{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T12:27:32Z","timestamp":1789216052812,"version":"build-2803163510"},"reference-count":25,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,5,20]],"date-time":"2025-05-20T00:00:00Z","timestamp":1747699200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Pediatr."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Auscultation is a critical diagnostic feature of lung diseases, but it is subjective and challenging to measure accurately. To overcome these limitations, artificial intelligence models have been developed.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>In this prospective study, we aimed to compare respiratory sound feature extraction methods to develop an optimal machine learning model for detecting wheezing in children. Pediatric pulmonologists recorded and verified 103 instances of wheezing and 184 other respiratory sounds in 76 children. Various methods were used for sound feature extraction, and dimensions were reduced using t-distributed Stochastic Neighbor Embedding (t-SNE). The performance of models in wheezing detection was evaluated using a kernel support vector machine (SVM).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The duration of recordings in the wheezing and non-wheezing groups were 89.36\u2009\u00b1\u200939.51\u2005ms and 63.09\u2009\u00b1\u200927.79\u2005ms, respectively. The Mel-spectrogram, Mel-frequency Cepstral Coefficient (MFCC), and spectral contrast achieved the best expression of respiratory sounds and showed good performance in cluster classification. The SVM model using spectral contrast exhibited the best performance, with an accuracy, precision, recall, and F-1 score of 0.897, 0.800, 0.952, and 0.869, respectively.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Mel-spectrograms, MFCC, and spectral contrast are effective for characterizing respiratory sounds in children. A machine learning model using spectral contrast demonstrated high detection performance, indicating its potential utility in ensuring accurate diagnosis of pediatric respiratory diseases.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fped.2025.1428862","type":"journal-article","created":{"date-parts":[[2025,5,20]],"date-time":"2025-05-20T10:14:05Z","timestamp":1747736045000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Machine learning-driven strategies for enhanced pediatric wheezing detection"],"prefix":"10.3389","volume":"13","author":[{"given":"Hye Jeong","family":"Moon","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyunmin","family":"Ji","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baek Seung","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Beom Joon","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kyunghoon","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,5,20]]},"reference":[{"key":"B1","first-page":"1109","article-title":"The diagnosis of wheezing in children","volume":"77","author":"Weiss","year":"2008","journal-title":"Am Fam Physician"},{"key":"B2","doi-asserted-by":"publisher","first-page":"744","DOI":"10.1056\/NEJMra1302901","article-title":"Fundamentals of lung auscultation","volume":"370","author":"Bohadana","year":"2014","journal-title":"N Engl J Med"},{"key":"B3","doi-asserted-by":"publisher","first-page":"344","DOI":"10.7581\/pard.2012.22.4.344","article-title":"Validity of cough-holter monitoring for the objective assessment of cough and wheezing in children with respiratory symptoms","volume":"22","author":"Ha-Neul","year":"2012","journal-title":"Pediatr Allergy Respir Dis"},{"key":"B4","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1016\/j.prrv.2011.01.008","article-title":"Episodic viral wheeze and multiple trigger wheeze in preschool children: a useful distinction for clinicians?","volume":"12","author":"Schultz","year":"2011","journal-title":"Paediatr Respir Rev"},{"key":"B5","doi-asserted-by":"publisher","first-page":"22465","DOI":"10.1038\/s41598-022-25953-1","article-title":"An accurate deep learning model for wheezing in children using real world data","volume":"12","author":"Kim","year":"2022","journal-title":"Sci Rep"},{"key":"B6","doi-asserted-by":"publisher","first-page":"627337","DOI":"10.3389\/fped.2021.627337","article-title":"Real-world verification of artificial intelligence algorithm-assisted auscultation of breath sounds in children","volume":"9","author":"Zhang","year":"2021","journal-title":"Front Pediatr"},{"key":"B7","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.artmed.2018.04.008","article-title":"Lung sounds classification using convolutional neural networks","volume":"88","author":"Bardou","year":"2018","journal-title":"Artif Intell Med"},{"key":"B8","doi-asserted-by":"publisher","first-page":"e0177926","DOI":"10.1371\/journal.pone.0177926","article-title":"Automatic adventitious respiratory sound analysis: a systematic review","volume":"12","author":"Pramono","year":"2017","journal-title":"PLoS One"},{"key":"B9","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1186\/s12890-022-01896-1","article-title":"The coming era of a new auscultation system for analyzing respiratory sounds","volume":"22","author":"Kim","year":"2022","journal-title":"BMC Pulm Med"},{"key":"B10","doi-asserted-by":"publisher","first-page":"883","DOI":"10.1007\/s00431-019-03363-2","article-title":"Practical implementation of artificial intelligence algorithms in pulmonary auscultation examination","volume":"178","author":"Grzywalski","year":"2019","journal-title":"Eur J Pediatr"},{"key":"B11","doi-asserted-by":"crossref","DOI":"10.1109\/ICASSP40776.2020.9053274","article-title":"Acoustic scene classification using deep residual networks with late fusion of separated high and low frequency paths","author":"McDonnell","year":"2020"},{"key":"B12","doi-asserted-by":"crossref","DOI":"10.1109\/ICASSP.2019.8682774","article-title":"Comparing cqt and reassignment based chroma features for template-based automatic chord recognition","author":"O\u2019Hanlon","year":"2019"},{"key":"B13","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-41063-0","volume-title":"Support Vector Machines and Perceptrons: Learning, Optimization, Classification, and Application to Social Networks","author":"Murty","year":"2016"},{"key":"B14","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Van der Maaten","year":"2008","journal-title":"J Mach Learn Res"},{"key":"B15","first-page":"2349","article-title":"Voice recognition using MFCC algorithm","volume":"1","author":"Chakraborty","year":"2014","journal-title":"Int J Innov Res Adv Eng (IJIRAE)"},{"key":"B16","doi-asserted-by":"crossref","DOI":"10.1109\/ICASSP.2011.5947425","article-title":"Delta-spectral cepstral coefficients for robust speech recognition","author":"Kumar","year":"2011"},{"key":"B17","volume-title":"Short-Time Fourier Transform and Chroma Features","author":"M\u00fcller","year":"2015"},{"key":"B18","article-title":"Constant-Q transform toolbox for music processing","author":"Sch\u00f6rkhuber","year":"2010"},{"key":"B19","article-title":"Music type classification by spectral contrast feature","author":"Jiang","year":"2002"},{"key":"B20","doi-asserted-by":"crossref","DOI":"10.1109\/ICASSP.2012.6287914","article-title":"Learning a robust tonnetz-space transform for automatic chord recognition","author":"Humphrey","year":"2012"},{"key":"B21","first-page":"1385","article-title":"A Kullback-Leibler divergence based kernel for SVM classification in multimedia applications","volume":"16","author":"Moreno","year":"2003","journal-title":"Adv Neural Inf Process Syst"},{"key":"B22","doi-asserted-by":"publisher","first-page":"1521","DOI":"10.3390\/s22041521","article-title":"Feature-based fusion using CNN for lung and heart sound classification","volume":"22","author":"Tariq","year":"2022","journal-title":"Sensors"},{"key":"B23","doi-asserted-by":"publisher","first-page":"714811","DOI":"10.3389\/fmed.2021.714811","article-title":"Abnormal respiratory sounds classification using deep CNN through artificial noise addition","volume":"8","author":"Zulfiqar","year":"2021","journal-title":"Front Med"},{"key":"B24","doi-asserted-by":"publisher","first-page":"17186","DOI":"10.1038\/s41598-021-96724-7","article-title":"Respiratory sound classification for crackles, wheezes, and rhonchi in the clinical field using deep learning","volume":"11","author":"Kim","year":"2021","journal-title":"Sci Rep"},{"key":"B25","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1007\/s42979-024-03506-9","article-title":"Multi-task learning for lung sound and lung disease classification","volume":"6","author":"Suma","year":"2025","journal-title":"SN Comput Sci"}],"container-title":["Frontiers in Pediatrics"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fped.2025.1428862\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,20]],"date-time":"2025-05-20T10:14:08Z","timestamp":1747736048000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fped.2025.1428862\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,20]]},"references-count":25,"alternative-id":["10.3389\/fped.2025.1428862"],"URL":"https:\/\/doi.org\/10.3389\/fped.2025.1428862","relation":{},"ISSN":["2296-2360"],"issn-type":[{"value":"2296-2360","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,20]]},"article-number":"1428862"}}