Enhancing Scoliosis Diagnosis via Deep Learning

Scoliosis is a medical disorder characterised by an abnormal lateral curvature or bending of the spine that, if left untreated, can cause serious health problems. This work describes a deep learning-based strategy for automating the detection and assessment of scoliosis severity using medical X-ray images. The technology uses a U-Net design to partition spinal images, then identifies important spots to precisely determine vertebral positions. The Cobb angle, an important measure of scoliosis severity, is then calculated automatically using the segmented spine. The model was trained and evaluated using a dataset of X-rays from scoliosis and normal spines, and it demonstrated great accuracy in both Cobb angle estimation and scoliosis classification. Standard criteria like as accuracy and training loss were used to evaluate performance. It demonstrates the effectiveness of the suggested methodology in delivering a trustworthy and efficient tool for automated scoliosis screening and severity grading. This development has the potential to help clinicians improve diagnostic and treatment planning processes.