Trial reportWorld journal of cardiology2025
Development and validation of a machine learning model for diagnosis of ischemic heart disease using single-lead electrocardiogram parameters.
Trial report in World journal of cardiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06181799 (Biomarkers of the Exhaled Breath and Single-Lead Electrocardiography in the Diagnosis of Myocardial Ischemia), which is not on this map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Biomarkers of the Exhaled Breath and Single-Lead Electrocardiography in the Diagnosis of Myocardial Ischemia
Who cites it
3 citing papers in PubMed.
- The Evolving Role of Artificial Intelligence and Machine Learning in the Wearable Electrocardiogram: A Primer on Wearable-Enabled Prediction of Cardiac Dysfunction.Bioengineering (Basel, Switzerland) · 2026Review
- Artificial Intelligence-Enabled Electrocardiogram for the Detection and Management of Cancer Therapy-Related Cardiotoxicity.Cancer innovation · 2025Article
- Artificial intelligence-enabled single-lead electrocardiogram in early detection of ischemic heart disease.World journal of cardiology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIschemic heart disease (IHD) impacts the quality of life and has the highest mortality rate of cardiovascular diseases globally.
aimTo 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.
methodsThis single center observational study included 80 participants. The participants were aged ≥ 40 years and given an informed written consent to participate in the study. Both groups, G1 (
resultsBicycle 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).
conclusionThe 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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Registered trials
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