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.
ABSTRACT
PURPOSE: Recent experimental evidence shows that extracellular vesicles (EVs) in indoor dust induce neurtrophilic pulmonary inflammation, which is a characteristic pathology in patients with severe asthma and chronic obstructive pulmonary disease (COPD). In addition, COPD is known to be an important risk factor for lung cancer, irrespective of cigarette smoking. Here, we evaluated whether sensitization to indoor dust EVs is a risk for the development of asthma, COPD, or lung cancer. METHODS: Serum IgG antibodies against dust EVs were measured in 90 healthy control subjects, 294 asthmatics, 242 COPD patients, and 325 lung cancer patients. Serum anti-dust EV IgG titers were considered high if they exceeded a 95 percentile value of the control subjects. Age-, gender-, and cigarette smoke-adjusted multiple logistic regression analyses were performed to determine odds ratios (ORs) for asthma, COPD, and lung cancer patients vs the control subjects. RESULTS: In total, 4.4%, 13.6%, 29.3%, and 54.9% of the control, asthma, COPD, and lung cancer groups, respectively, had high serum anti-dust EV IgG titers. Adjusted multiple logistic regression revealed that sensitization to dust EVs (high serum anti-dust EV IgG titer) was an independent risk factor for asthma (adjusted OR, 3.3; 95% confidence interval [CI], 1.1-10.0), COPD (adjusted OR, 8.0; 95% CI, 2.0-32.5) and lung cancer (adjusted OR, 38.7; 95% CI, 10.4-144.3). CONCLUSIONS: IgG sensitization to indoor dust EVs appears to be a major risk for the development of asthma, COPD, and lung cancer.