. 0.65 for clinician reads and 0.71 for TnT.
- External validation in Europe (18k patients) showed strong results too, with an AUROC of 0.81 for revascularization and 0.85 for type 1 MI.
AI could also expand ECG into HF risk prediction as part of a recent JAMA study which found that a positive AI-ECG screening result predicted a potentially seven-fold higher HF risk.
- AI-ECG’s discrimination for new-onset HF was 0.723 in the U.S., 0.736 in the U.K., and 0.828 in Brazil.
- When compared to the standard PCP-HF and PREVENT equations for classifying HF, AI-ECG showed net reclassification improvements of up to 47.2% and 47.5%.
A third study gave another example of how AI can automate echocardiography by using deep-learning to accurately detect clinically significant tricuspid regurgitation from full transthoracic echoes.
- The AI model detected moderate or severe TR with an AUC of 0.95 and severe TR with an AUC of 0.98.
- Researchers now hope this project can serve as the foundation for future AI–assisted Echo workflows in TR patients thanks to the model’s strong accuracy.
The Takeaway
While these studies might not sway all the AI skeptics in healthcare, they’re part of a growing trend that shows how AI can expand the functionality and efficiency of well-established modalities like ECG and Echo.
