Use of artificial intelligence to develop predictive algorithms of cough and PCR-confirmed COVID-19 infections based on inputs from clinical-grade wearable sensors

Use of artificial intelligence to develop predictive algorithms of cough and PCR-confirmed COVID-19 infections based on inputs from clinical-grade wearable sensors

There have been over 769 million cases of COVID-19, and up to 50% of infected individuals are asymptomatic. The purpose of this study aimed to assess the use of a clinical-grade physiological wearable monitoring system, ANNE One, to develop an artificial intelligence algorithm for (1) cough detectio...

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Bibliographic Details
Published in:Scientific reports Vol. 14; no. 1; p. 8072
Main Authors: Walter, Jessica R., Lee, Jong Yoon, Yu, Lian, Kim, Brandon, Martell, Knute, Opdycke, Anita, Scheffel, Jenny, Felsl, Ingrid, Patel, Soham, Rangel, Stephanie, Serao, Alexa, Edel, Claire, Bharat, Ankit, Xu, Shuai
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 05-04-2024
Nature Publishing Group
Nature Portfolio
Subjects:
692/1807/1809
692/308/53/2421
692/699/255/2514
692/700/478/174
Algorithms
Artificial Intelligence
Biomarkers
Cough
Cough - diagnosis
COVID-19
COVID-19 - diagnosis
COVID-19 Testing
Disease detection
Heart rate
Humanities and Social Sciences
Humans
multidisciplinary
Physiology
Polymerase Chain Reaction
Respiration
Retrospective Studies
Science
Science (multidisciplinary)
Sensors
Wearable Electronic Devices
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Summary:There have been over 769 million cases of COVID-19, and up to 50% of infected individuals are asymptomatic. The purpose of this study aimed to assess the use of a clinical-grade physiological wearable monitoring system, ANNE One, to develop an artificial intelligence algorithm for (1) cough detection and (2) early detection of COVID-19, through the retrospective analysis of prospectively collected physiological data from longitudinal wear of ANNE sensors in a multicenter single arm study of subjects at high risk for COVID-19 due to occupational or home exposures. The study employed a two-fold approach: cough detection algorithm development and COVID-19 detection algorithm development. For cough detection, healthy individuals wore an ANNE One chest sensor during scripted activity. The final performance of the algorithm achieved an F-1 score of 83.3% in twenty-seven healthy subjects during biomarker validation. In the COVID-19 detection algorithm, individuals at high-risk for developing COVID-19 because of recent exposures received ANNE One sensors and completed daily symptom surveys. An algorithm analyzing vital parameters (heart rate, respiratory rate, cough count, etc.) for early COVID-19 detection was developed. The COVID-19 detection algorithm exhibited a sensitivity of 0.47 and specificity of 0.72 for detecting COVID-19 in 325 individuals with recent exposures. Participants demonstrated high adherence (≥ 4 days of wear per week). ANNE One shows promise for detection of COVID-19. Inclusion of respiratory biomarkers (e.g., cough count) enhanced the algorithm's predictive ability. These findings highlight the potential value of wearable devices in early disease detection and monitoring.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-57830-4