用步态模式识别脊柱侧弯，无辐射且准确率高
Leveraging Gait Patterns as Biomarkers: An attention-guided Deep Multiple Instance Learning Network for Scoliosis Classification
- 通过注意力引导的多实例学习，从步态中提取关键特征
- 对难判别的中性病例准确率显著提升，达92.3%
- 适合大规模筛查，尤其在数据不平衡时表现稳定
脊柱侧弯是一种难以早期发现的脊柱弯曲疾病，可能压迫胸腔，影响呼吸和心脏功能。青少年若延误检测与治疗，会导致压缩加剧。传统检测依赖临床经验，且X光检查存在辐射风险，限制了大规模早期筛查。本文提出一种注意力引导的深度多实例学习方法（Gait-MIL），有效捕捉步态模式中的判别特征，基于首个大规模步态数据集进行评估。结果表明，该方法显著提升了以步态为生物标志物的脊柱侧弯检测性能，尤其在难以判断的中性病例中准确率明显提高。Gait-MIL在数据不平衡场景下仍具鲁棒性，具备成为大规模筛查工具的潜力。
原文摘要 · Abstract (English)
Scoliosis is a spinal curvature disorder that is difficult to detect early and can compress the chest cavity, impacting respiratory function and cardiac health. Especially for adolescents, delayed detection and treatment result in worsening compression. Traditional scoliosis detection methods heavily rely on clinical expertise, and X-ray imaging poses radiation risks, limiting large-scale early screening. We propose an Attention-Guided Deep Multi-Instance Learning method (Gait-MIL) to effectively capture discriminative features from gait patterns, which is inspired by ScoNet-MT's pioneering use of gait patterns for scoliosis detection. We evaluate our method on the first large-scale dataset based on gait patterns for scoliosis classification. The results demonstrate that our study improves the performance of using gait as a biomarker for scoliosis detection, significantly enhances detection accuracy for the particularly challenging Neutral cases, where subtle indicators are often overlooked. Our Gait-MIL also performs robustly in imbalanced scenarios, making it a promising tool for large-scale scoliosis screening.
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