Key result
Machine learning using single-lead ECG fails to outperform bicycle ergometry in diagnosing ischemic heart disease.
Why the study?
Ischemic heart disease significantly impacts quality of life and causes high global mortality, warranting evaluation of single-lead ECG parameters during rest and exertion for diagnosis.
Does single-lead ECG combined with machine learning improve diagnostic accuracy for ischemic heart disease compared to bicycle ergometry in adults aged ≥ 40 years?
Observational (n=80)
No
Does single-lead ECG combined with machine learning improve diagnostic accuracy for ischemic heart disease compared to bicycle ergometry in adults aged ≥ 40 years?
A machine learning model using single-lead ECG parameters showed a relatively higher, though not statistically significant, diagnostic accuracy for ischemic heart disease compared to standard bicycle ergometry.
Answers are grounded in this paper and cite their sources.
Single-lead ECG ML model was not superior to ergometry for IHD diagnosis; leaves open its utility pending larger prospective validation.
BACKGROUND: Ischemic heart disease (IHD) impacts the quality of life and has the highest mortality rate of cardiovascular diseases globally. AIM: To compare variations in the parameters of the single-lead electrocardiogram (ECG) during resting conditions and physical exertion in individuals diagnosed with IHD and those without the condition using vasodilator-induced stress computed tomography (CT) myocardial perfusion imaging as the diagnostic reference standard. METHODS: = 49) without post stress induced myocardial perfusion defect, passed cardiologist consultation, anthropometric measurements, blood pressure and pulse rate measurement, echocardiography, cardio-ankle vascular index, bicycle ergometry, recording 3-min single-lead ECG (Cardio-Qvark) before and just after bicycle ergometry followed by performing CT myocardial perfusion. The LASSO regression with nested cross-validation was used to find the association between Cardio-Qvark parameters and the existence of the perfusion defect. Statistical processing was performed with the R programming language v4.2, Python v.3.10 [^R], and Statistica 12 program. RESULTS: Bicycle ergometry yielded an area under the receiver operating characteristic curve of 50.7% [95% confidence interval (CI): 0.388-0.625], specificity of 53.1% (95%CI: 0.392-0.673), and sensitivity of 48.4% (95%CI: 0.306-0.657). In contrast, the Cardio-Qvark test performed notably better with an area under the receiver operating characteristic curve of 67% (95%CI: 0.530-0.801), specificity of 75.5% (95%CI: 0.628-0.88), and sensitivity of 51.6% (95%CI: 0.333-0.695). CONCLUSION: The single-lead ECG has a relatively higher diagnostic accuracy compared with bicycle ergometry by using machine learning models, but the difference was not statistically significant. However, further investigations are required to uncover the hidden capabilities of single-lead ECG in IHD diagnosis.
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Marzoog et al. (2025) conducted an observational in Ischemic heart disease (n=80). Single-lead ECG (Cardio-Qvark) with machine learning vs. Bicycle ergometry was evaluated on Diagnostic accuracy (AUC) for ischemic heart disease (95% CI 0.530-0.801, p=0.337). A machine learning model using single-lead ECG parameters achieved a diagnostic accuracy of 67% for ischemic heart disease compared to 50.7% for bicycle ergometry.
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