{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T16:08:57Z","timestamp":1773763737794,"version":"3.50.1"},"reference-count":36,"publisher":"American Association for the Advancement of Science (AAAS)","funder":[{"name":"Capital\u2019s Funds for Health Improvement and Research","award":["2024-2-40912"],"award-info":[{"award-number":["2024-2-40912"]}]},{"name":"Innovation and Transformation Project, Peking University Third Hospital","award":["BYSYZHKC106"],"award-info":[{"award-number":["BYSYZHKC106"]}]}],"content-domain":{"domain":["spj.science.org"],"crossmark-restriction":true},"short-container-title":["Intell Comput"],"published-print":{"date-parts":[[2026,1,1]]},"abstract":"<jats:p>\n                    <jats:bold>Background:<\/jats:bold>\n                    The identification of difficult laryngoscopy is a critical skill for anesthesiologists, particularly in high-risk procedures such as cervical spine surgery, which demands heightened accuracy in preoperative airway assessment. This study developed a deep learning algorithm, the double-pose feature clustering network (DPFCNet), designed to improve the identification of patients with difficult laryngoscopy through comprehensive analysis of neutral and extended cervical spine x-ray images.\n                    <jats:bold>Methods:<\/jats:bold>\n                    In this prospective cohort study, 14,407 patients who underwent elective cervical spine surgery under general anesthesia (July 2016 to July 2023) were initially enrolled. Following rigorous screening, 1,568 participants were eligible and included in the analysis, comprising 319 difficult laryngoscopy cases and 1,249 easy laryngoscopy cases. The study evaluated the effectiveness of a deep learning model for identifying difficult laryngoscopy that was developed using a ResNet-based feature extraction module with dual-position image fusion technology and systematically compared it against conventional bedside assessments using area under the receiver operating characteristic curve (AUC) analysis.\n                    <jats:bold>Results:<\/jats:bold>\n                    Conventional clinical indicators showed poor predictive capacity for difficult laryngoscopy: thyromental distance (AUC = 0.594), neck circumference (AUC = 0.662), inter-incisor gap (AUC = 0.607), and modified Mallampati test (AUC = 0.624). Their combination achieved AUC = 0.743. In contrast, DPFCNet considerably outperformed conventional methods with AUC = 0.866, demonstrating superior discriminative power.\n                    <jats:bold>Conclusion:<\/jats:bold>\n                    This study employed a deep learning model for the comprehensive analysis of both neutral and extended cervical spine x-ray images, considerably improving the prediction accuracy of difficult laryngoscopy. DPFCNet provides an effective decision-support tool for preoperative risk stratification in cervical spondylosis patients.\n                  <\/jats:p>","DOI":"10.34133\/icomputing.0321","type":"journal-article","created":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T12:26:03Z","timestamp":1773318363000},"update-policy":"https:\/\/doi.org\/10.34133\/aaas_crossmark_01","source":"Crossref","is-referenced-by-count":0,"title":["Identification of Difficult Laryngoscopy via Double-Pose Feature Clustering Network in Patients with Cervical Spondylosis"],"prefix":"10.34133","volume":"5","author":[{"given":"Mingya","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Anesthesiology, \rPeking University Third Hospital, Beijing 100191, China."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fei","family":"Jia","sequence":"additional","affiliation":[{"name":"Cloud Computing and Big Data Research Institute, China Academy of Information and Communications Technology (CAICT), Beijing 100191, China."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cuiying","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology, General Hospital of Yangquan Coal Industry Group, Yangquan, Shanxi 045000, China."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingchao","family":"Fang","sequence":"additional","affiliation":[{"name":"Department of Radiology, \rPeking University Third Hospital, Beijing 00191, China."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiao","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology, \rPeking University Third Hospital, Beijing 100191, China."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Tian","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology, \rPeking University Third Hospital, Beijing 100191, China."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Min","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology, \rPeking University Third Hospital, Beijing 100191, China."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangyang","family":"Guo","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology, \rPeking University Third Hospital, Beijing 100191, China."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Changwei","family":"Wei","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology, Beijing Chaoyang Hospital, \rCapital Medical University, Beijing 100020, China."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7523-8801","authenticated-orcid":true,"given":"Yongzheng","family":"Han","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology, \rPeking University Third Hospital, Beijing 100191, China."}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"221","published-online":{"date-parts":[[2026,3,17]]},"reference":[{"issue":"6","key":"e_1_3_4_2_2","doi-asserted-by":"crossref","first-page":"428","DOI":"10.4103\/ija.IJA_250_19","article-title":"Focused review on management of the difficult paediatric airway","volume":"63","author":"Huang AS","year":"2019","unstructured":"Huang AS, Hajduk J, Rim C, Coffield S, Jagannathan N. 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