Scoliosis detection method based on fringe projection profilometry and deep learning

In this paper, a fringe projection profilometry (FPP) system for three-dimensional (3D) imaging of the human back is proposed to assess scoliosis, which has developed into one of the top four health concerns affecting adolescents. The FPP system includes a binocular stereo vision system and an infrared projector, with a Field Programmable Gate Array (FPGA) based synchronization control system enabling a 240Hz acquisition frame rate. This high frame rate mitigates the impact of involuntary body movement on 3D image quality. Various features can be extracted from the generated 3D model of the back for quantitative postural assessment. Specifically, a deep learning approach is employed to analyze the extracted trunk rotation angle curve for scoliosis detection. Experimental results demonstrate a correlation between this method and X-ray detection, offering a promising exploratory approach to reduce reliance on X-ray imaging in scoliosis monitoring.