Accuracy of a new intellectual method in measuring Cobb’s angle on spinal radiographs and the possibility of its clinical application

Authors

  • Dima Kassab St. Petersburg State University, 7–9, Universitetskaya nab., St. Petersburg, 199034, Russian Federation
  • Irina Kamyshanskaya St. Petersburg State University, 7–9, Universitetskaya nab., St. Petersburg, 199034, Russian Federation ; St. Petersburg City Mariinsky Hospital, 56, Liteynii pr., St. Petersburg, 191014, Russian Federation
  • Stanislav Trukhan LLC “Esper”, 24, ul. Uspenskaia, Krasnogorsk, 143409, Russian Federation
  • Natalia Ladogubets St. Petersburg State University, 7–9, Universitetskaya nab., St. Petersburg, 199034, Russian Federation

DOI:

https://doi.org/10.21638/spbu11.2023.305

Abstract

Cobb’s angle is until now considered the gold standard method for measuring the angle of scoliosis. The subjectivity of this method has always been its main disadvantage. The aim of this work is to evaluate a new system (computer program) “Esper.Scoliosis”, based on artificial neural networks that can measure Cobb’s angle automatically on frontal radiographs. We compared the angles measured by the automatic system with measurements of a radiologist using a testing set of 114 digital X-rays with variable grades of scoliosis. In 84.8 % of scoliotic curvatures detected by the system, no significant measurement variability (< 2,5°) of the angles was found between the two methods. The system shows better results in X-rays with scoliosis
grades 1 and 2. In our work, image quality has the largest effect on accuracy of the system and
measurement’s variability. We concluded that controlled clinical use of “Esper.Scoliosis” for automatic Cobb’s angle measurement is recommended.

Keywords:

scoliosis, radiographs, artificial intelligence, spine, artificial neural networks

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References

Литература

Negrini S., Donzelli S., Aulisa A. G., Czaprowski D., Schreiber S., de Mauroy J. C., Diers H., Grivas T. B.,Knott P., Kotwicki T., Lebel A., Marti C., Maruyama T., O’Brien J., Price N., Parent E., Rigo M., Romano M., Stikeleather L., Wynne J., Zaina F. 2016 SOSORT guidelines: orthopedic and rehabilitation treatment of idiopathic scoliosis during growth // Scoliosis and Spinal Disorders. 2018. Vol. 13, no. 3.https://doi.org/10.1186/s13013-017-0145-8

Ketenci İ. E., Yanik H. S., Erdoğan Ö., Adıyeke L., Erdem Ş. Reliability of 2 Smartphone Applications for Cobb Angle Measurement in Scoliosis // Clin. Orthop. Surg. 2021. Vol. 13, no. 1. P. 67–70.

Briganti G., Le Moine O. Artificial intelligence in medicine: Today and Tomorrow // Front. Med. 2020.Vol. 5. P. 7–27.

Amisha, Malik P., Pathania M., Rathaur V. K. Overview of artificial intelligence in medicine // Journal of Family Medicine and Primary Care. 2019. Vol. 8, no. 7. P. 2328–2331.

Rong L. A. Review of medical artificial intelligence // Global Health Journal. 2020. Vol. 4, no. 2. P. 42–45.

Huang J., Li J., Li Z., Zhu Z., Shen C., Qi G., Yu G. Detection of diseases using machine learning image recognition technology in artificial intelligence // Computational Intelligence Neuroscience. 2022.April 13. https://doi.org/10.1155/2022/5658641

Гусев А. В. Перспективы нейронных сетей и глубокого машинного обучения в создании решений для здравоохранения // Врач и информационные технологии. 2017. № 3. С. 92–105.

Ортопедия: клинические рекомендации / под ред. С. П. Миронова. М.: ГЭОТАР-Медиа, 2018.


References

Negrini S., Donzelli S., Aulisa A. G., Czaprowski D., Schreiber S., de Mauroy J. C., Diers H.,Grivas T. B., Knott P., Kotwicki T., Lebel A., Marti C., Maruyama T., O’Brien J., Price N., Parent E.,Rigo M., Romano M., Stikeleather L., Wynne J., Zaina F. 2016 SOSORT guidelines: orthopedic and rehabilitation treatment of idiopathic scoliosis during growth. Scoliosis and Spinal Disorders, 2018, vol. 13, no. 3. https://doi.org/10.1186/s13013-017-0145-8

Ketenci İ. E., Yanik H. S., Erdoğan Ö., Adıyeke L., Erdem Ş. Reliability of 2 Smartphone Applications for Cobb Angle Measurement in Scoliosis. Clin. Orthop. Surg., 2021, vol. 13, no. 1, pp. 67–70.

Briganti G., Le Moine O. Artificial intelligence in medicine: Today and Tomorrow. Front. Med., 2020,vol. 5, pp. 7–27.

Amisha, Malik P., Pathania M., Rathaur V. K. Overview of artificial intelligence in medicine. Journal of Family Medicine and Primary Care, 2019, vol. 8, no. 7, pp. 2328–2331.

Rong L. A. Review of medical artificial intelligence. Global Health Journal, 2020, vol. 4, no. 2, pp. 42–45.

Huang J., Li J., Li Z., Zhu Z., Shen C., Qi G., Yu G. Detection of diseases using machine learning image recognition technology in artificial intelligence. Computational Intelligence Neuroscience, 2022,April 13. https://doi.org/10.1155/2022/5658641

Gusеv A. V. Prospects for neural networks and deep machine learning in creating health solutions.Information technologies for the Physician, 2017, no. 3, pp. 92–105. (In Russian)

Orthopedics: clinical guidelines, ed. by S. P. Mironov. Moscow, GEOTAR-Media Publ., 2018, 784 p.(In Russian)

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Published

2024-03-19 — Updated on 2024-04-02

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How to Cite

Kassab, D., Kamyshanskaya, I., Trukhan, S., & Ladogubets, N. . (2024). Accuracy of a new intellectual method in measuring Cobb’s angle on spinal radiographs and the possibility of its clinical application. Vestnik of Saint Petersburg University. Medicine, 18(3), 293–303. https://doi.org/10.21638/spbu11.2023.305 (Original work published March 19, 2024)

Issue

Vol. 18 No. 3 (2023)

Section

Diagnostic radiology, therapy radiology

License

Articles of "Vestnik of Saint Petersburg University. Medicine" are open access distributed under the terms of the License Agreement with Saint Petersburg State University, which permits to the authors unrestricted distribution and self-archiving free of charge.