Artificial Intelligence for Detection of Cardiovascular-Related Diseases from Wearable Devices: A Systematic Review and Meta-Analysis
Yonsei Medical Journal 2022³â 63±Ç 0È£ p.93 ~ p.107
À̼Ҷ÷(Lee So-Lam) - Yonsei University Wonju College of Medicine Department of Preventive Medicine
(Chu Yu-Seong) - Yonsei University Department of Biomedical Engineering
(Ryu Ji-Seung) - Yonsei University Wonju College of Medicine Department of Dermatology
¹Ú¿µÁØ(Park Young-Jun) - Yonsei University Wonju College of Medicine Wonju Severance Christian Hospital Department of Internal Medicine
¾ç¼¼Á¤(Yang Se-Jung) - Yonsei University Department of Biomedical Engineering
°í»ó¹é(Koh Sang-Baek) - Yonsei University Wonju College of Medicine Department of Preventive Medicine
Abstract
Purpose: Several artificial intelligence (AI) models for the detection and prediction of cardiovascular-related diseases, including arrhythmias, diabetes, and sleep apnea, have been reported. This systematic review and meta-analysis aimed to identify AI models developed for or applicable to wearable and mobile devices for diverse cardiovascular-related diseases.
Materials and Methods: The searched databases included Medline, Embase, and Cochrane Library. For AI models for atrial fibrillation (AF) detection, a meta-analysis of diagnostic accuracy was performed to summarize sensitivity and specificity.
Results: A total of 102 studies were included in the qualitative review. There were AI models for the detection of arrythmia (n=62), followed by sleep apnea (n=11), peripheral vascular diseases (n=6), diabetes mellitus (n=5), hyper/hypotension (n=5), valvular heart disease (n=4), heart failure (n=3), myocardial infarction and cardiac arrest (n=2), and others (n=4). For quantitative analysis of 26 studies reporting AI models for AF detection, meta-analyzed sensitivity was 94.80% and specificity was 96.96%. Deep neural networks showed superior performance [meta-analyzed area under receiver operating characteristics curve (AUROC) of 0.981] compared to conventional machine learning algorithms (meta-analyzed AUROC of 0.961). However, AI models tested with proprietary dataset (meta-analyzed AUROC of 0.972) or data acquired from wearable devices (meta-analyzed AUROC of 0.977) showed inferior performance than those with public dataset (meta-analyzed AUROC of 0.986) or data from in-hospital devices (meta-analyzed AUROC of 0.983).
Conclusion: This review found that AI models for diverse cardiovascular-related diseases are being developed, and that they are gradually developing into a form that is suitable for wearable and mobile devices.
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Electrocardiography, photoplethysmography, artificial intelligence, cardiovascular disease, machine learning, deep learning
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À¯È¿¼º°á°ú(Recomendation)
This systematic review and meta-analysis revealed that AI models for the diagnosis and prediction of various cardiovascular-related diseases as well as arrhythmias are being developed, and that they are gradually developing into a form that is suitable for wearable and mobile devices.