Development and validation of an automatic diagnostic tool for lumbar stability based on deep learning_Chinese Journal of Reparative and Reconstructive Surgery

Authors：

HU Houmin ¹ , WANG Xiandi ² , YANG Heng ¹ , ZHANG Jin ¹ ,  LI Kang ³ ,  ZENG Jiancheng ²

1. College of Electrical Engineering, Sichuan University, Chengdu Sichuan, 610041, P. R. China;
2. Orthopedic Research Institute, Department of Orthopedics, West China Hospital, Sichuan University, Chengdu Sichuan, 610041, P. R. China;
3. West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu Sichuan, 610041, P. R. China;

Corresponding author：

LI Kang, Email: likang@wchscu.cn; ZENG Jiancheng, Email: tomzeng5@vip.163.com

Keywords：

Artificial intelligence; medical image processing; key point location; lumbar instability; auxiliary diagnosis

DOI：

10.7507/1002-1892.202209058

Video：

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Objective To develop an automatic diagnostic tool based on deep learning for lumbar spine stability and validate diagnostic accuracy. Methods Preoperative lumbar hyper-flexion and hyper-extension X-ray films were collected from 153 patients with lumbar disease. The following 5 key points were marked by 3 orthopedic surgeons: L₄ posteroinferior, anterior inferior angles as well as L₅ posterosuperior, anterior superior, and posterior inferior angles. The labeling results of each surgeon were preserved independently, and a total of three sets of labeling results were obtained. A total of 306 lumbar X-ray films were randomly divided into training (n=156), validation (n=50), and test (n=100) sets in a ratio of 3∶1∶2. A new neural network architecture, Swin-PGNet was proposed, which was trained using annotated radiograph images to automatically locate the lumbar vertebral key points and calculate L_{4, 5} intervertebral Cobb angle and L₄ lumbar sliding distance through the predicted key points. The mean error and intra-class correlation coefficient (ICC) were used as an evaluation index, to compare the differences between surgeons’ annotations and Swin-PGNet on the three tasks (key point positioning, Cobb angle measurement, and lumbar sliding distance measurement). Meanwhile, the change of Cobb angle more than 11° was taken as the criterion of lumbar instability, and the lumbar sliding distance more than 3 mm was taken as the criterion of lumbar spondylolisthesis. The accuracy of surgeon annotation and Swin-PGNet in judging lumbar instability was compared. Results ① Key point: The mean error of key point location by Swin-PGNet was (1.407±0.939) mm, and by different surgeons was (3.034±2.612) mm. ② Cobb angle: The mean error of Swin-PGNet was (2.062±1.352)° and the mean error of surgeons was (3.580±2.338)°. There was no significant difference between Swin-PGNet and surgeons (P>0.05), but there was a significant difference between different surgeons (P<0.05). ③ Lumbar sliding distance: The mean error of Swin-PGNet was (1.656±0.878) mm and the mean error of surgeons was (1.884±1.612) mm. There was no significant difference between Swin-PGNet and surgeons and between different surgeons (P>0.05). The accuracy of lumbar instability diagnosed by surgeons and Swin-PGNet was 75.3% and 84.0%, respectively. The accuracy of lumbar spondylolisthesis diagnosed by surgeons and Swin-PGNet was 70.7% and 71.3%, respectively. There was no significant difference between Swin-PGNet and surgeons, as well as between different surgeons (P>0.05). ④ Consistency of lumbar stability diagnosis: The ICC of Cobb angle among different surgeons was 0.913 [95%CI (0.898, 0.934)] (P<0.05), and the ICC of lumbar sliding distance was 0.741 [95%CI (0.729, 0.796)] (P<0.05). The result showed that the annotating of the three surgeons were consistent. The ICC of Cobb angle between Swin-PGNet and surgeons was 0.922 [95%CI (0.891, 0.938)] (P<0.05), and the ICC of lumbar sliding distance was 0.748 [95%CI(0.726, 0.783)] (P<0.05). The result showed that the annotating of Swin-PGNet were consistent with those of surgeons. Conclusion The automatic diagnostic tool for lumbar instability constructed based on deep learning can realize the automatic identification of lumbar instability and spondylolisthesis accurately and conveniently, which can effectively assist clinical diagnosis.

Citation： HU Houmin, WANG Xiandi, YANG Heng, ZHANG Jin, LI Kang, ZENG Jiancheng. Development and validation of an automatic diagnostic tool for lumbar stability based on deep learning. Chinese Journal of Reparative and Reconstructive Surgery, 2023, 37(1): 81-90. doi: 10.7507/1002-1892.202209058 Copy

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16.	Sun Y, Xing Y, Zhao Z, et al. Comparison of manual versus automated measurement of Cobb angle in idiopathic scoliosis based on a deep learning keypoint detection technology. Eur Spine J, 2022, 31(8): 1969-1978.
17.	Pan Y, Chen Q, Chen T, et al. Evaluation of a computer-aided method for measuring the Cobb angle on chest X-rays. Eur Spine J, 2019, 28(12): 3035-3043.
18.	Liu J, Yuan C, Sun X, et al. The measurement of Cobb angle based on spine X-ray images using multi-scale convolutional neural network. Phys Eng Sci Med, 2021, 44(3): 809-821.
19.	Liu Z, Lin Y, Cao Y, et al. Swin transformer: Hierarchical vision transformer using shifted windows//2021 IEEE/CVF International Conference on Computer Vision (ICCV). USA: IEEE, 2021.
20.	He K, Zhang X, Ren S, et al. Deep residual learning for image recognition//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . USA: IEEE, 2016.
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22.	Hu H, Gu J, Zhang Z, et al. Relation networks for object detection//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. USA: IEEE, 2018.
23.	Hu H, Zhang Z, Xie Z, et al. Local relation networks for image recognition//2019 IEEE/CVF International Conference on Computer Vision (ICCV). USA: IEEE, 2019.
24.	Papandreou G, Zhu T, Kanazawa N, et al. Towards accurate multi-person pose estimation in the wild//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). USA: IEEE, 2017.
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26.	Quinnell RC, Stockdale HR. Flexion and extension radiography of the lumbar spine: a comparison with lumbar discography. Clin Radiol, 1983, 34(4): 405-411.

1. Schwab JH. Global sagittal alignment. Skeletal Radiology, 2017, (46): 1613-1614.
2. Duval-Beaupère G, Schmidt C, Cosson P. A Barycentremetric study of the sagittal shape of spine and pelvis: the conditions required for an economic standing position. Ann Biomed Eng, 1992, 20(4): 451-462.
3. Diebo BG, Varghese JJ, Lafage R, et al. Sagittal alignment of the spine: What do you need to know? Clin Neurol Neurosurg, 2015, 139: 295-301.
4. Berven S, Wadhwa R. Sagittal alignment of the lumbar spine. Neurosurg Clin N Am, 2018, 29(3): 331-339.
5. Vrtovec T, Pernus F, Likar B. A review of methods for quantitative evaluation of spinal curvature. Eur Spine J, 2009, 18(5): 593-607.
6. Zhang J, Li H, Lv L, et al. Computer-aided Cobb measurement based on automatic detection of vertebral slopes using deep neural network. Int J Biomed Imaging, 2017, 2017: 9083916. doi: 10.1155/2017/9083916.
7. Smith JS, Shaffrey CI, Ames CP, et al. Treatment of adult thoracolumbar spinal deformity: past, present, and future. J Neurosurg Spine, 2019, 30(5): 551-567.
8. Lafage R, Ferrero E, Henry JK, et al. Validation of a new computer-assisted tool to measure spino-pelvic parameters. Spine J, 2015, 15(12): 2493-2502.
9. Sun H, Zhen X, Bailey C, et al. Direct estimation of spinal Cobb angles by structured multi-output regression//International Conference on Information Processing in Medical Imaging. Cham: Springer, 2017.
10. Langensiepen S, Semler O, Sobottke R, et al. Measuring procedures to determine the Cobb angle in idiopathic scoliosis: a systematic review. Eur Spine J, 2013, 22(11): 2360-2371.
11. Zhang J, Lou E, Le LH, et al. Automatic Cobb measurement of scoliosis based on fuzzy Hough Transform with vertebral shape prior. J Digit Imaging, 2009, 22(5): 463-472.
12. Alukaev D, Kiselev S, Mustafaev T, et al. A deep learning framework for vertebral morphometry and Cobb angle measurement with external validation. Eur Spine J, 2022, 31(8): 2115-2124.
13. Wang L, Xu Q, Leung S, et al. Accurate automated Cobb angles estimation using multi-view extrapolation net. Med Image Anal, 2019, 58: 101542. doi: 10.1016/j.media.2019.101542.
14. Fu X, Yang G, Zhang K, et al. An automated estimator for Cobb angel measurement using multi-task networks. Neural Computing and Applications, 2021, 33: 4755-4761.
15. Zhao Y, Zhang J, Li H, et al. Automatic Cobb angle measurement method based on vertebra segmentation by deep learning. Med Biol Eng Comput, 2022, 60(8): 2257-2269.
16. Sun Y, Xing Y, Zhao Z, et al. Comparison of manual versus automated measurement of Cobb angle in idiopathic scoliosis based on a deep learning keypoint detection technology. Eur Spine J, 2022, 31(8): 1969-1978.
17. Pan Y, Chen Q, Chen T, et al. Evaluation of a computer-aided method for measuring the Cobb angle on chest X-rays. Eur Spine J, 2019, 28(12): 3035-3043.
18. Liu J, Yuan C, Sun X, et al. The measurement of Cobb angle based on spine X-ray images using multi-scale convolutional neural network. Phys Eng Sci Med, 2021, 44(3): 809-821.
19. Liu Z, Lin Y, Cao Y, et al. Swin transformer: Hierarchical vision transformer using shifted windows//2021 IEEE/CVF International Conference on Computer Vision (ICCV). USA: IEEE, 2021.
20. He K, Zhang X, Ren S, et al. Deep residual learning for image recognition//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . USA: IEEE, 2016.
21. Ronneberger O, Fischer P, Brox T. U-Net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015. Cham: Springer, 2015.
22. Hu H, Gu J, Zhang Z, et al. Relation networks for object detection//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. USA: IEEE, 2018.
23. Hu H, Zhang Z, Xie Z, et al. Local relation networks for image recognition//2019 IEEE/CVF International Conference on Computer Vision (ICCV). USA: IEEE, 2019.
24. Papandreou G, Zhu T, Kanazawa N, et al. Towards accurate multi-person pose estimation in the wild//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). USA: IEEE, 2017.
25. Boden SD, Wiesel SW. Lumbosacral segmental motion in normal individuals. Have we been measuring instability properly? Spine (Phila Pa 1976), 1990, 15(6): 571-576.
26. Quinnell RC, Stockdale HR. Flexion and extension radiography of the lumbar spine: a comparison with lumbar discography. Clin Radiol, 1983, 34(4): 405-411.

Chinese Journal of Reparative and Reconstructive Surgery

Development and validation of an automatic diagnostic tool for lumbar stability based on deep learning

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