TokenSTFormer：用于青少年特发性脊柱侧凸筛查中整体运动分析的分词时空注意力模型
TokenSTFormer: A Tokenized Spatial-temporal Attention Model for Holistic Motion Analysis in Adolescent Idiopathic Scoliosis Screening
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中文总结 AI 辅助
本研究针对青少年特发性脊柱侧凸筛查的传统方法局限，构建ScoliGait数据集并提出TokenSTFormer模型，其性能超越Vision Transformer，为AIS筛查提供了可扩展的新方案。
中文摘要 AI 辅助
青少年特发性脊柱侧凸（Adolescent Idiopathic Scoliosis, AIS）是青少年中普遍存在的脊柱畸形，若不治疗可导致严重健康后果。传统筛查方法受限于主观解读、依赖专业知识及低可扩展性。为应对这些挑战，我们推出ScoliGait数据集，包含1516个步态视频片段及对应的X光记录；同时引入TokenSTFormer这一新模型，该模型对空间和时间语义进行分词以增强特征表示与收敛性。我们的模型取得了超越传统Vision Transformer编码器的SOTA性能，关键指标中准确率达0.79。本研究凸显了利用步态视频衍生的整体运动特征与注意力模型实现可扩展、高性价比AIS筛查的潜力，为脊柱侧凸检测的未来临床应用铺平了道路。
英文摘要
Adolescent Idiopathic Scoliosis (AIS) is a prevalent spinal deformity in adolescents that, if left untreated, can result in severe health outcomes. Traditional screening methods are limited by subjective interpretation, reliance on professional expertise and low scalability. To address these challenges, we present ScoliGait dataset, which comprises 1,516 gait video clips paired with corresponding X-ray records. We also introduce TokenSTFormer, a novel model that tokenizes spatial and temporal semantics to enhance feature representation and convergence. Our model achieves state-of-the-art performance, surpassing vanilla Vision Transformer encoder across key metrics, including accuracy of 0.79. This study highlights the potential of leveraging holistic motion features derived from gait video and attention-based models for scalable, cost-effective AIS screening, paving the way for future clinical applications in scoliosis detection.
