具有潜在注意力池的临床先验引导多模态学习用于基于步态的脊柱侧弯筛查
Clinical-Prior Guided Multi-Modal Learning with Latent Attention Pooling for Gait-Based Scoliosis Screening
AI总结：
本文提出了一种基于步态的脊柱侧弯筛查方法，通过多模态学习和潜在注意力池化机制，结合临床先验知识，实现了可解释的特征表示和高精度的筛查性能。
摘要：
青少年特发性脊柱侧弯（AIS）是一种常见的脊柱畸形，其进展可以通过早期检测来缓解。传统筛查方法往往主观性强，难以扩展，并依赖于专业的临床经验。基于视频的步态分析提供了一种有前景的替代方案，但当前的数据集和方法经常受到数据泄露的影响，性能被同一个体重复片段夸大，或使用过于简化的模型，缺乏临床可解释性。为了解决这些限制，我们引入了ScoliGait，一个新的基准数据集，包含1,572个用于训练的步态视频片段和300个完全独立的测试片段。每个片段都标注了放射学Cobb角和基于临床运动先验的描述性文本。我们提出了一种多模态框架，整合了临床先验引导的运动知识图谱以实现可解释的特征表示，以及潜在注意力池化机制以融合视频、文本和知识图谱模态。我们的方法建立了新的最先进的状态，展示了在现实的、非重复的受试者基准上的显著性能差距。我们的方法在现实的、受试者无关的基准上展示了显著的性能提升。这项工作为可扩展、非侵入性的AIS评估提供了稳健、可解释和临床基础的框架。
英文摘要：
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.
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