Automation analysis X-ray of the spine to objectify the assessment of the severity of scoliotic deformity in idiopathic scoliosis: a preliminary report

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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.

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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 Petersburg

Natalia 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 Petersburg

Gulnar М. Mammadova

INPRIS Limited Liability Company

Email: mgm.gulnar@gmail.com
ORCID iD: 0000-0001-9738-9259

analyst

Russian Federation, Moscow

Andrey A. Smirnov

INPRIS Limited Liability Company

Email: smirnov.andrey.aleksandrovich@gmail.com
ORCID iD: 0000-0002-7062-5677

Analyst

Russian Federation, Moscow

Maxim M. Statsenko

Mail.ru Limited Liability Company

Email: maxstatsenko@gmail.com
ORCID iD: 0000-0002-6826-9116

head of the development team

Russian Federation, Moscow

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Supplementary files

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2. Fig. 1. Types of arches of idiopathic scoliosis in accordance with the classification of Rigo et al., 2010: TP — transitional point that could be located between the thoracic arch and the lumbar or thoracolumbar one relative to the central sacrum line; TP on the central sacrum line indicate that its balanced, while that installed beyond the line specified indicates its imbalance. T — thoracic, L — lumbar, CSL — central sacrum line

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3. Fig. 2. Neural network U-Net

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4. Fig. 3. An example of the result of the neural network operation

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5. Fig. 4. An example of the result of the program: a — the result of the program; b — the same image processed manually (digits in squares indicate the values of the Cobb angle of scoliotic deformity arches)

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Copyright (c) 2020 Lein G.A., Nechaeva N.S., Mammadova G.М., Smirnov A.A., Statsenko M.M.

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This work is licensed under a Creative Commons Attribution 4.0 International License.
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