{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T17:47:01Z","timestamp":1791913621929,"version":"4.3.4"},"reference-count":30,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2024,1,13]],"date-time":"2024-01-13T00:00:00Z","timestamp":1705104000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2024,1,13]],"date-time":"2024-01-13T00:00:00Z","timestamp":1705104000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["The Laryngoscope"],"published-print":{"date-parts":[[2024,6]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>While videostroboscopy is recognized as the most popular approach for investigating vocal fold function, evaluating the numerical values, such as the membranous glottal gap area, remains too time consuming for clinical applications.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We used a total of 2507 videostroboscopy images from 137 patients and developed five U\u2010Net\u2010based deep\u2010learning image segmentation models for automatic masking of the membranous glottal gap area. To further validate the models, we used another 410 images from 41 different patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>During development, all five models exhibited acceptable and similar metrics. While the VGG19 U\u2010Net had a long inference time of 1654\u2009ms, the other four models had more practical inference times, ranging from 16 to 138\u2009ms.<\/jats:p>\n                    <jats:p>During further validation, Efficient U\u2010Net demonstrated the highest intersection over union of 0.8455, the highest Dice coefficient of 0.9163, and the lowest Hausdorff distance of 1.5626. The normalized membranous glottal gap area index was also calculated and validated. Efficient U\u2010Net and VGG19 U\u2010Net exhibited the lowest mean squared errors (3.5476 and 3.3842) and the lowest mean absolute errors (1.8835 and 1.8396).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Automatic segmentation of the membranous glottal gap area can be achieved through U\u2010net\u2010based architecture. Considering the segmentation quality and speed, Efficient U\u2010Net is a reasonable choice for this task, while the other four models remain valuable competitors. The models' masked area enables possible calculation of the normalized membranous glottal gap area and analysis of the glottal area waveform, revealing promising clinical applications for this model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Level of Evidence<\/jats:title>\n                    <jats:p>\n                      NA\n                      <jats:italic>Laryngoscope<\/jats:italic>\n                      , 134:2835\u20132843, 2024\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/lary.31266","type":"journal-article","created":{"date-parts":[[2024,1,13]],"date-time":"2024-01-13T06:29:53Z","timestamp":1705127393000},"page":"2835-2843","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Automatic Segmentation of Membranous Glottal Gap Area with\n                    <scp>U\u2010Net<\/scp>\n                    \u2010Based Architecture"],"prefix":"10.1002","volume":"134","author":[{"given":"Acquah","family":"Hackman","sequence":"first","affiliation":[{"name":"Artificial Intelligence Development Center Changhua Christian Hospital  Changhua Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chih\u2010Hua","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Otorhinolaryngology, Head and Neck Surgery Changhua Christian Hospital  Changhua Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5610-7080","authenticated-orcid":false,"given":"Andy Wei\u2010Ge","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Otorhinolaryngology, Head and Neck Surgery Changhua Christian Hospital  Changhua Taiwan"},{"name":"Doctoral Program in Translational Medicine National Chung Hsing University  Taichung Taiwan"},{"name":"Rong Hsing Translational Medicine Research Center National Chung Hsing University  Taichung Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mu\u2010Kuan","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Otorhinolaryngology, Head and Neck Surgery Changhua Christian Hospital  Changhua Taiwan"},{"name":"Department of Post\u2010Baccalaureate Medicine, College of Medicine National Chung Hsing University  Taichung 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