aceModerate evidence
Published recordHealthThe Trace
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Machine learning-driven ECG screening for early coronary artery disease detection
- Truvace record
- TRV-2026-2634raw data (JSON)
- Version
- v1Published
- First published
- Evidence type
- Controlled or validated comparison
- Sources
- 1 sourcePeer-reviewed
- sha256
- e873fd8bb698…check it
BackgroundElectrocardiography (ECG) is inexpensive and widely available, yet its ability to detect early and nonobstructive coronary artery disease (CAD) is limited when interpreted using conventional criteria. We evaluated whether a deep learning model applied to standard 12-lead-ECG images can identify angiographically confirmed CAD and enable clinically meaningful risk stratification in a real-world population.MethodsIn this multicentric study, 15,890 ECG-angiography pairs (12-lead ECG paired with coronary an…
58Gain side
How the two sides scoreClose call
60Problem side
The gain
In a multicentric study, a deep learning model reading routine ECG images detected 618 of 708 angiography-confirmed coronary artery disease cases (87% sensitivity) on a 1,339-sample test set, with a ROC-AUC of 0.80.
The problem
In the same multicentric ECG study, the deep learning model correctly classified only 317 of 631 people without coronary artery disease (50% specificity) on the test set, and its sensitivity fell to 0.67 on the later validation cohort of 2,168 samples.
A fixed formula sets both scores from the evidence and its sources. Reader votes don’t move them.
What the scores mean
G is the gain side, out of 100. It goes up when the gain is specific, when it measurably helped people and when the sources behind it are strong.
P is the problem side, out of 100. It goes up when the problem is specific, when it measurably hurt people and when the sources behind it are strong.
What this doesn’t fix
Specificity was only 50% on the test set, and on the temporal validation cohort sensitivity dropped to 0.67 with an NPV of 0.57, so the model's operating balance varied between datasets.
Sources
1 on file- Asian Cardiovascular and Thoracic Annalsdoi:10.1177/02184923261503901
Cite this record
v1Truvace Impact Record TRV-2026-2634, v1: “Machine learning-driven ECG screening for early coronary artery disease detection.” Truvace, 2026-11-12. /record/TRV-2026-2634 (accessed on the day you copy it). sha256 e873fd8bb698783f…
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