Gait Patterns as Biomarkers: A Video-Based Approach for Classifying Scoliosis
This work addresses the problem of limited early screening for scoliosis in adolescents, offering a non-invasive alternative to traditional methods, though it appears incremental as it builds on existing deep learning approaches.
The study tackled the challenge of early scoliosis detection in adolescents by introducing a video-based, non-invasive method using gait analysis, resulting in the creation of the Scoliosis1K dataset and ScoNet-MT model with promising diagnostic accuracy.
Scoliosis presents 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, effectively circumventing these limitations. This study presents Scoliosis1K, the first large-scale dataset specifically designed for video-based scoliosis classification, encompassing over one thousand adolescents. Leveraging this dataset, we developed ScoNet, an initial model that faced challenges in handling the complexities of real-world data. This led to the development of ScoNet-MT, an enhanced model incorporating multi-task learning, which demonstrates promising diagnostic accuracy for practical applications. Our findings demonstrate that gait can serve as a non-invasive biomarker for scoliosis, revolutionizing screening practices through deep learning and setting a precedent for non-invasive diagnostic methodologies. The dataset and code are publicly available at https://zhouzi180.github.io/Scoliosis1K/.