Artificial Intelligence-based Models for Spine Malalignment Auto-analysis

This retrospective study aimed to enhance and validate a model for diagnosing adolescent idiopathic scoliosis (AIS) across multiple medical centers. The study included 2,763 participants from prestigious hospitals in mainland China and Hong Kong. X-rays were used to develop and validate the model, with data from different hospitals to ensure robustness. Participants aged 10-18 with confirmed AIS were enrolled, and data were deidentified for privacy. The model was optimized using training data and validated internally before being deployed for real-world application. A novel data augmentation technique was used to address data heterogeneity, and a standardized analysis platform, AlignProCARE, was employed for evaluation. X-rays were annotated with vertebra landmarks, and traditional and intensity-based data augmentation methods were applied for image processing. Coronal Cobb angle was used to evaluate spinal alignment, with severity classified as normal-mild, moderate, or severe. The model's performance was statistically assessed for accuracy in predicting Cobb angle and severity grading. Overall, the study aimed to provide a reliable diagnostic tool for AIS analysis in clinical practice, improving efficiency and standardization in diagnosis and treatment.