Fully Automated Deep Learning-Based Lenke Classification for Adolescent Idiopathic Scoliosis Using Multi-View...

Study DesignRetrospective validation study.ObjectivesTo develop and validate a fully automated deep learning framework for radiographic measurement and Lenke classification in adolescent idiopathic scoliosis (AIS) using multi-view full-spine radiographs.MethodsConsecutive patients with AIS from 3 hospitals who underwent standardized 4-view full-spine radiography between January 2019 and January 2026 were retrospectively reviewed. The automated framework incorporated vertebral detection, vertebra-level landmark estimation, radiographic parameter computation, and rule-based Lenke classification. Expert manual measurements and consensus Lenke classification served as the reference standard. Performance was assessed on an independent test set using detection and keypoint metrics, Cobb angle measurement agreement, classification accuracy, clinician agreement, and workflow efficiency.ResultsIn 76 independent test cases, the framework achieved a vertebral detection AP@0.75 of 0.858 and an overall keypoint AP of 0.951. Cobb angle measurement showed a mean absolute error of 2.4°, with excellent agreement with expert measurements (ICC = 0.991; Pearson correlation = 0.987; Spearman correlation = 0.983). Accuracy was 0.895 for Lenke curve type, 0.868 for lumbar modifier, 0.908 for sagittal thoracic modifier, and 0.816 for the complete Lenke label. Agreement between the AI system and the senior surgeon was high ( κ = 0.873 for curve type; κ = 0.781 for complete label). Mean processing time was 12.3 seconds per case, and AI-assisted review reduced mean review time from 8.2 to 1.5 minutes per case.ConclusionsThis fully automated deep learning framework achieved accurate radiographic measurement and Lenke classification with strong agreement with spine surgeons and marked efficiency gains. The method may serve as an interpretable decision-support tool for preoperative AIS assessment.