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

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.

Automated summary

In this study, an automated computer-aided diagnosis (CAD) tool based on deep learning was developed to measure the sagittal alignment of the spine from X-ray images. The CAD system achieved high accuracy in segmenting the spine and measuring the thoracic kyphosis and lumbar lordosis angles. The performance of the CAD algorithm was verified using various analysis methods and showed high similarity and reliability. Therefore, CAD can assist clinicians in diagnosing spinal curvatures while reducing observer-based variability and required time or effort.