Key result
Machine learning improves CAD diagnostic precision in stress ECGs and echocardiography, reaching ~96% sensitivity and specificity.
Why the study?
Exercise stress testing has variable accuracy for coronary artery disease diagnosis, prompting exploration of machine learning methods to refine data interpretation.
Do machine learning models improve the diagnostic accuracy and interpretation of stress ECG and stress echocardiography for CAD prognosis?
Systematic Review
Do machine learning models improve the diagnostic accuracy and interpretation of stress ECG and stress echocardiography for CAD prognosis?
Machine learning models demonstrate potential to significantly improve the diagnostic precision, sensitivity, and specificity of stress ECG and stress echocardiography for coronary artery disease.
May enhance CAD detection in stress testing; extends evidence for ML integration in cardiac diagnostics.
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.
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Barzilai et al. (2024) conducted a systematic review in Coronary artery disease (CAD). Machine learning applications was evaluated on Diagnostic performance metrics including sensitivity, specificity, and accuracy for CAD prognosis. Machine learning applications in stress ECGs and echocardiography improved diagnostic precision for CAD, with some models achieving sensitivity and specificity rates above 96%.
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