Adolescent Idiopathic Scoliosis (AIS) is a prevalent spinal deformity whose progression can be mitigated through early detection. Conventional screening methods are often subjective, difficult to scale, and reliant on specialized clinical expertise. Video-based gait analysis offers a promising alternative, but current datasets and methods frequently suffer from data leakage, where performance is inflated by repeated clips from the same individual, or employ oversimplified models that lack clinical interpretability. To address these limitations, we introduce ScoliGait, a new benchmark dataset comprising 1,572 gait video clips for training and 300 fully independent clips for testing. Each clip is annotated with radiographic Cobb angles and descriptive text based on clinical kinematic priors. We propose a multi-modal framework that integrates a clinical-prior-guided kinematic knowledge map for interpretable feature representation, alongside a latent attention pooling mechanism to fuse video, text, and knowledge map modalities. Our method establishes a new state-of-the-art, demonstrating a significant performance gap on a realistic, non-repeating subject benchmark. Our approach establishes a new state of the art, showing a significant performance gain on a realistic, subject-independent benchmark. This work provides a robust, interpretable, and clinically grounded foundation for scalable, non-invasive AIS assessment.

翻译：青少年特发性脊柱侧弯（AIS）是一种常见的脊柱畸形，早期检测可有效延缓其进展。传统筛查方法通常具有主观性强、难以规模化实施且依赖专业临床经验的局限。基于视频的步态分析为此提供了有前景的替代方案，但现有数据集与方法常受数据泄露问题困扰（即因同一受试者的重复视频片段导致性能评估虚高），或采用过于简化、缺乏临床可解释性的模型。为应对这些挑战，本研究提出了ScoliGait——一个包含1,572个训练用步态视频片段及300个完全独立测试片段的新基准数据集。每个片段均依据临床运动学先验知识，标注了X射线科布角与描述性文本。我们设计了一个多模态框架，该框架整合了临床先验引导的运动学知识图谱以实现可解释的特征表征，并采用潜在注意力池化机制融合视频、文本与知识图谱模态。我们的方法在真实、非重复受试者的基准测试中取得了显著性能优势，确立了新的技术标杆。本研究为可扩展、非侵入性的AIS评估奠定了鲁棒、可解释且具有临床依据的基础。