{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,13]],"date-time":"2026-09-13T23:00:58Z","timestamp":1789340458736,"version":"build-2803163510"},"reference-count":32,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2021R1C1C1010352"],"award-info":[{"award-number":["2021R1C1C1010352"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"name":"AI Institute at Seoul National University"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["DIGITAL HEALTH"],"published-print":{"date-parts":[[2023,1]]},"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Endotracheal intubation (ETI) is critical to secure the airway in emergent situations. Although artificial intelligence algorithms are frequently used to analyze medical images, their application to evaluating intraoral structures based on images captured during emergent ETI remains limited. The aim of this study is to develop an artificial intelligence model for segmenting structures in the oral cavity using video laryngoscope (VL) images.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>From 54 VL videos, clinicians manually labeled images that include motion blur, foggy vision, blood, mucus, and vomitus. Anatomical structures of interest included the tongue, epiglottis, vocal cord, and corniculate cartilage. EfficientNet-B5 with DeepLabv3+, EffecientNet-B5 with U-Net, and Configured Mask R-Convolution Neural Network (CNN) were used; EffecientNet-B5 was pretrained on ImageNet. Dice similarity coefficient (DSC) was used to measure the segmentation performance of the model. Accuracy, recall, specificity, and F1 score were used to evaluate the model's performance in targeting the structure from the value of the intersection over union between the ground truth and prediction mask.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The DSC of tongue, epiglottis, vocal cord, and corniculate cartilage obtained from the EfficientNet-B5 with DeepLabv3+, EfficientNet-B5 with U-Net, and Configured Mask R-CNN model were 0.3351\/0.7675\/0.766\/0.6539, 0.0\/0.7581\/0.7395\/0.6906, and 0.1167\/0.7677\/0.7207\/0.57, respectively. Furthermore, the processing speeds (frames per second) of the three models stood at 3, 24, and 32, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The algorithm developed in this study can assist medical providers performing ETI in emergent situations.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/20552076231211547","type":"journal-article","created":{"date-parts":[[2023,11,7]],"date-time":"2023-11-07T02:29:38Z","timestamp":1699324178000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":13,"title":["Mask R-CNN based multiclass segmentation model for endotracheal intubation using video laryngoscope"],"prefix":"10.1177","volume":"9","author":[{"given":"Seung Jae","family":"Choi","sequence":"first","affiliation":[{"name":"Transdisciplinary Department of Medicine and Advanced Technology, Seoul National University Hospital, Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dae Kon","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Emergency Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea"},{"name":"Department of Emergency Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea"},{"name":"Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Byeong Soo","family":"Kim","sequence":"additional","affiliation":[{"name":"Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minwoo","family":"Cho","sequence":"additional","affiliation":[{"name":"Transdisciplinary Department of Medicine and Advanced Technology, Seoul National University Hospital, Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joo","family":"Jeong","sequence":"additional","affiliation":[{"name":"Department of Emergency Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea"},{"name":"Department of Emergency Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"You Hwan","family":"Jo","sequence":"additional","affiliation":[{"name":"Department of Emergency Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea"},{"name":"Department of Emergency Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kyoung Jun","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Emergency Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea"},{"name":"Department of Emergency Medicine, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu Jin","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Emergency Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea"},{"name":"Department of Emergency Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9318-849X","authenticated-orcid":false,"given":"Sungwan","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Republic of Korea"},{"name":"Institute of Bioengineering, Seoul National University, Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2023,11,6]]},"reference":[{"key":"bibr1-20552076231211547","doi-asserted-by":"publisher","DOI":"10.1097\/MEJ.0000000000000161"},{"key":"bibr2-20552076231211547","doi-asserted-by":"publisher","DOI":"10.1097\/00004311-200007000-00003"},{"key":"bibr3-20552076231211547","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-019-1481-4"},{"key":"bibr4-20552076231211547","doi-asserted-by":"publisher","DOI":"10.1111\/anae.14945"},{"key":"bibr5-20552076231211547","doi-asserted-by":"publisher","DOI":"10.1016\/j.annemergmed.2010.05.035"},{"key":"bibr6-20552076231211547","doi-asserted-by":"publisher","DOI":"10.1007\/s12630-012-9859-5"},{"key":"bibr7-20552076231211547","doi-asserted-by":"publisher","DOI":"10.3109\/10903127.2016.1139220"},{"key":"bibr8-20552076231211547","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. In:\n                      Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18\n                      , 2015, pp. 234\u2013241. Springer.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"bibr9-20552076231211547","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.103116"},{"key":"bibr10-20552076231211547","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.322"},{"key":"bibr11-20552076231211547","doi-asserted-by":"publisher","DOI":"10.4015\/S1016237221500277"},{"key":"bibr12-20552076231211547","first-page":"5998","volume":"30","author":"Vaswani A","year":"2017","journal-title":"Adv Neural Inf Process Syst"},{"key":"bibr13-20552076231211547","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, et al. An image is worth 16x16 words: transformers for image recognition at scale.\n                      arXiv preprint arXiv\n                      2020: 201011929."},{"key":"bibr14-20552076231211547","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.103890"},{"key":"bibr15-20552076231211547","doi-asserted-by":"publisher","DOI":"10.1002\/lary.28539"},{"key":"bibr16-20552076231211547","doi-asserted-by":"publisher","DOI":"10.1007\/s11548-018-01910-0"},{"key":"bibr17-20552076231211547","doi-asserted-by":"publisher","DOI":"10.1097\/MD.0000000000032084"},{"key":"bibr18-20552076231211547","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"bibr19-20552076231211547","unstructured":"Tan M, Le Q. Efficientnet: rethinking model scaling for convolutional neural networks. In:\n                      International conference on machine learning\n                      . 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