{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,11]],"date-time":"2025-07-11T05:40:14Z","timestamp":1752212414337,"version":"3.41.2"},"reference-count":27,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,7,11]],"date-time":"2025-07-11T00:00:00Z","timestamp":1752192000000},"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. Digit. Health"],"abstract":"<jats:p>Manual diagnostic methods for assessing exercise-induced laryngeal obstruction (EILO) contain human bias and can lead to subjective decisions. Several studies have proposed machine learning methods for segmenting laryngeal structures to automate and make diagnostic outcomes more objective. Four state-of-the-art models for laryngeal image segmentation are implemented, trained, and compared using our pre-processed dataset containing laryngeal images derived from continuous laryngoscopy exercise-test (CLE-test) data. These models include both convolutional-based and transformer-based methods. We propose a new framework called LarynxFormer, consisting of a pre-processing pipeline, transformer-based segmentation, and post-processing of laryngeal images. This study contributes to the investigation of using machine learning as a diagnostic tool for EILO. Furthermore, we show that a transformer-based approach for larynx segmentation outperforms conventional state-of-the-art image segmentation methods in terms of performance metrics and computational speed, demonstrating up to 2x faster inference time compared to the other methods.<\/jats:p>","DOI":"10.3389\/fdgth.2025.1459136","type":"journal-article","created":{"date-parts":[[2025,7,11]],"date-time":"2025-07-11T05:24:24Z","timestamp":1752211464000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["LarynxFormer: a transformer-based framework for processing and segmenting laryngeal images"],"prefix":"10.3389","volume":"7","author":[{"given":"Rune","family":"M\u00e6stad","sequence":"first","affiliation":[]},{"given":"Abdul","family":"Hanan","sequence":"additional","affiliation":[]},{"given":"Haakon","family":"Kristian Kvidaland","sequence":"additional","affiliation":[]},{"given":"Hege","family":"Clemm","sequence":"additional","affiliation":[]},{"given":"Reza","family":"Arghandeh","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2025,7,11]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1911","DOI":"10.1016\/j.rmed.2009.05.024","article-title":"Exercise induced dyspnea in the young. Larynx as the bottleneck of the airways","volume":"103","author":"R\u00f8ksund","year":"2009","journal-title":"Respir Med"},{"key":"B2","doi-asserted-by":"publisher","first-page":"622","DOI":"10.1136\/bjsports-2021-104704","article-title":"Exercise-induced laryngeal obstruction (eilo) in athletes: a narrative review by a subgroup of the ioc consensus on acute respiratory illness in the athlete","volume":"56","author":"Clemm","year":"2022","journal-title":"Br J Sports Med"},{"key":"B3","doi-asserted-by":"publisher","first-page":"3509","DOI":"10.1002\/ppul.25104","article-title":"Prevalence of exercise-induced bronchoconstriction and laryngeal obstruction in adolescent athletes","volume":"55","author":"Ersson","year":"2020","journal-title":"Pediatr Pulmonol"},{"key":"B4","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1136\/thoraxjnl-2014-205738","article-title":"Prevalence of exercise-induced bronchoconstriction and exercise-induced laryngeal obstruction in a general adolescent population","volume":"70","author":"Johansson","year":"2015","journal-title":"Thorax"},{"key":"B5","doi-asserted-by":"publisher","first-page":"1313","DOI":"10.1007\/s00405-011-1612-0","article-title":"Exercise-induced laryngeal obstructions: prevalence and symptoms in the general public","volume":"268","author":"Christensen","year":"2011","journal-title":"Eur Arch Otorhinolaryngol"},{"key":"B6","doi-asserted-by":"publisher","first-page":"1041","DOI":"10.1111\/sms.14137","article-title":"Conundrums in the breathless athlete; exercise-induced laryngeal obstruction or asthma?","volume":"32","author":"Hammer","year":"2022","journal-title":"Scand J Med Sci Sports"},{"key":"B7","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1097\/01.mlg.0000184528.16229.ba","article-title":"Continuous laryngoscopy exercise test: a method for visualizing laryngeal dysfunction during exercise","volume":"116","author":"Heimdal","year":"2006","journal-title":"Laryngoscope"},{"key":"B8","doi-asserted-by":"publisher","first-page":"00195","DOI":"10.1183\/23120541.00195-2021","article-title":"Characteristics and impact of exercise-induced laryngeal obstruction: an international perspective","volume":"7","author":"Walsted","year":"2021","journal-title":"ERJ Open Res"},{"key":"B9","doi-asserted-by":"publisher","first-page":"1929","DOI":"10.1007\/s00405-009-1030-8","article-title":"Audiovisual assessment of exercise-induced laryngeal obstruction: reliability and validity of observations","volume":"266","author":"Maat","year":"2009","journal-title":"Eur Arch Otorhinolaryngol"},{"key":"B10","doi-asserted-by":"publisher","first-page":"00070","DOI":"10.1183\/23120541.00070-2017","article-title":"Validity and reliability of grade scoring in the diagnosis of exercise-induced laryngeal obstruction","volume":"3","author":"Walsted","year":"2017","journal-title":"ERJ Open Res"},{"key":"B11","doi-asserted-by":"publisher","first-page":"1880","DOI":"10.3390\/electronics13101880","article-title":"Diagnostics of exercise-induced laryngeal obstruction using machine learning: a narrative review","volume":"13","author":"M\u00e6stad","year":"2024","journal-title":"Electronics"},{"key":"B12","doi-asserted-by":"publisher","first-page":"1127","DOI":"10.1109\/TBME.2018.2867636","article-title":"Quantification and analysis of laryngeal closure from endoscopic videos","volume":"66","author":"Lin","year":"2019","journal-title":"IEEE Trans Biomed Eng"},{"key":"B13","doi-asserted-by":"publisher","first-page":"20552076231211547","DOI":"10.1177\/20552076231211547","article-title":"Mask r-cnn based multiclass segmentation model for endotracheal intubation using video laryngoscope","volume":"9","author":"Choi","year":"2023","journal-title":"Digit Health"},{"key":"B14","doi-asserted-by":"crossref","DOI":"10.1109\/ICCV.2017.322","article-title":"Mask r-cnn","author":"He","year":""},{"key":"B15","doi-asserted-by":"publisher","first-page":"e0227791","DOI":"10.1371\/journal.pone.0227791","article-title":"Fully automatic segmentation of glottis and vocal folds in endoscopic laryngeal high-speed videos using a deep convolutional lstm network","volume":"15","author":"Fehling","year":"2020","journal-title":"PLoS One"},{"key":"B16","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1109\/JTEHM.2023.3237859","article-title":"Glottisnetv2: temporal glottal midline detection using deep convolutional neural networks","volume":"11","author":"Kruse","year":"2023","journal-title":"IEEE J Transl Eng Health Med"},{"key":"B17","article-title":"Attention is all you need","author":"Vaswani","year":""},{"key":"B18","article-title":"An image is worth","author":"Dosovitskiy","year":""},{"key":"B19","article-title":"Segformer: simple and efficient design for semantic segmentation with transformers","author":"Xie","year":""},{"key":"B20","article-title":"Opencv","year":""},{"key":"B21","article-title":"Label studio","year":""},{"key":"B22","article-title":"Pytorch torchvision transforms","year":""},{"key":"B23","article-title":"An image is worth","author":"Dosovitskiy","year":""},{"key":"B24","doi-asserted-by":"crossref","DOI":"10.1109\/CVPR.2016.90","article-title":"Deep residual learning for image recognition","author":"He","year":""},{"key":"B25","doi-asserted-by":"publisher","first-page":"104791","DOI":"10.1016\/j.bspc.2023.104791","article-title":"Transformers in medical image segmentation: a review","volume":"84","author":"Xiao","year":"2023","journal-title":"Biomed Signal Process Control"},{"key":"B26","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-24574-4_28","article-title":"U-net: convolutional networks for biomedical image segmentation","author":"Ronneberger","year":""},{"key":"B27","article-title":"Decoupled weight decay regularization","author":"Loshchilov","year":""}],"container-title":["Frontiers in Digital Health"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fdgth.2025.1459136\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,11]],"date-time":"2025-07-11T05:24:25Z","timestamp":1752211465000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fdgth.2025.1459136\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,11]]},"references-count":27,"alternative-id":["10.3389\/fdgth.2025.1459136"],"URL":"https:\/\/doi.org\/10.3389\/fdgth.2025.1459136","relation":{},"ISSN":["2673-253X"],"issn-type":[{"value":"2673-253X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,11]]},"article-number":"1459136"}}