Yingting Li

Abstract The assessment of coronal balance is critical for cosmetic appearance and quality of life in patients with adolescent idiopathic scoliosis (AIS), yet its manual quantification remains constrained by substantial workflow demands and inherent subjectivity. While artificial intelligence has shown promise in automated Cobb angle measurement, robust and clinically safety-aware solutions for coronal balance parameters are still lacking.

Abstract Fractures are among the most common presentations to emergency and orthopedic services, yet radiograph interpretation remains inconsistent, particularly in resource-limited and off-hours settings. Weakly supervised learning offers a scalable alternative to costly pixel-level annotation but often fails to capture subtle, focal fracture cues.

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