Advancing Auscultation Education: Signals Visualization as a Novel Tool for Enhancing Pathological Respiratory Sounds Detection

• Autorzy: Pastusiak Anna Katarzyna , Hafke-Dys Honorata , Kociński Jędrzej , Szarzyński Krzysztof , Janeczek Kamil

Identyfikatory

DOI

10.2478/pjmpe-2024-0001

• Języki publikacji: EN

Abstrakty

EN

Introduction: Auscultation remains a pivotal diagnostic modality for various respiratory pathologies. To augment its clinical relevance, the continuous expansion of our understanding of pulmonary acoustics, coupled with the advancement of auscultation recording and analysis methodologies, is imperative. Material and methods: We investigated how the bimodal presentation of auscultatory signals (sound and visual cue perception) influences the subjective efficacy of pathological respiratory sound detection, which is a critical step in the development of a new auscultation tool. Recordings of pediatric breath sounds were presented in three different forms - audio only, visual representation only (spectrogram) or audiovisual (both together). The F1-score, sensitivity and specificity parameters were calculated and compared to the gold standard (GS). Subsequent to the detection experiment, participants completed a survey to subjectively assess the usability of spectrograms in the procedure. Results: Over 60% of all responders ranked the spectrogram as important (40.8%) or very important (21.1%). Moreover, 11.3% of all participants found this new form of presentation of auscultation results to be more useful than the evaluation of sound only. The addition of visual information did not statistically significantly change the evaluation of the sounds themselves, an observable trend implies that enhancing audio recordings with visualizations can enhance detection performance. This is evident in the 4 p.p. and 2 p.p. sensitivity increments for physicians and students, respectively, even without specialized visual training. Conclusions: Our research findings indicate that the integration of spectrograms with conventional auditory assessment, albeit based on observed trends and survey responses, presents a promising avenue for improving the precision and quality of medical education, as well as enhancing diagnosis and monitoring processes.

Słowa kluczowe

EN

pulmonary; medical education; breath sounds; wheezes; crackles

• Wydawca: Polskie Towarzystwo Fizyki Medycznej ; De Gruyter
• Czasopismo: Polish Journal of Medical Physics and Engineering
• Rocznik: 2024
• Tom: Vol. 30, Iss. 1
• Strony: 1--10
• Opis fizyczny: Bibliogr. 22 poz., rys., tab.

Twórcy

autor

Pastusiak Anna Katarzyna

• Department of Acoustics, Faculty of Physics, Adam Mickiewicz University Poznan, Poland
• StethoMe, Poznan, Poland

autor

Hafke-Dys Honorata

• Department of Acoustics, Faculty of Physics, Adam Mickiewicz University Poznan, Poland
• StethoMe, Poznan, Poland

autor

Kociński Jędrzej

• Department of Acoustics, Faculty of Physics, Adam Mickiewicz University Poznan, Poland
• StethoMe, Poznan, Poland

autor

Szarzyński Krzysztof

• StethoMe, Poznan, Poland

autor

Janeczek Kamil

• Department of Paediatric Pulmonology and Rheumatology, Medical University of Lublin, Poland

Bibliografia

• 1. Grzywalski T, Piecuch M, Szajek M, et al. Practical implementation of artificial intelligence algorithms in pulmonary auscultation examination. European Journal of Pediatrics. 2019;178(6):883-890. https://doi.org/10.1007/s00431-019-03363-2
• 2. Pasterkamp H, Brand PLP, Everard M, Garcia-Marcos L, Melbye H, Priftis KN. Towards the standardisation of lung sound nomenclature. European Respiratory Journal. 2016;47(3):724-732. https://doi.org/10.1183/13993003.01132-2015
• 3. Grzywalski T, Szajek M, Hafke-Dys H, et al. Respiratory system auscultation using machine learning - a big step towards objectivisation? In: M-Health/e-Health. European Respiratory Society; 2019: PA2231. https://doi.org/10.1183/13993003.congress-2019.PA2231
• 4. Melbye H, Garcia-Marcos L, Brand P, Everard M, Priftis K, Pasterkamp H. Wheezes, crackles and rhonchi: simplifying description of lung sounds increases the agreement on their classification: a study of 12 physicians’ classification of lung sounds from video recordings. BMJ Open Respiratory Research. 2016;3(1):e000136. https://doi.org/10.1136/bmjresp-2016-000136
• 5. Welch R, Warren D. Intersensory interactions. In: Boff K, Kaufman L, Thomas J, eds. Handbook of Perception and Performance. Vol 1. Wiley, 1981:251-253.
• 6. McGurk H, MacDonald J. Hearing lips and seeing voices. Nature. 1976;264(5588):746-748. https://doi.org/10.1038/264746a0
• 7. Sumby WH, Pollack I. Visual Contribution to Speech Intelligibility in Noise. The Journal of the Acoustical Society of America. 1954;26(2):212-215. https://doi.org/10.1121/1.1907309
• 8. Aviles-Solis JC, Storvoll I, Vanbelle S, Melbye H. The use of spectrograms improves the classification of wheezes and crackles in an educational setting. Scientific Reports. 2020;10(1):8461. https://doi.org/10.1038/s41598-020-65354-w
• 9. Mangione S, Nieman LZ. Pulmonary Auscultatory Skills During Training in Internal Medicine and Family Practice. American Journal of Respiratory and Critical Care Medicine. 1999;159(4):1119-1124. https://doi.org/10.1164/ajrccm.159.4.9806083
• 10. Hafke-Dys H, Bręborowicz A, Kleka P, Kociński J, Biniakowski A. The accuracy of lung auscultation in the practice of physicians and medical students. PLOS ONE. 2019;14(8):e0220606. https://doi.org/10.1371/journal.pone.0220606
• 11. Likert R. A technique for the measurement of attitudes. Archives of Psychology, 1932;22(140):1-55.
• 12. Reichert S, Gass R, Brandt C. Analysis of respiratory sounds: state of the art. Clinical medicine. Circulatory, respiratory and pulmonary medicine. 2008;2:45-58. https://doi.org/10.4137/ccrpm.s530
• 13. Pramono R, Bowyer S, Rodriguez-Villegas E. Automatic adventitious respiratory sound analysis: A systematic review. PLOS ONE. 2017;12(5):e0177926. https://doi.org/10.1371/journal.pone.0177926
• 14. Kiyokawa H, Greenberg M, Shirota K, Pasterkamp H. Auditory Detection of Simulated Crackles in Breath Sounds. Chest. 2001;119(6):1886-1892. https://doi.org/10.1378/chest.119.6.1886
• 15. Wilkins RL, Dexter JR, Murphy RLH, DelBono EA. Lung Sound Nomenclature Survey. Chest. 1990;98(4):886-889. https://doi.org/10.1378/chest.98.4.886
• 16. Pasterkamp H, Montgomery M, Wiebicke W. Nomenclature used by health care professionals to describe breath sounds in asthma. Chest. 1987;92(2):346-352. https://doi.org/10.1378/chest.92.2.346
• 17. Andrès E, Gass R, Charloux A, Brandt C, Hentzler A. Respiratory sound analysis in the era of evidence-based medicine and the world of medicine 2.0. J Med Life. 2018;11(2):89-106.
• 18. Kim Y, Hyon Y, Jung S.S.Respiratory sound classification for crackles, wheezes, and rhonchi in the clinical field using deep learning. Sci Rep. 2021;11:17186. https://doi.org/10.1038/s41598-021-96724-7
• 19. Kevat A, Kalirajah A, Roseby R. Artificial intelligence accuracy in detecting pathological breath sounds in children using digital stethoscopes. Respir Res. 2020;21:253. https://doi.org/10.1186/s12931-020-01523-9
• 20. Ahmed S, Mitra DK, Nair H.Digital auscultation as a novel childhood pneumonia diagnostic tool for community clinics in Sylhet, Bangladesh: protocol for a cross-sectional study. BMJ Open. 2022;12(2):e059630. https://doi.org/10.1136/bmjopen-2021-059630
• 21. Falter M, Gruwez H, Young J. The future is more than a digital stethoscope. European Heart Journal - Digital Health. 2021;2(4):557-558. https://doi.org/10.1093/ehjdh/ztab077
• 22. Hoffman HJ, Dobie RA, Losonczy KG et al. Declining Prevalence of Hearing Loss in US Adults Aged 20 to 69 Years. JAMA Otolaryngol Head Neck Surg. 2017;143(3):274-285. https://doi.org/10.1001/jamaoto.2016.3527

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).