Computer-Aided Diagnosis for Determining Sagittal Spinal Curvatures Using Deep Learning and Radiography
- Authors
- Lee, Hyo Min; Kim, Young Jae; Cho, Je Bok; Jeon, Ji Young; Kim, Kwang Gi
- Issue Date
- Aug-2022
- Publisher
- SPRINGER
- Keywords
- Deep learning; Segmentation; Computer-aided diagnosis; Thoracic kyphosis; Lumbar lordosis
- Citation
- JOURNAL OF DIGITAL IMAGING, v.35, no.4, pp.846 - 859
- Journal Title
- JOURNAL OF DIGITAL IMAGING
- Volume
- 35
- Number
- 4
- Start Page
- 846
- End Page
- 859
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/85497
- DOI
- 10.1007/s10278-022-00592-0
- ISSN
- 0897-1889
- 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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Collections - 의과대학 > 의학과 > 1. Journal Articles
- 보건과학대학 > 의용생체공학과 > 1. Journal Articles
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