Radiographic Analysis of Scoliosis Using Convolutional Neural Network in Clinical Practice

Abstract

Purpose To assess the reliability and accuracy of an automated Cobb angle measurement (ACAM) using a convolutional neural network (CNN) for scoliosis evaluation and to compare measurement times. Materials and Methods ACAM was applied to spine radiographs in 411 patients suspected of scoliosis. Observer 1 (consensus of two musculoskeletal radiologists) and observer 2 (a radiology resident) measured Cobb angle (CA). CA measurements were categorized using observer 1’s measurements as the reference standard. Inter-observer reliability and correlation were assessed using intraclass correlation coefficient (ICC) and Spearman’s rank correlation coefficient, respectively. Accuracy and measurement time of ACAM and observers were evaluated. Results ACAM demonstrated excellent reliability and very high correlation with observer 1 (ICC = 0.976, Spearman’s rank correlation = 0.948), with a mean CA difference of 1.1. Overall accuracy was high (88.2%), particularly in mild (92.2%) and moderate (96%) scoliosis. Accuracy was lower in spinal asymmetry (77.1%) and higher in severe scoliosis (95%), although the CA was lower compared to the observers. ACAM significantly reduced measurement time by nearly half compared to the observers (p < 0.001). Conclusion ACAM using CNN enhances CA measurement for assessing mild or moderate scoliosis, despite limitations in spinal asymmetry or severe scoliosis. Nonetheless, it substantially decreases measurement time.

Keywords

• scoliosis
• cobb angle
• convolutional neural network
• radiography