Scalable and Robust Artificial Intelligence for Spine Alignment Assessment: Multicenter Study Enabled by Real-Time Data Transformation

Background: Artificial intelligence (AI) has shown promise for automating spinal alignment assessment in adolescent idiopathic scoliosis (AIS). However, AI models typically exhibit reduced accuracy and robustness when deployed across multiple medical centers due to variability in imaging protocols and data characteristics, potentially compromising clinical diagnosis and treatment decisions. Objective: This study aimed to develop a real-time, plug-and-play data transformation method to enhance the robustness of deep learning models against data heterogeneity in radiographs, thereby improving their performance in assessing AIS across multiple medical centers. Methods: In this retrospective multicenter study, 3899 full-spine radiographs from 7 hospitals (2 from Hong Kong and 5 from Mainland China), collected between January 2012 and August 2024, were included. Data from 2 hospitals in Hong Kong (n=3034) were used for model training and internal validation, while radiographs from the 5 mainland hospitals (n=865) formed 5 independent external validation datasets. A novel pixel intensity–based data transformation method was developed to standardize image contrast and brightness across datasets and integrated into the model training process to enhance our previously developed AI model, SpineHRNet+. The enhanced model’s accuracy and robustness for cobb angle (CA) prediction and severity classification were evaluated using both internal and external datasets. Data heterogeneity across centers was quantified by brightness and contrast differences. CA prediction accuracy was evaluated using residual analysis, linear regression (coefficient of determination [R²]), and Bland-Altman analyses. Model performance for disease severity classification was assessed using sensitivity, specificity, precision, negative predictive value, accuracy, and confusion matrix analysis. The transformation method aligns pixel intensity distributions across datasets using statistical profiling and optimization, ensuring consistent image characteristics while preserving anatomical integrity. Results: The developed data transformation method significantly reduced contrast variability between datasets, improving consistency in image characteristics and enabling more reliable AI analysis. The enhanced SpineHRNet+ achieved consistent and accurate CA predictions across external validation datasets, with mean prediction errors within 4° (SD 3.12°), and maintained an R² greater than 0.90 for all centers. The sensitivity and negative predictive value for disease severity grading improved to 90.18% and 93.16%, respectively. Bland-Altman analyses demonstrated robust agreement, with 95% limits of agreement within 7.51° across all datasets. Conclusions: The proposed data transformation approach effectively addressed data heterogeneity, significantly improving the accuracy and robustness of SpineHRNet+ in multicenter AIS assessments. The real-time processing capability and preservation of anatomical integrity underscore the method’s clinical practicality, enabling scalable and reliable AI applications in diverse health care environments. Trial Registration: