Development and validation of a deep learning model to diagnose COVID‐19 using time‐series heart rate values before the onset of symptoms

Abstract

One of the effective ways to minimize the spread of COVID-19 infection is to diagnose it as early as possible before the onset of symptoms. In addition, if the infection can be simply diagnosed using a smartwatch, the effectiveness of preventing the spread will be greatly increased. In this study, we aimed to develop a deep learning model to diagnose COVID-19 before the onset of symptoms using heart rate (HR) data obtained from a smartwatch. In the deep learning model for the diagnosis, we proposed a transformer model that learns HR variability patterns in presymptom by tracking relationships in sequential HR data. In the cross-validation (CV) results from the COVID-19 unvaccinated patients, our proposed deep learning model exhibited high accuracy metrics: sensitivity of 84.38%, specificity of 85.25%, accuracy of 84.85%, balanced accuracy of 84.81%, and area under the receiver operating characteristics (AUROC) of 0.8778. Furthermore, we validated our model using external multiple datasets including healthy subjects, COVID-19 patients, as well as vaccinated patients. In the external healthy subject group, our model also achieved high specificity of 77.80%. In the external COVID-19 unvaccinated patient group, our model also provided similar accuracy metrics to those from the CV: balanced accuracy of 87.23% and AUROC of 0.8897. In the COVID-19 vaccinated patients, the balanced accuracy and AUROC dropped by 66.67% and 0.8072, respectively. The first finding in this study is that our proposed deep learning model can simply and accurately diagnose COVID-19 patients using HRs obtained from a smartwatch before the onset of symptoms. The second finding is that the model trained from unvaccinated patients may provide less accurate diagnosis performance compared with the vaccinated patients. The last finding is that the model trained in a certain period of time may provide degraded diagnosis performances as the virus continues to mutate.

Limitations of the Cough Sound-Based COVID-19 Diagnosis Artificial Intelligence Model and its Future Direction: Longitudinal Observation Study

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Quantification of identifying cognitive impairment using olfactory-stimulated functional near-infrared spectroscopy with machine learning: a post hoc analysis of a diagnostic trial and validation of an external additional trial

“…Baseline data are presented as median and interquartile range or mean and standard deviation. Statistical analysis was performed using R software, version 3.1.1 (R Foundation, Vienna, Austria), and SPSS (version 25.0; IBM Corp., Armonk, NY, USA) [ 16 – 18 ]. Two-tailed P -values < 0.05 were considered statistically significant.…”

Artificial intelligence in the healthcare sector: comparison of deep learning networks using chest X-ray images

“…Deep learning has also been used in the literature for the detection of pneumonia and COVID-19. Chung et al ( 10 ) attempted to detect COVID-19 before symptoms appeared using deep learning. He successfully developed a deep learning model that diagnoses symptoms using heart rate (HR) data obtained from a smartwatch.…”

“…Ultimately, leveraging the opportunities presented by the contemporary information age has become imperative in the healthcare sector to mitigate and overcome these detrimental circumstances and scenarios ( 8 , 9 ). Consequently, the healthcare sector has started to use artificial intelligence ( 10–12 ) as a means to address the challenges resulting from the scarcity of qualified human resources and the burden of excessive workload. Therefore, milestones such as the use of vaccines (1796), anesthesia (1846), microscopic organism theory (1861), medical imaging technology (1895), antibiotics (1928), organ transplantation (1954), antiviral treatment technology (1960), stem cell therapy (1970) and immunotherapy (1975) have reached a new milestone with the use of artificial intelligence technologies in this sector ( 13–16 ).…”