Deep learning automates Cobb angle measurement compared with multi-expert observers.

Objectives: Scoliosis, a prevalent condition characterized by abnormal spinal curvature leading to deformity, requires precise assessment methods for effective diagnosis and management. The Cobb angle is a widely used scoliosis quantification method that measures the degree of curvature between the tilted vertebrae. Yet, manual measuring of Cobb angles is time-consuming and labour-intensive, fraught with significant interobserver and intraobserver variability. To address these challenges and the lack of interpretability found in certain existing automated methods, we have created fully automated software that not only precisely measures the Cobb angle but also provides clear visualizations of these measurements. Methods: This software integrates a deep neural network-based spine region detection and segmentation, spine centreline identification, pinpointing the most significantly tilted vertebrae, and direct visualization of Cobb angles on the original images. Results: Upon comparison with the assessments of 7 expert readers, our algorithm exhibited a mean deviation in Cobb angle measurements of 4.17 degrees, notably surpassing the manual approach's average intra-reader discrepancy of 5.16 degrees. The algorithm also achieved intraclass correlation coefficients (ICC) exceeding 0.96 and Pearson correlation coefficients above 0.944, reflecting robust agreement with expert assessments and superior measurement reliability. Conclusions: Through the comprehensive reader study and statistical analysis, we believe this algorithm not only ensures a higher consensus with expert readers but also enhances interpretability and reproducibility during assessments. It holds significant promise for clinical application, potentially helping physicians assess and diagnose scoliosis more accurately, thus improving patient care. Advances in knowledge: A fully automated Cobb angle measurement algorithm was compared with a comprehensive multi-expert study and demonstrated superior performance relative to human observers. The code is publicly available at GitHub.