Machine learning in cardiac stress test interpretation: a systematic review.

Autor:	Hadida Barzilai D; Sami Sagol AI Hub, ARC, Sheba Medical Center, 31 Emek Ha'ela, Ramat Gan 5262000, Israel., Cohen-Shelly M; Sami Sagol AI Hub, ARC, Sheba Medical Center, 31 Emek Ha'ela, Ramat Gan 5262000, Israel.; Leviev Heart Center, Sheba Medical Center, 31 Emek Ha'ela, Ramat Gan 5262000, Israel., Sorin V; School of Medicine, Tel Aviv University, Tel Aviv, Ramat Aviv 69978, Israel.; Department of Diagnostic Radiology, Sheba Medical Center, 31 Emek Ha'ela, Ramat Gan 5262000, Israel., Zimlichman E; Sami Sagol AI Hub, ARC, Sheba Medical Center, 31 Emek Ha'ela, Ramat Gan 5262000, Israel.; School of Medicine, Tel Aviv University, Tel Aviv, Ramat Aviv 69978, Israel.; Sheba Medical Center, The Sheba Talpiot Medical Leadership Program, 31 Emek Ha'ela, Ramat Gan 5262000, Israel., Massalha E; Leviev Heart Center, Sheba Medical Center, 31 Emek Ha'ela, Ramat Gan 5262000, Israel., Allison TG; Department of Cardiovascular Medicine, Mayo Clinic, 21 2nd St SW Suite 30, Rochester, MN 55905, USA.; Division of Pediatric Cardiology, Department of Pediatric and Adolescent Medicine, Mayo Clinic, 200 1st St SW, Rochester, MN 55905, USA., Klang E; Sami Sagol AI Hub, ARC, Sheba Medical Center, 31 Emek Ha'ela, Ramat Gan 5262000, Israel.; School of Medicine, Tel Aviv University, Tel Aviv, Ramat Aviv 69978, Israel.; Department of Diagnostic Radiology, Sheba Medical Center, 31 Emek Ha'ela, Ramat Gan 5262000, Israel.; Sheba Medical Center, The Sheba Talpiot Medical Leadership Program, 31 Emek Ha'ela, Ramat Gan 5262000, Israel.; Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York, NY 10029-5674, USA.
Jazyk:	angličtina
Zdroj:	European heart journal. Digital health [Eur Heart J Digit Health] 2024 Apr 17; Vol. 5 (4), pp. 401-408. Date of Electronic Publication: 2024 Apr 17 (Print Publication: 2024).
DOI:	10.1093/ehjdh/ztae027
Abstrakt:	Coronary artery disease (CAD) is a leading health challenge worldwide. Exercise stress testing is a foundational non-invasive diagnostic tool. Nonetheless, its variable accuracy prompts the exploration of more reliable methods. Recent advancements in machine learning (ML), including deep learning and natural language processing, have shown potential in refining the interpretation of stress testing data. Adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we conducted a systematic review of ML applications in stress electrocardiogram (ECG) and stress echocardiography for CAD prognosis. Medical Literature Analysis and Retrieval System Online, Web of Science, and the Cochrane Library were used as databases. We analysed the ML models, outcomes, and performance metrics. Overall, seven relevant studies were identified. Machine-learning applications in stress ECGs resulted in sensitivity and specificity improvements. Some models achieved rates of above 96% in both metrics and reduced false positives by up to 21%. In stress echocardiography, ML models demonstrated an increase in diagnostic precision. Some models achieved specificity and sensitivity rates of up to 92.7 and 84.4%, respectively. Natural language processing applications enabled the categorization of stress echocardiography reports, with accuracy rates nearing 98%. Limitations include a small, retrospective study pool and the exclusion of nuclear stress testing, due to its well-documented status. This review indicates the potential of artificial intelligence applications in refining CAD stress testing assessment. Further development for real-world use is warranted. Competing Interests: Conflict of interest: none declared. (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology.)
Databáze:	MEDLINE
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