Auto‐ISC: Inter‐Sample Contrastive Learning for Automated Curve Angle Calculation From Ultrasound

Scoliosis assessment has been increasingly supported by AI‐assisted tools, enabling more efficient and standardised clinical workflows. However, existing ultrasound‐based pipelines often suffer from limited generalisation and unstable performance. These limitations primarily arise from two challenges: (1) suboptimal segmentation under low‐contrast and noisy imaging conditions, and (2) unreliable anatomical landmark identification due to missing or ambiguous features. To address these issues, we propose Auto‐ISC, a novel two‐stage automated ultrasound scoliosis assessment system. The first stage employs the ISC‐Swin segmentation model, enhanced by an Inter‐Sample Contrastive Bank (ISCB) to improve cross‐sample feature consistency and class‐level discrimination. The second stage introduces a dynamic pairing logic algorithm for accurate matching of thoracic vertebrae, enabling reliable downstream angle estimation. Experimental results demonstrate strong segmentation performance (Dice: 86.10%, IoU: 76.08%) and high agreement with manual measurements. The proposed method achieves low average absolute differences (ABS) (thoracic: 2.83° 2.16°; lumbar: 2.89° 2.55°) and strong correlations ( = 0.91 for thoracic and 0.81 for lumbar). Overall, Auto‐ISC provides a reliable and radiation‐free solution for automated scoliosis assessment, with improved clinical applicability.