Graph convolutional networks for 3D skeleton-based scoliosis screening using gait sequences

Abstract Adolescent idiopathic scoliosis is a significant health concern, ranked as the third most prevalent issue among adolescents after obesity and myopia. Traditional screening methods rely on the use of complex and expensive measuring instruments and expert physicians to interpret X-ray images. These methods can be both time-consuming and inaccessible for widespread screening efforts. To address these challenges, we propose a standardized protocol for the collection of scoliosis gait dataset. This protocol enables the systematic capture of relevant gait characteristics associated with scoliosis, leading to the creation of a comprehensive, annotated dataset tailored for research and diagnostic purposes. Leveraging this dataset, we developed an effective deep learning algorithm based on graph convolutional networks, which outperforms traditional CNN by effectively modeling the complex spatial and temporal dynamics of human gait and posture, leveraging skeletal structure as a graph for more accurate and robust scoliosis screening. We also explored various optimization strategies to enhance the model’s accuracy and efficiency, ensuring robust performance across diverse scenarios. Our innovative approach allows for the rapid and non-invasive recognition of scoliosis. This method is not only scalable but also eliminates the need for specialized equipment or extensive medical expertise, making it ideal for large-scale screening initiatives. By improving the accessibility and efficiency of scoliosis detection, our approach has the potential to facilitate early intervention.