Characterization of dynamic features in the walking videos of patients with adolescent idiopathic scoliosis based on moving entropy.

Abstract

Adolescent idiopathic scoliosis (AIS), which typically occurs in patients between the ages of 10 and 18, can be caused by a variety of reasons, and no definitive cause has been found. Early diagnosis of AIS or timely recognition of progression is crucial for the prevention of spinal deformity and the reduction of the risk of surgery or postponement. However, it remains a significant challenge. The purpose of this study is to develop an easy-to-use, non-invasive, and portable method for early diagnosis of AIS. A new framework of moving entropy-based computer vision method is presented, which can determine the severity of AIS by analyzing patients' walking videos. First, Alphapose system and direct linear transformation method are employed to estimate 3D keypoint coordinates. Then, the joint angle-based and joint distance-based dynamic network are constructed. Based on these works, the new measures called moving angle entropy and moving edge-weighted graph entropy are proposed and fused using canonical correlation analysis. Finally, the power spectral exponents of entropy sequences are calculated and used in recognizing the severity of AIS. A comparison with healthy subjects and statistical analysis for entropy values can provide effective information for quantifying AIS. The recognized results of our proposed method were also comparable with the clinical diagnosis of Cobb angle from imaging by a certified clinician.