{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T01:50:01Z","timestamp":1785462601757,"version":"3.56.0"},"reference-count":33,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2022,10,5]],"date-time":"2022-10-05T00:00:00Z","timestamp":1664928000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100014188","name":"Ministry of Science and ICT, South Korea","doi-asserted-by":"publisher","award":["IITP\u20102021\u20102018\u20100\u201001833"],"award-info":[{"award-number":["IITP\u20102021\u20102018\u20100\u201001833"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["associationofanaesthetists-publications.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Anaesthesia"],"published-print":{"date-parts":[[2023,1]]},"abstract":"<jats:title>Summary<\/jats:title>\n                  <jats:p>\n                    Unanticipated difficult laryngoscopy is associated with serious airway\u2010related complications. We aimed to develop and test a convolutional neural network\u2010based deep\u2010learning model that uses lateral cervical spine radiographs to predict Cormack\u2013Lehane grade 3 or 4 direct laryngoscopy views of the glottis. We analysed the radiographs of 5939 thyroid surgery patients at our hospital, 253 (4%) of whom had grade 3 or 4 glottic views. We used 10 randomly sampled datasets to train a model. We compared the new model with six similar models (VGG, ResNet, Xception, ResNext, DenseNet and SENet). The Brier score (95%CI) of the new model, 0.023 (0.021\u20130.025), was lower (\u2018better\u2019) than the other models: VGG, 0.034 (0.034\u20130.035); ResNet, 0.033 (0.033\u20130.035); Xception, 0.032 (0.031\u20130.033); ResNext, 0.033 (0.032\u20130.033); DenseNet, 0.030 (0.029\u20130.032); SENet, 0.031 (0.029\u20130.032), all p\u2009&lt;\u20090.001. We calculated mean (95%CI) of the new model for: R\n                    <jats:sup>2<\/jats:sup>\n                    , 0.428 (0.388\u20130.468); mean squared error, 0.023 (0.021\u20130.025); mean absolute error, 0.048 (0.046\u20130.049); balanced accuracy, 0.713 (0.684\u20130.742); and area under the receiver operating characteristic curve, 0.965 (0.962\u20130.969). Radiographic features around the hyoid bone, pharynx and cervical spine were associated with grade 3 and 4 glottic views.\n                  <\/jats:p>","DOI":"10.1111\/anae.15874","type":"journal-article","created":{"date-parts":[[2022,10,5]],"date-time":"2022-10-05T18:00:13Z","timestamp":1664992813000},"page":"64-72","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Deep\u2010learning model associating lateral cervical radiographic features with\n                    <scp>Cormack\u2013Lehane<\/scp>\n                    grade 3 or 4 glottic view"],"prefix":"10.1111","volume":"78","author":[{"given":"H.\u2010Y.","family":"Cho","sequence":"first","affiliation":[{"name":"Department of Anaesthesiology and Pain Medicine Seoul National University College of Medicine Seoul National University Hospital  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"K.","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science Daegu Gyeongbuk Institute of Science and Technology  Daegu Republic of Korea"},{"name":"Department of Biomedical Engineering Medical Big data Research Center Seoul National University College of Medicine  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"H.\u2010J.","family":"Kong","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering Medical Big data Research Center Seoul National University College of Medicine  Seoul Republic of Korea"},{"name":"Transdisciplinary Department of Medicine and Advanced Technology Seoul National University Hospital  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"H.\u2010L.","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Anaesthesiology and Pain Medicine Seoul National University College of Medicine Seoul National University Hospital  Seoul Republic of Korea"},{"name":"Biomedical Research Institute Seoul National University Hospital  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"C.\u2010W.","family":"Jung","sequence":"additional","affiliation":[{"name":"Department of Anaesthesiology and Pain Medicine Seoul National University College of Medicine Seoul National University Hospital  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"H.\u2010P.","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Anaesthesiology and Pain Medicine Seoul National University College of Medicine Seoul National University Hospital  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J. Y.","family":"Hwang","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science Daegu Gyeongbuk Institute of Science and Technology  Daegu Republic of Korea"},{"name":"Department of Interdisciplinary Studies of Artificial Intelligence Daegu Gyeongbuk Institute of Science and Technology  Daegu Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0048-7958","authenticated-orcid":false,"given":"H.\u2010C.","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Anaesthesiology and Pain Medicine Seoul National University College of Medicine Seoul National University Hospital  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2022,10,5]]},"reference":[{"key":"e_1_2_6_2_1","doi-asserted-by":"publisher","DOI":"10.1093\/bja\/aes393"},{"key":"e_1_2_6_3_1","doi-asserted-by":"publisher","DOI":"10.1093\/bja\/aer292"},{"key":"e_1_2_6_4_1","doi-asserted-by":"publisher","DOI":"10.23736\/S0375-9393.19.14127-2"},{"key":"e_1_2_6_5_1","doi-asserted-by":"publisher","DOI":"10.1111\/aas.13078"},{"key":"e_1_2_6_6_1","doi-asserted-by":"publisher","DOI":"10.1017\/S0265021505001146"},{"key":"e_1_2_6_7_1","doi-asserted-by":"publisher","DOI":"10.1097\/EJA.0000000000000430"},{"key":"e_1_2_6_8_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-021-00438-z"},{"key":"e_1_2_6_9_1","doi-asserted-by":"publisher","DOI":"10.1046\/j.1365-2044.2000.01270.x"},{"key":"e_1_2_6_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"e_1_2_6_11_1","first-page":"2921","volume-title":"2016 Institute of Electrical and Electronics Engineers Conference on Computer Vision and Pattern Recognition","author":"Zhou B","year":"2016"},{"key":"e_1_2_6_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_2_6_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.195"},{"key":"e_1_2_6_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.634"},{"key":"e_1_2_6_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"e_1_2_6_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00745"},{"key":"e_1_2_6_17_1","doi-asserted-by":"crossref","first-page":"61","DOI":"10.7551\/mitpress\/1113.003.0008","volume-title":"Advances in Large Margin Classifiers","author":"Platt J","year":"2000"},{"key":"e_1_2_6_18_1","first-page":"1321","volume-title":"Proceedings of the 34th International Conference on Machine Learning","author":"Guo C","year":"2017"},{"key":"e_1_2_6_19_1","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0262838","article-title":"Deep learning model calibration for improving performance in class\u2010imbalanced medical image classification tasks","volume":"17","author":"Sivaramakrishnan R","year":"2022","journal-title":"PLoS One"},{"key":"e_1_2_6_20_1","unstructured":"NattinoG FinazziS BertoliniG RossiC CarraraG.givitiR: the GiViTI calibration test and belt. 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