Artificial Intelligence Approach to the Monitoring of Respiratory Sounds in Asthmatic Patients

• Published in: Frontiers in physiology Vol. 12; p. 745635
• Main Authors: Hafke-Dys, Honorata, Kuźnar-Kamińska, Barbara, Grzywalski, Tomasz, Maciaszek, Adam, Szarzyński, Krzysztof, Kociński, Jędrzej
• Format: Journal Article
• Language: English
• Published: Frontiers Media S.A 11.11.2021
• Subjects: asthma; auscultation; monitoring; Physiology; rhonchi; stethoscope; wheezes
• ISSN: 1664-042X; 1664-042X
• DOI: 10.3389/fphys.2021.745635

Background: Effective and reliable monitoring of asthma at home is a relevant factor that may reduce the need to consult a doctor in person. Aim: We analyzed the possibility to determine intensities of pathological breath phenomena based on artificial intelligence (AI) analysis of sounds recorded during standard stethoscope auscultation. Methods: The evaluation set comprising 1,043 auscultation examinations (9,319 recordings) was collected from 899 patients. Examinations were assigned to one of four groups: asthma with and without abnormal sounds (AA and AN, respectively), no-asthma with and without abnormal sounds (NA and NN, respectively). Presence of abnormal sounds was evaluated by a panel of 3 physicians that were blinded to the AI predictions. AI was trained on an independent set of 9,847 recordings to determine intensity scores (indexes) of wheezes, rhonchi, fine and coarse crackles and their combinations: continuous phenomena (wheezes + rhonchi) and all phenomena. The pair-comparison of groups of examinations based on Area Under ROC-Curve (AUC) was used to evaluate the performance of each index in discrimination between groups. Results: Best performance in separation between AA and AN was observed with Continuous Phenomena Index (AUC 0.94) while for NN and NA. All Phenomena Index (AUC 0.91) showed the best performance. AA showed slightly higher prevalence of wheezes compared to NA. Conclusions: The results showed a high efficiency of the AI to discriminate between the asthma patients with normal and abnormal sounds, thus this approach has a great potential and can be used to monitor asthma symptoms at home.