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Computer-Aided Diagnosis for Determining Sagittal Spinal Curvatures Using Deep Learning and Radiography

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Abstract

Analyzing spinal curvatures manually is time-consuming and tedious for clinicians, and intra-observer and inter-observer variability can affect manual measurements. In this study, we developed and evaluated the performance of an automated deep learning–based computer-aided diagnosis (CAD) tool for measuring the sagittal alignment of the spine from X-ray images. The CAD system proposed here performs two functions: deep learning–based lateral spine segmentation and automatic analysis of thoracic kyphosis and lumbar lordosis angles. We utilized 322 datasets with data augmentation for learning and fivefold cross-validation. The segmentation model was based on U-Net, which has multiple applications in medical image processing. Here, we utilized parameter equations and trigonometric functions to design spinal angle measurement algorithms. The kyphosis (T4–T12) and lordosis angle (L1–S1, L1–L5) were automatically measured to help diagnose kyphosis and lordosis. The segmentation model had precision, sensitivity, and dice similarity coefficient values of 90.53 ± 4.61%, 89.53 ± 1.8%, and 90.22 ± 0.62%, respectively. The performance of the CAD algorithm was also verified with the Pearson correlation, Bland–Altman, and intra-class correlation coefficient (ICC) analysis. The proposed angle measurement algorithm exhibited high similarity and reliability during verification. Therefore, CAD can help clinicians in reaching a diagnosis by analyzing the sagittal spinal curvatures while reducing observer-based variability and the required time or effort.

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Funding

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2019R1G1A1100487), and by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2021–2017-0–01630) supervised by the IITP (Institute for Information & communications Technology Promotion), and by the GRRC program of Gyeonggi province. [GRRC-Gachon2020(B01), AI-based Medical Image Analysis.

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Authors and Affiliations

  1. Department of Biomedical Engineering, College of Health Science, Gachon University, Seongnam, South Korea

    Hyo Min Lee & Young Jae Kim

  2. Department of Medical Devices Convergence Center, Gachon University, Seongnam, South Korea

    Je Bok Cho

  3. Department of Radiology, Gil Medical Center, Gachon University College of Medicine, 21, Namdong-daero 774 beon-gil, Namdong-gu, Incheon, 21565, Republic of Korea

    Ji Young Jeon

  4. Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology (GAIHST), Gachon University, Seongnam, South Korea

    Kwang Gi Kim

  5. Department of Biomedical Engineering, Gil Medical Center, Gachon University College of Medicine, 21, Namdong-daero 774 beon-gil, Namdong-gu, Incheon, 21565, Republic of Korea

    Kwang Gi Kim

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Kwang Gi Kim and Ji Young Jeon contributed equally in this work.

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Correspondence to Ji Young Jeon or Kwang Gi Kim.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The study was approved by the institutional review board of Gachon University Gil Hospital (IRB number: GDIRB2019-137).

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Lee, H.M., Kim, Y.J., Cho, J.B. et al. Computer-Aided Diagnosis for Determining Sagittal Spinal Curvatures Using Deep Learning and Radiography. J Digit Imaging 35, 846–859 (2022). https://doi.org/10.1007/s10278-022-00592-0

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