Scholarlink

Objectives <p>To develop a deep learning model to automatically measure the curvature-related sagittal parameters on cervical spinal X-ray images.</p> Methods <p>This retrospective study collected a total of 700 lateral cervical spine X-ray images from three hospitals, consisting of 500 training sets, 100 internal test sets, and 100 external test sets. 6 measured parameters and 34 landmarks were measured and labeled by two doctors and averaged as the gold standard. A Convolutional neural network (CNN) model was built by training on 500 images and testing on 200 images. Statistical analysis is used to evaluate labeling differences and model performance.</p> Results <p>The percentages of the difference in distance between landmarks within 4&#xa0;mm were 96.90% (Dr. A vs. Dr. B), 98.47% (Dr. A vs. model), and 97.31% (Dr. B vs. model); within 3&#xa0;mm were 94.88% (Dr. A vs. Dr. B), 96.43% (Dr. A vs. model), and 94.16% (Dr. B vs. model). The mean difference of the algorithmic model in labeling landmarks was 1.17 ± 1.14&#xa0;mm. The mean absolute error (MAE) of the algorithmic model for the Borden method, Cervical curvature index (CCI), Vertebral centroid measurement cervical lordosis (CCL), C<sub>0</sub>–C<sub>7</sub> Cobb, C<sub>1</sub>–C<sub>7</sub> Cobb, C<sub>2</sub>–C<sub>7</sub> Cobb in the test sets are 1.67&#xa0;mm, 2.01%, 3.22°, 2.37°, 2.49°, 2.81°, respectively; symmetric mean absolute percentage error (SMAPE) was 20.06%, 21.68%, 20.02%, 6.68%, 5.28%, 20.46%, respectively. Also, the algorithmic model of the six cervical sagittal parameters is in good agreement with the gold standard (intraclass correlation efficiency was 0.983; <i>p</i> &lt; 0.001).</p> Conclusion <p>Our deep learning algorithmic model had high accuracy in recognizing the landmarks of the cervical spine and automatically measuring cervical spine-related parameters, which can help radiologists improve their diagnostic efficiency.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Development of a deep learning model for measuring sagittal parameters on cervical spine X-ray

  • 期刊论文

  • 发表日期：

    2025年6月5日

查看全文
  • Simin Wang,
  • Kun Li,
  • Shaojie Zhang,
  • Dezhou Zhang,
  • Yunteng Hao,
  • Yongtang Zhou,
  • Chaoqun Wang,
  • Hailong Zhao,
  • Yuan Ma,
  • Danyang Zhao,
  • Jie Chen,
  • Xiaohe Li,
  • Huaqiong Wang,
  • Zhijun Li,
  • Jun Shi,
  • Xing Wang

摘要

Objectives

To develop a deep learning model to automatically measure the curvature-related sagittal parameters on cervical spinal X-ray images.

Methods

This retrospective study collected a total of 700 lateral cervical spine X-ray images from three hospitals, consisting of 500 training sets, 100 internal test sets, and 100 external test sets. 6 measured parameters and 34 landmarks were measured and labeled by two doctors and averaged as the gold standard. A Convolutional neural network (CNN) model was built by training on 500 images and testing on 200 images. Statistical analysis is used to evaluate labeling differences and model performance.

Results

The percentages of the difference in distance between landmarks within 4 mm were 96.90% (Dr. A vs. Dr. B), 98.47% (Dr. A vs. model), and 97.31% (Dr. B vs. model); within 3 mm were 94.88% (Dr. A vs. Dr. B), 96.43% (Dr. A vs. model), and 94.16% (Dr. B vs. model). The mean difference of the algorithmic model in labeling landmarks was 1.17 ± 1.14 mm. The mean absolute error (MAE) of the algorithmic model for the Borden method, Cervical curvature index (CCI), Vertebral centroid measurement cervical lordosis (CCL), C0–C7 Cobb, C1–C7 Cobb, C2–C7 Cobb in the test sets are 1.67 mm, 2.01%, 3.22°, 2.37°, 2.49°, 2.81°, respectively; symmetric mean absolute percentage error (SMAPE) was 20.06%, 21.68%, 20.02%, 6.68%, 5.28%, 20.46%, respectively. Also, the algorithmic model of the six cervical sagittal parameters is in good agreement with the gold standard (intraclass correlation efficiency was 0.983; p < 0.001).

Conclusion

Our deep learning algorithmic model had high accuracy in recognizing the landmarks of the cervical spine and automatically measuring cervical spine-related parameters, which can help radiologists improve their diagnostic efficiency.