Artificial Intelligence for Detection of Cardiovascular-Related Diseases from Wearable Devices: A Systematic Review and Meta-Analysis

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.

Keywords: Electrocardiography; artificial intelligence; cardiovascular disease; deep learning; machine learning; photoplethysmography.

Fig. 1. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 flow diagram for study selection.

Fig. 2. Schematic illustration for wearable device-based artificial intelligence for cardiovascular-related diseases. ECG, electrocardiography; PPG, photoplethysmography; CNN, convolutional neural network; RNN, recurrent neural network; LSTM, long short-term memory.

Fig. 3. Meta-analyzed sensitivity and specificity of artificial intelligence for atrial fibrillation detection.

Fig. 4. Hierarchical summary of receiver operating characteristics curves of artificial intelligence for atrial fibrillation detection. (A) All studies. (B) Studies with conventional machine learning vs. studies with deep neural networks. (C) Studies tested with public dataset vs. studies tested with proprietary dataset. (D) Studies tested with data acquired from in-hospital devices vs. studies tested with data acquired from wearable devices. HSROC, hierarchical summary receiver operating characteristics.