Deep Learning for Cervical Spine Radiography: Automated Measurement of Intervertebral and Neural Foraminal Distances

Huang, Ya-Yun; Wang, Hong-Kai; Chi, Tsun-Kuang; Liu, Chao-Shin; Tsai, Sung-Hsin; Liong, Sze-Teng; Chen, Tsung-Yi; Li, Kuo-Chen; Tu, Wei-Chen; R. Abu, Patricia Angela

doi:10.3390/diagnostics15172162

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Deep Learning for Cervical Spine Radiography: Automated Measurement of Intervertebral and Neural Foraminal Distances

by

Ya-Yun Huang

¹

,

Hong-Kai Wang

²

,

Tsun-Kuang Chi

^3,*

,

Chao-Shin Liu

⁴,

Sung-Hsin Tsai

⁵,

Sze-Teng Liong

⁴,

Tsung-Yi Chen

^4,*

,

Kuo-Chen Li

⁶

,

Wei-Chen Tu

^1,7

and

Patricia Angela R. Abu

⁸

¹

Program on Semiconductor Manufacturing Technology, Academy of Innovative Semiconductor and Sustainable Manufacturing, National Cheng Kung University, Tainan City 701401, Taiwan

²

Department of Neurosurgery, Linkou Chang Gung Memorial Hospital, Taoyuan City 333423, Taiwan

³

Department of Electrical Engineering, Ming Chi University of Technology, New Taipei City 243303, Taiwan

⁴

Department of Electronic Engineering, Feng Chia University, Taichung City 40724, Taiwan

⁵

Department of Medical Education, Chang Gung Memorial Hospital Linkou, Taoyuan City 333423, Taiwan

⁶

Department of Information Management, Chung Yuan Christian University, Taoyuan City 320317, Taiwan

⁷

Department of Electronic Engineering, National Cheng Kung University, Tainan City 701401, Taiwan

⁸

Ateneo Laboratory for Intelligent Visual Environments, Department of Information Systems and Computer Science, Ateneo de Manila University, Quezon City 1108, Philippines

^*

Authors to whom correspondence should be addressed.

Diagnostics 2025, 15(17), 2162; https://doi.org/10.3390/diagnostics15172162

Submission received: 22 July 2025 / Revised: 20 August 2025 / Accepted: 25 August 2025 / Published: 26 August 2025

(This article belongs to the Section Machine Learning and Artificial Intelligence in Diagnostics)

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Abstract

Background/Objectives: The precise localization of cervical vertebrae in X-ray imaging was essential for effective diagnosis and treatment planning, particularly as the prevalence of cervical degenerative conditions increased with an aging population. 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.

Keywords: YOLOv8; cervical spine localization; neural foramen distance measurement; deep learning; vertebral segmentation; spinal radiography

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MDPI and ACS Style

Huang, Y.-Y.; Wang, H.-K.; Chi, T.-K.; Liu, C.-S.; Tsai, S.-H.; Liong, S.-T.; Chen, T.-Y.; Li, K.-C.; Tu, W.-C.; R. Abu, P.A. Deep Learning for Cervical Spine Radiography: Automated Measurement of Intervertebral and Neural Foraminal Distances. Diagnostics 2025, 15, 2162. https://doi.org/10.3390/diagnostics15172162

AMA Style

Huang Y-Y, Wang H-K, Chi T-K, Liu C-S, Tsai S-H, Liong S-T, Chen T-Y, Li K-C, Tu W-C, R. Abu PA. Deep Learning for Cervical Spine Radiography: Automated Measurement of Intervertebral and Neural Foraminal Distances. Diagnostics. 2025; 15(17):2162. https://doi.org/10.3390/diagnostics15172162

Chicago/Turabian Style

Huang, Ya-Yun, Hong-Kai Wang, Tsun-Kuang Chi, Chao-Shin Liu, Sung-Hsin Tsai, Sze-Teng Liong, Tsung-Yi Chen, Kuo-Chen Li, Wei-Chen Tu, and Patricia Angela R. Abu. 2025. "Deep Learning for Cervical Spine Radiography: Automated Measurement of Intervertebral and Neural Foraminal Distances" Diagnostics 15, no. 17: 2162. https://doi.org/10.3390/diagnostics15172162

APA Style

Huang, Y.-Y., Wang, H.-K., Chi, T.-K., Liu, C.-S., Tsai, S.-H., Liong, S.-T., Chen, T.-Y., Li, K.-C., Tu, W.-C., & R. Abu, P. A. (2025). Deep Learning for Cervical Spine Radiography: Automated Measurement of Intervertebral and Neural Foraminal Distances. Diagnostics, 15(17), 2162. https://doi.org/10.3390/diagnostics15172162

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Deep Learning for Cervical Spine Radiography: Automated Measurement of Intervertebral and Neural Foraminal Distances

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