{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T01:04:44Z","timestamp":1755219884594,"version":"3.43.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686080","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T00:00:00Z","timestamp":1754524800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,8,7]]},"abstract":"<jats:p>Manual labeling in video-based studies is often limited by the high effort required, leading to the sampling of frames leading to potential loss of valuable data. This study introduces a semi-automatic labeling approach using the Segment Anything Model 2 (SAM 2) to augment training datasets for segmentation models. Videos of emergency endotracheal intubation were sampled, and a subset of frames was manually labeled to form a baseline dataset. SAM 2 was then employed to generate labels for the remaining frames. Results show that models trained on the augmented dataset achieved improved Dice Similarity Coefficient (DSC) scores. These findings demonstrate that SAM 2 can substantially reduce manual labeling efforts while enhancing model performance.<\/jats:p>","DOI":"10.3233\/shti251206","type":"book-chapter","created":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:44:27Z","timestamp":1754567067000},"source":"Crossref","is-referenced-by-count":0,"title":["Leveraging SAM 2 for Semi-Supervised Learning in Endotracheal Intubation Video Segmentation"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-9331-7209","authenticated-orcid":false,"given":"Seung Jae","family":"Choi","sequence":"first","affiliation":[{"name":"Transdisciplinary Department of Medicine and Advanced Technology, Seoul National University Hospital, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dae Kon","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Public Health Care Service, Seoul National University Bundang Hospital, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jaeyoung","family":"Kim","sequence":"additional","affiliation":[{"name":"Transdisciplinary Department of Medicine and Advanced Technology, Seoul National University Hospital, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seo Young","family":"Mun","sequence":"additional","affiliation":[{"name":"Transdisciplinary Department of Medicine and Advanced Technology, Seoul National University Hospital, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minwoo","family":"Cho","sequence":"additional","affiliation":[{"name":"Transdisciplinary Department of Medicine and Advanced Technology, Seoul National University Hospital, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","MEDINFO 2025 \u2014 Healthcare Smart \u00d7 Medicine Deep"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI251206","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:44:27Z","timestamp":1754567067000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI251206"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,7]]},"ISBN":["9781643686080"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti251206","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,7]]}}}