PT  - JOURNAL ARTICLE
AU  - Emeryk, Andrzej
AU  - Derom, Eric
AU  - Janeczek, Kamil
AU  - Kuźnar-Kamińska, Barbara
AU  - Zelent, Anna
AU  - Łukaszyk, Mateusz
AU  - Grzywalski, Tomasz
AU  - Pastusiak, Anna
AU  - Biniakowski, Adam
AU  - Szarzyński, Krzysztof
AU  - Botteldooren, Dick
AU  - Kociński, Jędrzej
AU  - Hafke-Dys, Honorata
TI  - Home Monitoring of Asthma Exacerbations in Children and Adults With Use of an AI-Aided Stethoscope
AID  - 10.1370/afm.3039
DP  - 2023 Nov 01
TA  - The Annals of Family Medicine
PG  - 517--525
VI  - 21
IP  - 6
4099  - http://www.annfammed.org/content/21/6/517.short
4100  - http://www.annfammed.org/content/21/6/517.full
SO  - Ann Fam Med2023 Nov 01; 21
AB  - PURPOSE The advent of new medical devices allows patients with asthma to self-monitor at home, providing a more complete picture of their disease than occasional in-person clinic visits. This raises a pertinent question: which devices and parameters perform best in exacerbation detection?METHODS A total of 149 patients with asthma (90 children, 59 adults) participated in a 6-month observational study. Participants (or parents) regularly (daily for the first 2 weeks and weekly for the next 5.5 months, with increased frequency during exacerbations) performed self-examinations using 3 devices: an artificial intelligence (AI)-aided home stethoscope (providing wheezes, rhonchi, and coarse and fine crackles intensity; respiratory and heart rate; and inspiration-to-expiration ratio), a peripheral capillary oxygen saturation (SpO2) meter, and a peak expiratory flow (PEF) meter and filled out a health state survey. The resulting 6,029 examinations were evaluated by physicians for the presence of exacerbations. For each registered parameter, a machine learning model was trained, and the area under the receiver operating characteristic curve (AUC) was calculated to assess its utility in exacerbation detection.RESULTS The best single-parameter discriminators of exacerbations were wheezes intensity for young children (AUC 84% [95% CI, 82%-85%]), rhonchi intensity for older children (AUC 81% [95% CI, 79%-84%]), and survey answers for adults (AUC 92% [95% CI, 89%-95%]). The greatest efficacy (in terms of AUC) was observed for a combination of several parameters.CONCLUSIONS The AI-aided home stethoscope provides reliable information on asthma exacerbations. The parameters provided are effective for children, especially those younger than 5 years of age. The introduction of this tool to the health care system might enhance asthma exacerbation detection substantially and make remote monitoring of patients easier.