Automation analysis X-ray of the spine to objectify the assessment of the severity of scoliotic deformity in idiopathic scoliosis: a preliminary report
- Authors: Lein G.A.1, Nechaeva N.S.1, Mammadova G.М.2, Smirnov A.A.2, Statsenko M.M.3
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Affiliations:
- Scoliologic.ru Limited Liability Company
- INPRIS Limited Liability Company
- Mail.ru Limited Liability Company
- Issue: Vol 8, No 3 (2020)
- Pages: 317-326
- Section: Experimental and theoretical research
- Submitted: 22.05.2020
- Accepted: 03.08.2020
- Published: 06.10.2020
- URL: https://journals.eco-vector.com/turner/article/view/34150
- DOI: https://doi.org/10.17816/PTORS34150
- ID: 34150
Cite item
Abstract
Background. A large number of studies have focused on automating the process of measuring the Cobb angle. Although there is no practical tool to assist doctors with estimating the severity of the curvature of the spine and determine the best suitable treatment type.
Aim. We aimed to examine the algorithms used for distinguishing vertebral column, vertebrae, and for building a tangent on the X-ray photographs. The superior algorithms should be implemented into the clinical practice as an instrument of automatic analysis of the spine X-rays in scoliosis patients.
Materials and methods. A total of 300 digital X-rays of the spine of children with idiopathic scoliosis were gathered. The X-rays were manually ruled by a radiologist to determine the Cobb angle. This data was included into the main dataset used for training and validating the neural network. In addition, the Sliding Window Method algorithm was implemented and compared with the machine learning algorithms, proving it to be vastly superior in the context of this research.
Results. This research can serve as the foundation for the future development of an automated system for analyzing spine X-rays. This system allows processing of a large amount of data for achieving >85% in training neural network to determine the Cobb angle.
Conclusions. This research is the first step toward the development of a modern innovative product that uses artificial intelligence for distinguishing the different portions of the spine on 2D X-ray images for building the lines required to determine the Cobb angle.
Keywords
Full Text
About the authors
Grigory A. Lein
Scoliologic.ru Limited Liability Company
Author for correspondence.
Email: Lein@scoliologic.ru
ORCID iD: 0000-0001-7904-8688
MD, traumatologist-orthopedist, PhD, General Director of Scoliologic.ru LLC
Russian Federation, Saint PetersburgNatalia S. Nechaeva
Scoliologic.ru Limited Liability Company
Email: n.nechaeva@scoliologic.ru
ORCID iD: 0000-0003-3510-9164
MD, scientific worker, radiologist
Russian Federation, Saint PetersburgGulnar М. Mammadova
INPRIS Limited Liability Company
Email: mgm.gulnar@gmail.com
ORCID iD: 0000-0001-9738-9259
analyst
Russian Federation, MoscowAndrey A. Smirnov
INPRIS Limited Liability Company
Email: smirnov.andrey.aleksandrovich@gmail.com
ORCID iD: 0000-0002-7062-5677
Analyst
Russian Federation, MoscowMaxim M. Statsenko
Mail.ru Limited Liability Company
Email: maxstatsenko@gmail.com
ORCID iD: 0000-0002-6826-9116
head of the development team
Russian Federation, MoscowReferences
- Ferguson AB. The study and treatment of scoliosis. South Med J. 1930;23(2):116-120.
- Сobb JR. Outline for the study of scoliosis. Instr Course Lect AAOS. 1948;5:261-275.
- Jentschura G. Zur pathogenese der säuglingsskoliose. Archiv für orthopädische und Unfall-Chirurgie, mit besonderer Berücksichtigung der Frakturenlehre und der orthopädisch-chirurgischen Technik. 1956;48(5):582-603.
- Абальмасова Е.А. Сколиоз в рентгеновском изображении и его измерение // Ортопедия и травматология. – 1964. – № 5. – С. 49–50. [Abalmasova EA. Skolioz v rentgenovskom izobrazhenii i ego izmerenie. Ortopediya i travmatologiya. 1964;(5):49-50. (In Russ.)]
- Тесаков Д.К., Тесакова Д.Д. Рентгенологические методики измерения дуг сколиотической деформации позвоночника во фронтальной плоскости и их сравнительный анализ // Проблемы здоровья и экологии. – 2007. – № 3. – С. 94–103. [Tesakov DK, Tesakova DD. Roetgenological methods of scoliotic spine deformity estimation in frontal plane and their comparative analysis. Problemy zdorov’ya i ekologii. 2007;(3):94-103. (In Russ.)]
- SOSORT. Методические рекомендации SOSORT 2011 г. Ортопедическое и реабилитационное лечение подросткового идиопатического сколиоза. 2011. [SOSORT. Metodicheskie rekomendatsii SOSORT 2011 g. Ortopedicheskoe i reabilitatsionnoe lechenie podrostkovogo idiopaticheskogo skolioza. 2011. (In Russ.)]
- Ньютон П.О., О’Браен М.Ф., Шаффлбаргер Г.Л., и др. Идиопатический сколиоз. Исследовательская группа Хармса: руководство по лечению. – М.: Лаборатория знаний, 2018. – 479 с. [Newton PO, O’Brien MF, Schafflebarger GL, et al. Idiopaticheskiy skolioz. Issledovatel’skaya gruppa Kharmsa: Rukovodstvo po lecheniyu. Moscow: Laboratoriya znaniy; 2018. 479 p. (In Russ.)]
- Wilson MS, Stockwell J, Leedy MG. Measurement of scoliosis by orthopedic surgeons and radiologists. Aviat Space Environ Med. 1983;54(1):69-71.
- Tanure MC, Pinheiro AP, Oliveira AS. Reliability assessment of Cobb angle measurements using manual and digital methods. Spine J. 2010;10(9):769-774. https://doi.org/10.1016/j.spinee.2010.02.020.
- Suwannarat P, Wattanapan P, Wiyanad A, et al. Reliability of novice physiotherapists for measuring Cobb angle using a digital method. Hong Kong Physiother J. 2017;37:34-38. https://doi.org/10.1016/ j.hkpj.2017.01.003.
- Wang J, Zhang J, Xu R, et al. Measurement of scoliosis Cobb angle by end vertebra tilt angle method. J Orthop Surg Res. 2018;13(1):223. https://doi.org/10.1186/s13018-018-0928-5.
- Horng MH, Kuok CP, Fu MJ, et al. Cobb angle measurement of spine from X-Ray images using convolutional neural network. Comput Math Methods Med. 2019;2019:6357171. https://doi.org/10.1155/ 2019/6357171.
- 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. https://doi.org/10.1007/s00586-019-06115-w.
- Safari A, Parsaei H, Zamani A, Pourabbas B. A Semi-Automatic algorithm for estimating Cobb angle. J Biomed Phys Eng. 2019;9(3):317-326. https://doi.org/10.31661/jbpe.v9i3Jun.730.
- Qiao J, Liu Z, Xu L, et al. Reliability analysis of a smartphone-aided measurement method for the Cobb angle of scoliosis. J Spinal Disord Tech. 2012;25(4):E88-92. https://doi.org/10.1097/BSD.0b013e3182463964.
- Jones JK, Krow A, Hariharan S, Weekes L. Measuring angles on digitalized radiographic images using Microsoft PowerPoint. West Indian Med J. 2008;57(1):14-19.
- Rigo MD, Villagrasa M, Gallo D. A specific scoliosis classification correlating with brace treatment: Description and reliability. Scoliosis. 2010;5(1):1. https://doi.org/10.1186/1748-7161-5-1.
- He К, Gkioxari G, Dollár P, Girshick R. Mask R-CNN. 2017. arXiv: 1703.06870.
- Long J, Shelhamer E, Darrell T. Fully Convolutional Networks for Semantic Segmentation. 2014. arXiv: 1411.4038.
- He K, Zhang X, Ren S, Sun J. Deep Residual Learning for Image Recognition. 2015. arXiv: 1512.03385.
- Ronneberger O, Fischer P, Brox T. U-Net: Convolutional Networks for Biomedical Image Segmentation. 2015. arXiv: 1505.04597.
- Liu W, Rabinovich A, Berg AC. ParseNet: Looking Wider to See Better. 2015. arXiv: 1506.04579.
- Mukherjee J, Kundu R, Chakrabarti A. Variability of Cobb angle measurement from digital X-ray image based on different de-noising techniques. Int J Biomed Eng Technol. 2014;16(2):113. https://doi.org/10.1504/ijbet. 2014.065656.
- Okashi OA, Du H, Al-Assam H. Automatic spine curvature estimation from X-ray images of a mouse model. Comput Methods Programs Biomed. 2017;140:175-184. https://doi.org/10.1016/j.cmpb.2016.12.010.
- Pinheiro AP, Coelho JC, Veiga ACP, Vrtovec T. A computerized method for evaluating scoliotic deformities using elliptical pattern recognition in X-ray spine images. Comput Methods Programs Biomed. 2018;161:85-92. ttps://doi.org/10.1016/j.cmpb.2018.04.015.