  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
labor-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. This software integrates deep neural
network-based spine region detection and segmentation, spine centerline
identification, pinpointing the most significantly tilted vertebrae, and direct
visualization of Cobb angles on the original images. 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 intra-class correlation coefficients (ICC) exceeding 0.96 and Pearson
correlation coefficients above 0.944, reflecting robust agreement with expert
assessments and superior measurement reliability. 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 aiding physicians in more
accurate scoliosis assessment and diagnosis, thereby improving patient care.
