ArticleNature communications2024
Enhancing the diagnosis of functionally relevant coronary artery disease with machine learning.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Machine learning and SHAP values for predicting coronary artery disease risk in Xinjiang, China.European journal of medical research · 2026Article
- Interpretable multimodal zero shot ECG diagnosis via structured clinical knowledge alignment.NPJ cardiovascular health · 2026Article
- A two-stage multimodal learning framework for the automated diagnosis of obstructive coronary artery disease based on dynamic single-photon emission computed tomography.Quantitative imaging in medicine and surgery · 2026Article
- AI approaches for predicting progression to acute coronary syndrome among stable coronary heart disease patients.NPJ cardiovascular health · 2025Article
- AI-enhanced flexible ECG patch for accurate heart disease diagnosis, optimal wear positioning, and interactive medical consultation.National science review · 2025Article
- Toward Artificial Intelligence in Oncology and Cardiology: A Narrative Review of Systems, Challenges, and Opportunities.Journal of clinical medicine · 2025Article
- Differential expression of glucose metabolism and circadian rhythm-related genes in patients with acute myocardial infarction and their clinical significance.Scientific reports · 2025Article
- Machine learning based characterization of high risk carriers of HTLV-1-associated myelopathy (HAM).Scientific reports · 2025Article
- A machine learning algorithm to predict treatment effectiveness for Kawasaki disease in China: a retrospective model development and validation study.Frontiers in immunology · 2025Article
- Machine learning-driven risk assessment of coronary heart disease: Analysis of NHANES data from 1999 to 2018.Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2024Article
- Enhancing the diagnosis of functionally relevant coronary artery disease with machine learning.Nature communications · 2024Article
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9 authors.
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Abstract
Functionally relevant coronary artery disease (fCAD) can result in premature death or nonfatal acute myocardial infarction. Its early detection is a fundamentally important task in medicine. Classical detection approaches suffer from limited diagnostic accuracy or expose patients to possibly harmful radiation. Here we show how machine learning (ML) can outperform cardiologists in predicting the presence of stress-induced fCAD in terms of area under the receiver operating characteristic (AUROC: 0.71 vs. 0.64, p = 4.0E-13). We present two ML approaches, the first using eight static clinical variables, whereas the second leverages electrocardiogram signals from exercise stress testing. At a target post-test probability for fCAD of <15%, ML facilitates a potential reduction of imaging procedures by 15-17% compared to the cardiologist's judgement. Predictive performance is validated on an internal temporal data split as well as externally. We also show that combining clinical judgement with conventional ML and deep learning using logistic regression results in a mean AUROC of 0.74.
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