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An Automatic Scoliosis Diagnosis Platform Based on Deep Learning Approach

Authors:

Ying HuAuthors Info & Claims

APIT '22: Proceedings of the 2022 4th Asia Pacific Information Technology Conference

Pages 215 - 223

https://doi.org/10.1145/3512353.3512385

Published: 14 March 2022 Publication History

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Editorial Notes

The authors have requested minor, non-substantive changes to the Version of Record and, in according with ACM policies, a Corrected Version of Record was published on April 1, 2022. For reference purposes, the VoR may still be accessed via the Supplemental Material section on this page.

Abstract

Clinically, manual measurement of deformity diagnosis is the standard but costly in labor and time. Deep Learning makes lots of progressions in medical image processing. However, for spinal deformity diagnosis, they all directly predict the spinal classification or measure specific cobb angle without considering the clinical demand in surgery and treatment. This research proposed an automatic scoliosis system based on multiple U-Net networks, and it produced the segmentation of vertebral bodies. The in-rules algorithms automatically estimate the clinical measurements in coronal and sagittal spinal deformity from segmentation results. For the calculation of the parameters, the system presents the outcome absolute normalized errors with less than 0.35 in Std, and the Mean is just under 0.33. The main contribution is providing most of the primary clinical parameters, which is the essentials of spinal treatment and surgery customization. In addition, it is necessary to extend the dataset to improve the accuracy of deep learning models and furtherly verify the algorithms.

Supplementary Material

3512385-vor (3512385-vor.pdf)

Version of Record for "An Automatic Scoliosis Diagnosis Platform Based on Deep Learning Approach" by Li et al., 2022 4th Asia Pacific Information Technology Conference (APIT '22).

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Cited By

Du XWang HJiang LLv CXi YYang H(2025)Adjacent point aided vertebral landmark detection and Cobb angle measurement for automated AIS diagnosisComputerized Medical Imaging and Graphics10.1016/j.compmedimag.2025.102496121(102496)Online publication date: Apr-2025
https://doi.org/10.1016/j.compmedimag.2025.102496
Bottino LSettino MPromenzio LCannataro M(2025)Deep Learning for Scoliosis Diagnosis: Methods and DatabasesNumerical Computations: Theory and Algorithms10.1007/978-3-031-81247-7_3(26-39)Online publication date: 1-Jan-2025
https://doi.org/10.1007/978-3-031-81247-7_3
Goral SKose U(2022)Development of a CapsNet and Fuzzy Logic Decision Support System for Diagnosing the Scoliosis and Planning Treatments via Schroth MethodIEEE Access10.1109/ACCESS.2022.322776310(129055-129078)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3227763

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APIT '22: Proceedings of the 2022 4th Asia Pacific Information Technology Conference

January 2022

239 pages

ISBN:9781450395571

DOI:10.1145/3512353

Copyright © 2022 ACM.

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Publication History

Published: 14 March 2022

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Sanming Project of Medicine in Shenzhen
Key Fundamental Research Program of Shenzhen
National Natural Science Foundation of China
National Key Research and Development Program of China

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APIT 2022

APIT 2022: 2022 4th Asia Pacific Information Technology Conference

January 14 - 16, 2022

Virtual Event, Thailand

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Cited By

Du XWang HJiang LLv CXi YYang H(2025)Adjacent point aided vertebral landmark detection and Cobb angle measurement for automated AIS diagnosisComputerized Medical Imaging and Graphics10.1016/j.compmedimag.2025.102496121(102496)Online publication date: Apr-2025
https://doi.org/10.1016/j.compmedimag.2025.102496
Bottino LSettino MPromenzio LCannataro M(2025)Deep Learning for Scoliosis Diagnosis: Methods and DatabasesNumerical Computations: Theory and Algorithms10.1007/978-3-031-81247-7_3(26-39)Online publication date: 1-Jan-2025
https://doi.org/10.1007/978-3-031-81247-7_3
Goral SKose U(2022)Development of a CapsNet and Fuzzy Logic Decision Support System for Diagnosing the Scoliosis and Planning Treatments via Schroth MethodIEEE Access10.1109/ACCESS.2022.322776310(129055-129078)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3227763

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