{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,1,16]],"date-time":"2025-01-16T05:36:01Z","timestamp":1737005761067,"version":"3.33.0"},"reference-count":34,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,5,26]],"date-time":"2024-05-26T00:00:00Z","timestamp":1716681600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Capital\u2019s Funds for Health Improvement and Research","award":["2020-2-6012"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"The increase in Cervical Spondylosis cases and the expansion of the affected demographic to younger patients have escalated the demand for X-ray screening. Challenges include variability in imaging technology, differences in equipment specifications, and the diverse experience levels of clinicians, which collectively hinder diagnostic accuracy. In response, a deep learning approach utilizing a ResNet-34 convolutional neural network has been developed. This model, trained on a comprehensive dataset of 1235 cervical spine X-ray images representing a wide range of projection angles, aims to mitigate these issues by providing a robust tool for diagnosis. Validation of the model was performed on an independent set of 136 X-ray images, also varied in projection angles, to ensure its efficacy across diverse clinical scenarios. The model achieved a classification accuracy of 89.7%, significantly outperforming the traditional manual diagnostic approach, which has an accuracy of 68.3%. This advancement demonstrates the viability of deep learning models to not only complement but enhance the diagnostic capabilities of clinicians in identifying Cervical Spondylosis, offering a promising avenue for improving diagnostic accuracy and efficiency in clinical settings.<\/jats:p>","DOI":"10.3390\/s24113428","type":"journal-article","created":{"date-parts":[[2024,5,27]],"date-time":"2024-05-27T13:33:31Z","timestamp":1716816811000},"page":"3428","source":"Crossref","is-referenced-by-count":1,"title":["Cervical Spondylosis Diagnosis Based on Convolutional Neural Network with X-ray Images"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-9025-8384","authenticated-orcid":false,"given":"Yang","family":"Xie","sequence":"first","affiliation":[{"name":"Department of Medical Imaging, China Rehabilitation Research Center and Capital Medical University School of Rehabilitation Medicine, Beijing 100068, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1840-791X","authenticated-orcid":false,"given":"Yali","family":"Nie","sequence":"additional","affiliation":[{"name":"Department of Electronics Design, Mid Sweden University, 85170 Sundsvall, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1819-6200","authenticated-orcid":false,"given":"Jan","family":"Lundgren","sequence":"additional","affiliation":[{"name":"Department of Electronics Design, Mid Sweden University, 85170 Sundsvall, Sweden"}]},{"given":"Mingliang","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Spinal and Neural Function Reconstruction, China Rehabilitation Research Center and Capital Medical University School of Rehabilitation Medicine, Beijing 100068, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8617-0435","authenticated-orcid":false,"given":"Yuxuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Electronics Design, Mid Sweden University, 85170 Sundsvall, Sweden"}]},{"given":"Zhenbo","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging, China Rehabilitation Research Center and Capital Medical University School of Rehabilitation Medicine, Beijing 100068, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1056\/NEJMra2003558","article-title":"Degenerative cervical spondylosis","volume":"383","author":"Theodore","year":"2020","journal-title":"N. 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