{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T01:11:06Z","timestamp":1778202666489,"version":"3.51.4"},"reference-count":25,"publisher":"The Korean Spinal Neurosurgery Society","issue":"2","license":[{"start":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T00:00:00Z","timestamp":1777507200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/3.0\/"}],"funder":[{"DOI":"10.13039\/501100001700","name":"Ministry of Education, Culture, Sports, Science, and Technology","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001700","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100018117","name":"Aichi Health Promotion Foundation","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100018117","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100002241","name":"Japan Science and Technology Agency","doi-asserted-by":"publisher","award":["JPMJBS2422"],"award-info":[{"award-number":["JPMJBS2422"]}],"id":[{"id":"10.13039\/501100002241","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["e-neurospine.org"],"crossmark-restriction":true},"short-container-title":["NS"],"published-print":{"date-parts":[[2026,4,30]]},"abstract":"<jats:p>Objective: Maintaining the occipito-axial (O\u2013C2) angle following occipitocervical fusion is crucial to prevent postoperative complications. Although automated O\u2013C2 measurement has been reported, practical methods that provide rapid results for routine practice remain limited. This study aimed to develop a deep learning model using the YOLO (You Only Look Once) object detection algorithm to automatically identify anatomical landmarks and rapidly calculate the O\u2013C2 angle.Methods: A retrospective analysis was conducted using cervical spine radiographs from 2 independent facilities. The internal dataset comprised 574 lateral cervical radiographs from 271 patients for model development, while the external validation dataset included 100 radiographs from 100 patients. Model performance was evaluated against manual measurements by 3 expert raters.Results: The model demonstrated excellent detection performance, achieving perfect metrics for the hard palate (F1 score: 1.00) and high performance for the occipital bone (F1 score: 0.97), anteroinferior corner of C2 (F1 score: 0.99), and posteroinferior corner of C2 (F1 score: 0.99). For O\u2013C2 angle estimation, the mean absolute error was 2.35\u00b0 and root mean squared error was 2.98\u00b0, with an accuracy of 94.7% for determining the presence or absence of the O\u2013C2 angle (i.e., whether all 4 anatomical landmarks were simultaneously detected). Bland-Altman analysis revealed minimal bias (0.57\u00b0; 95% confidence interval, -0.06\u00b0 to 1.12\u00b0) with limits of agreement from -5.19\u00b0 to 6.33\u00b0. Inference time was approximately 0.14 s per image.Conclusion: Our deep learning model enables rapid and accurate O\u2013C2 angle measurement on lateral cervical radiographs, demonstrating performance comparable to expert raters and potential clinical utility.<\/jats:p>","DOI":"10.14245\/ns.2551878.939","type":"journal-article","created":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T00:07:03Z","timestamp":1777421223000},"page":"380-392","update-policy":"https:\/\/doi.org\/10.14245\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Automated Measurement of Occipito-Axial Angle on Cervical Radiographs Using a Deep Learning Object Detection Model: A Proof-of-Concept Study"],"prefix":"10.14245","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0895-5667","authenticated-orcid":true,"given":"Shun","family":"Yamamoto","sequence":"first","affiliation":[{"name":"Department of Neurosurgery, Nagoya University Graduate School of Medicine, Nagoya, 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