Validation of a decision support system for determining the severity of scoliotic spinal deformity using radiographic image analysis

Objective. To prove the possibility of using a domestic computer program in clinical practice to determine Cobb angle by means of comparative analysis of the obtained automated data with the data of manual measurement by specialists.

Material and Methods. A total of 411 digital x-rays of the spine of children and adolescents were selected from the medical database of Prosthetic and Orthopedic Center «Scоliologic.ru». They were measured by a radiologist with significant experience in vertebrology
(VR-standard), by a radiologist without experience in vertebrology (R-beginner) and a computer program (CP). The CP data were compared with the standard twice – initially (CP1) and after fine-tuning (CP2). The mean absolute error and mean absolute deviation of the standard data of Cobb angle measurements were analyzed when compared with the indicators obtained by R-beginner, CP1 and CP2 for different types of scoliosis according to the Rigo classification, and in determining the main curve of different magnitude from 20° to 41° and more. The Pearson coefficient (R) and the intraclass correlation coefficient (ICC) were calculated.

Results. After fine-tuning, the domestic computer program improved the accuracy of measurement in general for curves and types of  scoliosis, exceeding the R-beginner indicators almost twice in mean absolute error. The previously identified program drawback in measuring the magnitude of the lumbar (lumbosacral) curve was eliminated. The CP2 data have the highest correlation with the standard (R = 0.94). The excellent level of reliability of the program (ICC = 0.95 when counting on the main curve and 0.97 when counting on all curves) comparable with foreign analogues was proved. It was also confirmed that the average absolute deviation of ±3.2° and ±4.0° for the main curve corresponds to foreign data.

Conclusion. It is possible to conclude that the domestic computer program may be validated, since it has been proven that when compared with a reference measurement, its current algorithm provides accuracy higher than that of a radiologist with no experience in vertebrology, and is comparable with foreign analogues.

1. Srinivasalu S, Modi HN, Smehta S, Suh SW, Chen T, Murun T. Cobb angle measurement of scoliosis using computer measurement of digitally acquired radiographs-intraobserver and interobserver variability. Asian Spine J. 2008;2:90–93. DOI: 10.4184/ asj.2008.2.2.90

2. Kuklo TR, Potter BK, Shroeder TM, O’Brien MF. Comparison of manual and digital measurements in adolescent idiopathic scoliosis. Spine. 2006;31:1240–1246. DOI: 10.1097/01.brs.0000217774.13433.a7

3. Wong JC, Reformat MZ, Parent EC, Stampe KP, Southon Hryniuk SC, Lou EH. Validation of an artificial intelligence-based method to automate Cobb angle measurement on spinal radiographs of children with adolescent idiopathic scoliosis. Eur J Phys Rehabil Med. 2023;59:535–542. DOI: 10.23736/S1973-9087.23.08091-7

4. Zhang J, Lou E, Hill DL, Raso JV, Wang Y, Le LH, Shi X. Computer-aided assessment of scoliosis on posteroanterior radiographs. Med Biol Eng Comput. 2010;48:185–195. DOI: 10.1007/s11517-009-0556-7

5. Langensiepen S, Semler O, Sobottke R, Fricke O, Franklin J, Schonau E, Eysel P. Measuring procedures to determine the Cobb angle in idiopathic scoliosis: a systematic review. Eur Spine J. 2013;22:2360–2371. DOI: 10.1007/s00586-013-2693-9

6. Padalko MA, Orlov SV, Naumov AM, Nazarikov SI, Lushnikov AA. Automatic system for determining the angles of scoliotic deformity of the human spine. Vestnik IKBFU. Physics, Mathematics, and Technology. 2019;(3):55–68.

7. Lein GA, Nechaeva NS, Mamedova GM, Smirnov AA, Statsenko ММ. Automation analysis X-ray of the spine to objectify the assessment of the severity of scoliotic deformity in idiopathic scoliosis: A preliminary report. Pediatric Traumatology, Orthopaedics and Reconstructive Surgery. 2020;8(3):317–326. DOI: 10.17816/PTORS34150

8. Lein GA, Nechaeva NS, Demchenko MO, Кoch NS, Levykin AG. Efficiency of the decision support system for determining the severity of scoliotic spinal deformity by the analysis of radiographs. Pediatric Traumatology, Orthopaedics and Reconstructive Surgery. 2024;12(3):307–316. DOI: 10.17816/PTORS629748

9. Rigo MD, Villagrasa M, Gallo D. A specific scoliosis classification correlating with brace treatment: description and reliability. Scoliosis. 2010;5:1. DOI: 10.1186/1748-7161-5-1

10. Idiopathic Scoliosis: National Clinical Guidelines (approved by the Ministry of Health of Russia). Moscow, 2024.

11. Koo TK, Li MY. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med. 2016;15:155–163. DOI: 10.1016/j.jcm.2016.02.012

12. Chaddock RE. Principles and Methods of Statistics. Boston, New York, [etc.]; 1925.

13. Wang J, Zhang J, Xu R, Chen TG, Zhou KS, Zhang HH. Measurement of scoliosis Cobb angle by end vertebra tilt angle method. J Orthop Surg Res. 2018;13:223. DOI: 10.1186/ s13018-018-0928-5

14. Pan Y, Chen Q, Chen T, Wang H, Zhu X, Fang Z, Lu Y. Evaluation of a computer-aided method for measuring the Cobb angle on chest X-rays. Eur Spine J. 2019;28:3035–3043. DOI: 10.1007/s00586-019-06115-w

15. Zhang K, Xu N, Guo C, Wu J. MPF-net: An effective framework for automated cobb angle estimation. Med Image Anal. 2022;75:102277. DOI: 10.1016/j.media.2021.102277

16. Huang X, Luo M, Liu L, Wu D, You X, Deng Z, Xiu P, Yang X, Zhou C, Feng G, Wang L, Zhou Z, Fan J, He M, Gao Z, Pu L, Wu Z, Zhou Z, Song Y, Huang S. The comparison of convolutional neural networks and the manual measurement of cobb angle in adolescent idiopathic scoliosis. Global Spine J. 2024;14:159–168. DOI: 10.1177/21925682221098672

17. Wang MX, Kim JK, Choi JW, Park D, Chang MC. Deep learning algorithm for automatically measuring Cobb angle in patients with idiopathic scoliosis. Eur Spine J. 2024;33:4155–4163. DOI: 10.1007/s00586-023-08024-5

18. Prestigiacomo FG, Hulsbosch MHHM, Bruls VEJ, Nieuwenhuis JJ. Intra- and inter-observer reliability of Cobb angle measurements in patients with adolescent idiopathic scoliosis. Spine Deform. 2022;10:79–86. DOI: 10.1007/s43390-021-00398-0

19. Caesarendra W, Rahmaniar W, Mathew J., Thien A. Automated Cobb angle measurement for adolescent idiopathic scoliosis using convolutional neural network. Diagnostics (Basel). 2022;12:396. DOI: 10.3390/diagnostics12020396

20. Sun Y, Xing Y, Zhao Z, Meng X, Xu G, Hai Y. Comparison of manual versus automated measurement of Cobb angle in idiopathic scoliosis based on a deep learning keypoint detection technology. Eur Spine J. 2022;31:1969–1978. DOI: 10.1007/s00586-021-07025-6