Scoliosis poses significant diagnostic challenges, particularly in adolescents, where early detection is crucial for effective treatment. Traditional diagnostic and follow-up methods, which rely on physical examinations and radiography, face limitations due to the need for clinical expertise and the risk of radiation exposure, thus restricting their use for widespread early screening. In response, we introduce a novel, video-based, non-invasive method for scoliosis classification using gait analysis, which circumvents these limitations. This study presents Scoliosis1K, the first large-scale dataset tailored for video-based scoliosis classification, encompassing over one thousand adolescents. Leveraging this dataset, we developed ScoNet, an initial model that encountered challenges in dealing with the complexities of real-world data. This led to the creation of ScoNet-MT, an enhanced model incorporating multi-task learning, which exhibits promising diagnostic accuracy for application purposes. Our findings demonstrate that gait can be a non-invasive biomarker for scoliosis, revolutionizing screening practices with deep learning and setting a precedent for non-invasive diagnostic methodologies. The dataset and code are publicly available at https://zhouzi180.github.io/Scoliosis1K/.

翻译：脊柱侧弯在诊断上面临重大挑战，尤其在青少年群体中，早期发现对有效治疗至关重要。传统的诊断与随访方法依赖于体格检查和放射成像，但由于需要临床专业知识且存在辐射暴露风险，限制了其在大规模早期筛查中的应用。为此，我们提出了一种基于步态分析的新型、无创的视频脊柱侧弯分类方法，以规避这些限制。本研究推出了首个专为视频脊柱侧弯分类设计的大规模数据集Scoliosis1K，涵盖超过一千名青少年。利用该数据集，我们开发了初始模型ScoNet，该模型在处理现实世界数据的复杂性时遇到了挑战。这促使我们进一步创建了增强模型ScoNet-MT，该模型结合了多任务学习，在应用层面展现出有前景的诊断准确性。我们的研究结果表明，步态可以作为一种无创的脊柱侧弯生物标志物，通过深度学习革新筛查实践，并为无创诊断方法树立了先例。数据集和代码已在https://zhouzi180.github.io/Scoliosis1K/公开提供。