Classification of Adolescent Idiopathic Scoliosis Curvature Using Contrastive Clustering

Retrospective image analysis study.

To propose a novel classification system for adolescent idiopathic scoliosis (AIS) curvature using unsupervised machine learning and evaluate its reliability and clinical implications.

Existing AIS classification systems, such as King and Lenke, have limitations in accurately describing curve variations, particularly long C-shaped curves or curves with distinct characteristics. Unsupervised machine learning offers an opportunity to refine classification and enhance clinical decision-making.

A total of 1156 AIS patients who underwent deformity correction surgery were analyzed. Standard posteroanterior radiographs were segmented using U-net algorithms. Contrastive clustering was employed for automatic grouping, with the number of clusters ranging from three to 10. Cluster quality was assessed using t-SNE and Silhouette scores. Clusters were defined based on consensus among spine surgeons. Interobserver reliability was evaluated using kappa coefficients.

Six clusters were identified, reflecting variations in structural curve location, single (C-shaped) versus double (S-shaped) curves, and thoracolumbar curve characteristics. Cluster reliability was moderate (kappa = 0.701-0.731). The silhouette score was 0.308, with t-SNE demonstrating distinct clustering patterns. The classification highlighted differences not captured by the Lenke classification, such as thoracic curves confined to the thoracic spine versus those extending to the lumbar spine.

Unsupervised machine learning successfully categorized AIS curvatures into six distinct clusters, revealing meaningful patterns such as unique variations in thoracic and lumbar curves. These findings could potentially inform surgical planning and prognostic assessments. However, further studies are needed to validate clinical applicability and improve clustering quality.

Level III.