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Semi-automatic method for pre-surgery scoliosis classification on X-ray images using Bending Asymmetry Index

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Bending Asymmetry Index (BAI) has been proposed to characterize the types of scoliotic curve in three-dimensional ultrasound imaging. Scolioscan has demonstrated its validity and reliability in scoliosis assessment with manual assessment-based X-ray imaging. The objective of this study is to investigate the ultrasound-derived BAI method to X-ray imaging of scoliosis, with supplementary information provided for the pre-surgery planning.

Methods

About 30 pre-surgery scoliosis subjects (9 males and 21 females; Cobb: 50.9 ± 19.7°, range 18°–115°) were investigated retrospectively. Each subject underwent three-posture X-ray scanning supine on a plain mattress on the same day. BAI is an indicator to distinguish structural or non-structural curves through the spine flexibility information obtained from lateral bending spinal profiles. BAI was calculated semi-automatically with manual annotation of vertebral centroids and pelvis level inclination adjustment. BAI classification was validated with the scoliotic curve type and traditional Lenke classification using side-bending Cobb angle measurement (S-Cobb).

Results

82 curves from 30 pre-surgery scoliosis patients were included. The correlation coefficient was R2 = 0.730 (p < 0.05) between BAI and S-Cobb. In terms of scoliotic curve type classification, all curves were correctly classified; out of 30 subjects, 1 case was confirmed as misclassified when applying to Lenke classification earlier, thus has been adjusted.

Conclusion

BAI method has demonstrated its inter-modality versatility in X-ray imaging application. The curve type classification and the pre-surgery Lenke classification both indicated promising performances upon the exploratory dataset. A fully-automated of BAI measurement is surely an interesting direction to continue our endeavor. Deep learning on the vertebral-level segmentation should be involved in further study.

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Funding

This study is partially supported by Research Impact Fund of Hong Kong Research Grant Council (R5017-18) and Health and Medical Research Fund of the Hong Kong (04152896).

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Authors and Affiliations

  1. Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China

    D. Yang, T. T. Y. Lee, K. K. L. Lai & Yong Ping Zheng

  2. SH Ho Scoliosis Research Lab, Joint Scoliosis Research Center of the Chinese University of Hong Kong and Nanjing University, Department of Orthopaedics and Traumatology, The Chinese University of Hong Kong, Shatin, Hong Kong

    T. P. Lam & J. C. Y. Cheng

  3. Department of Orthopaedic Surgery, University Medical Center Utrecht, Utrecht, Netherlands

    R. M. Castelein

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  1. D. Yang
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Correspondence to Yong Ping Zheng.

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Conflict of interest

The research has not yet been published, submitted or accepted for publication. This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of the Hong Kong Polytechnic University (06 Sep 2018/HSEARS20180906005). The correspondence author Zheng Y.P. owned a number of patents related to the Scolioscan system (derived the original ultrasonic parameter ‘BAI’), which have been licensed to Telefield Medical Imaging Limited for commercialization. Zheng YP held a consultant position at the Hong Kong Telefield Medical Imaging Ltd for the ongoing improvement of Scolioscan system. Other authors [Yang D., Lee T.T.Y., Lai K.K.L., Lam T.P., Castelein R.M., Cheng J.C.Y] have no relevant financial or non-financial interests to disclose.

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Yang, D., Lee, T.T.Y., Lai, K.K.L. et al. Semi-automatic method for pre-surgery scoliosis classification on X-ray images using Bending Asymmetry Index. Int J CARS 17, 2239–2251 (2022). https://doi.org/10.1007/s11548-022-02740-x

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