Abstract
Abstract: Adolescent idiopathic scoliosis (AIS) poses a significant health concern, affecting the spinal curvature of adolescents during their growth period. Early diagnosis of AIS is critical for effective intervention and management. However, conventional diagnostic methods often involve repetitive exposure to X-rays, which can lead to radiation-related side effects. This research paper explores the significance of early diagnosis in AIS and emphasizes the utilization of non-invasive techniques, particularly video raster stereography (VRS), combined with deep ensemble neural networks (DNNs) to enhance diagnostic accuracy while minimizing the risks associated with radiation exposure. We delve into the potential of DNNs in analyzing VRS data and compare its efficacy with other machine learning alternatives
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