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Vertebrae from C2 to C7 were commonly affected by disorders such as ossification of the posterior longitudinal ligament (OPLL) and nerve compression caused by posterior osteophytes, necessitating thorough evaluation. However, manual annotation remained a major aspect of traditional clinical procedures, making it challenging to manage increasing patient volumes and large-scale medical imaging data. Methods: To address this issue, this study presented an automated approach for localizing cervical vertebrae and measuring neural foraminal distance. The proposed technique analyzed the neural foramen distance and intervertebral space using image enhancement to determine the degree of nerve compression. YOLOv8 was employed to detect and segment the cervical vertebrae. Moreover, by integrating automated cervical spine analysis with advanced imaging technologies, the system enabled rapid detection of abnormal intervertebral disc gaps, facilitating early identification of degenerative changes. Results: According to the results, the system achieved a spine localization accuracy of 99.5%, representing an 11.7% improvement over existing approaches. Notably, it outperformed previous methods by 66.67% in recognizing the C7 vertebra, achieving a perfect 100% accuracy. Conclusions: Furthermore, the system significantly streamlined the diagnostic workflow by processing each X-ray image in just 17.9 milliseconds. This approach markedly improved overall diagnostic efficiency.<\/jats:p>","DOI":"10.3390\/diagnostics15172162","type":"journal-article","created":{"date-parts":[[2025,8,26]],"date-time":"2025-08-26T14:18:49Z","timestamp":1756217929000},"page":"2162","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Deep Learning for Cervical Spine Radiography: Automated Measurement of Intervertebral and Neural Foraminal Distances"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-0505-3375","authenticated-orcid":false,"given":"Ya-Yun","family":"Huang","sequence":"first","affiliation":[{"name":"Program on Semiconductor Manufacturing Technology, Academy of Innovative Semiconductor and Sustainable Manufacturing, National Cheng Kung University, Tainan City 701401, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3826-5832","authenticated-orcid":false,"given":"Hong-Kai","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Linkou Chang Gung Memorial Hospital, Taoyuan City 333423, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7109-6492","authenticated-orcid":false,"given":"Tsun-Kuang","family":"Chi","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Ming Chi University of Technology, New Taipei City 243303, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao-Shin","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Feng Chia University, Taichung City 40724, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sung-Hsin","family":"Tsai","sequence":"additional","affiliation":[{"name":"Department of Medical Education, Chang Gung Memorial Hospital Linkou, Taoyuan City 333423, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sze-Teng","family":"Liong","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Feng Chia University, Taichung City 40724, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-5964-6084","authenticated-orcid":false,"given":"Tsung-Yi","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Feng Chia University, Taichung City 40724, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0110-5491","authenticated-orcid":false,"given":"Kuo-Chen","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Information Management, Chung Yuan Christian University, Taoyuan City 320317, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1134-5859","authenticated-orcid":false,"given":"Wei-Chen","family":"Tu","sequence":"additional","affiliation":[{"name":"Program on Semiconductor Manufacturing Technology, Academy of Innovative Semiconductor and Sustainable Manufacturing, National Cheng Kung University, Tainan City 701401, Taiwan"},{"name":"Department of Electronic Engineering, National Cheng Kung University, Tainan City 701401, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8848-6644","authenticated-orcid":false,"given":"Patricia Angela R.","family":"Abu","sequence":"additional","affiliation":[{"name":"Ateneo Laboratory for Intelligent Visual Environments, Department of Information Systems and Computer Science, Ateneo de Manila University, Quezon City 1108, Philippines"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,26]]},"reference":[{"key":"ref_1","first-page":"599","article-title":"An ink-insensitive deep learning model for improving the printing quality in extrusion-based bioprinting","volume":"11","author":"Tseng","year":"2025","journal-title":"Int. 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