Developing a Spine Internal Rotation Angle Measurement System Based Machine Learning Using CT Reconstructed X-ray Anteroposterior Image

Tae-Seok Kang and Seung-Man Yu ()
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Tae-Seok Kang: Department of Radiologic Science, College of Medical Sciences, Jeonju University, Jeonju 55069, Republic of Korea
Seung-Man Yu: Department of Radiologic Science, College of Medical Sciences, Jeonju University, Jeonju 55069, Republic of Korea

Mathematics, 2022, vol. 10, issue 24, 1-11

Abstract: The purpose of this study was to develop a predictive model for estimating the rotation angle of the vertebral body on X-ray anteroposterior projection (AP) image by applying machine learning. This study is intended to replace internal/external rotation of the thoracic spine (T-spine), which can only be observed through computed tomography (CT), with an X-ray AP image. 3-dimension (3D) T-spine CT images were used to acquired reference spine axial angle and various internal rotation T-spine reconstructed X-ray AP image. Distance from the pedicle to the outside of the spine and change in distance between the periphery of the pedicle according to the rotation of the spine were designated as main variables using reconstructed X-ray AP image. The number of measured spines was 453 and the number of variables for each spine was 13, creating a total of 5889 data. We applied a total of 24 regression machine learning methods using MATLAB software, performed learning with the acquired data, and finally, the Gaussian regression method showed the lowest RMSE value. X-rays obtained with the phantom of the human body tilted by 16 degrees showed results with reproducibility within the RMSE range.

Keywords: scoliosis; X-ray image; machine learning; computed tomography (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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