Advanced Camera-Based Scoliosis Screening via Deep Learning Detection and Fusion of Trunk, Limb, and Skeleton Features.

Scoliosis significantly impacts quality of life, highlighting the need for effective early scoliosis screening (SS) and intervention. However, current SS methods often involve physical contact, undressing, or radiation exposure. This study introduces an innovative, non-invasive SS approach utilizing a monocular RGB camera that eliminates the need for undressing, sensor attachment, and radiation exposure. We introduce a novel approach that employs Parameterized Human 3D Reconstruction (PH3DR) to reconstruct 3D human models, thereby effectively eliminating clothing obstructions, seamlessly integrated with an ISANet segmentation network, which has been enhanced by Multi-Scale Fusion Attention (MSFA) module we proposed for facilitating the segmentation of distinct human trunk and limb features (HTLF), capturing body surface asymmetries related to scoliosis. Additionally, we propose a Swin Transformer-enhanced CMU-Pose to extract human skeleton features (HSF), identifying skeletal asymmetries crucial for SS. Finally, we develop a fusion model that integrates the HTLF and HSF, combining surface morphology and skeletal features to improve the precision of SS. The experiments demonstrated that PH3DR and MSFA significantly improved the segmentation and extraction of HTLF, whereas ST-based CMU-Pose substantially enhanced the extraction of HSF. Our final model achieved a comparable F1 (0.895 ±0.014) to the best-performing baseline model, with only 0.79% of the parameters and 1.64% of the FLOPs, achieving 36 FPS-significantly higher than the best-performing baseline model (10 FPS). Moreover, our model outperformed two spine surgeons, one less experienced and the other moderately experienced. With its patient-friendly, privacy-preserving, and easily deployable solution, this approach is particularly well-suited for early SS and routine monitoring.