Browse ATS 2021 Abstracts

HomeProgram ▶ Browse ATS 2021 Abstracts

ATS 2021 will feature presentations of original research from accepted abstracts. Mini Symposia and Thematic Poster Sessions are abstract based sessions.

Please use the form below to browse scientific abstracts and case reports accepted for ATS 2021. Abstracts presented at the ATS 2021 will be published in the Online Abstract Issue of the American Journal of Respiratory and Critical Care Medicine, Volume 203, May 3, 2021.

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An Artificial Intelligence Algorithm-Based Smartphone Application Can Help to Monitor Cough in Real Time

Session Title
A2362 - An Artificial Intelligence Algorithm-Based Smartphone Application Can Help to Monitor Cough in Real Time
Author Block: M. Yang1, B. Kim2, M. Kim3, S. Kim4, C. Song5, S. Kang5, J. Kwon6, J. Shim3, S. Lee7, S. Kang7, H. Park8, M. R. Sher9, H. Park10; 1Internal Medicine, SMG-SNU Boramae Medical Center, Seoul, Korea, Republic of, 2Internal Medicine, Korea University Medical Center Anam Hospital, Seoul, Korea, Republic of, 3Internal Medicine, Ewha Womans University College of Medicine, Seoul, Korea, Republic of, 4Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea, Republic of, 5Soundable Health, Inc., San Francisco, CA, United States, 6Internal Medicine, Kangwon National University School of Medicine, Chuncheon, Korea, Republic of, 7Internal Medicine, Gachon University Gil Medical Center, Incheon, Korea, Republic of, 8Internal Medicine, Kyungpook National University, Daegu, Korea, Republic of, 9Center for Cough, Largo, FL, United States, 10Internal Medicine, Seoul National University College of Medicine, SEOUL, Korea, Republic of.
RATIONALE Cough is one of the most frequently encountered symptoms for many physicians. However, it is difficult to objectively measure cough in real time. We developed an artificial intelligence (AI) algorithm-based on a smartphone application that measures cough sound in real time. We did a preliminary analysis to evaluate the performance of this system. METHODS We recruited 53 participants who visited outpatient clinic for sub-acute or chronic cough at 8 academic medical centers in Korea. The participants were asked to record 1-3 hours of ambient sounds during daytime and at least 5 hours during nighttime sleep for 2 days using smartphone. In addition, visual analogue scales (VAS) for cough were measured at the time of enrollment. The recorded files were analyzed independently by two trained researchers to count the number of coughs. The number of coughs by the researchers was compared with the number of coughs measured using an AI algorithm. Two deep learning algorithms were developed for this, one for analyzing daytime ambient sounds and the other for nighttime sleep sounds. The deep learning algorithm counted the number of coughs 3 times from the same data and the average error rate was obtained. RESULTS There were 37 (69.8%) females and 16 (30.2%) males. About majority (73.6%) of the patients were less than 50 years old. The mean VAS score was 54.3 ± 21.4. From 255.04 hours of daytime recordings and 614.56 hours of nighttime sleep recordings, 15,050 daytime coughs and 3,442 nighttime sleep coughs were collected. The cough frequency was median 34.2 (0 to 433.7) and 1.6 (0 to 58.3) during days and nights, respectively. The AI algorithm analyzed test sets including manually counted 2,941 daytime coughs and 684 nighttime coughs and AI algorithm counted 2,998 and 730 in average. The average error rate was calculated as 6.0% and 9.1%, respectively, which was better than expected error rate of 10%. CONCLUSION Our AI algorithm could monitor cough sounds in real time with an accuracy of more than 90%. Further development and external validation with larger participants would be conducted to guarantee reliability and robustness in daily clinical and home setting.