This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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/s00431-019-03363-2Search in Google Scholar
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/10.1183/13993003.01132-2015Search in Google Scholar
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.PA2231Search in Google Scholar
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-2016-000136Search in Google Scholar
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.Search in Google Scholar
McGurk H, MacDonald J. Hearing lips and seeing voices. Nature. 1976;264(5588):746-748. https://doi.org/10.1038/264746a0Search in Google Scholar
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/10.1121/1.1907309Search in Google Scholar
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-wSearch in Google Scholar
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.159.4.9806083Search in Google Scholar
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.0220606Search in Google Scholar
Likert R. A technique for the measurement of attitudes. Archives of Psychology, 1932;22(140):1-55.Search in Google Scholar
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.s530Search in Google Scholar
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.0177926Search in Google Scholar
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.119.6.1886Search in Google Scholar
Wilkins RL, Dexter JR, Murphy RLH, DelBono EA. Lung Sound Nomenclature Survey. Chest. 1990;98(4):886-889. https://doi.org/10.1378/chest.98.4.886Search in Google Scholar
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.92.2.346Search in Google Scholar
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.Search in Google Scholar
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/s41598-021-96724-7Search in Google Scholar
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/s12931-020-01523-9Search in Google Scholar
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-2021-059630Search in Google Scholar
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/ztab077Search in Google Scholar
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.2016.3527Search in Google Scholar