Rrol of Deep Learning Algorithm in Assessment and Management of Scoliosis

Although attention in AI related to healthcare is expanding, there hast been much progress in translating or implementing these technologies for clinical usage. Therefore, as we conduct our research, we will open a new field of study for the integration of artificial intelligence (AI) in medical assessment tools and physical therapy field. This will save physiotherapists time and effort for developing Proper evaluations and conduction of treatment plans, benefit patients and the country overall by lowering the financial burden associated with making accurate evaluations and management for these cases, and provide clear, objective evaluations for the majority of spinal scoliosis deformities as well as appropriate personalization for treatment plans.

Martial and Methods

1. Experiment martials This section presents the findings of the research, detailing the methodologies employed and the outcomes obtained. The analysis begins with an overview of the dataset used, followed by the preprocessing steps undertaken to ensure data quality and suitability for model training. then delve into the various approaches and models selected for this study, including both custom Convolutional Neural Networks (CNNs) and pre-trained models utilized through transfer learning. Each model is assessed based on its performance, and discuss the experimental setup and evaluation metrics used to measure effectiveness. Finally, presenting the experimental results, providing a comprehensive analysis of model accuracy, precision, recall, and other relevant metrics.

2.1 Dataset collection The data of the subjects were divided for 2 groups. Group (A) normal spine total X-ray consisted of 664 image and group (B) scoliotic patients x-ray consisted of 4307 images. All spinal X-ray involved in this study were retrospectively compiled from manifold sources, including BUU Datasets , Kaggle, Mendeley Data, Huggingface, Dropbox , and Roboflow, ensuring a diverse and comprehensive collection of scoliosis-related images. Patient with scoliosis were (1) diagnosed with scoliosis for different etiology, (2) Clear X- Ray for spine for all spinal curvatures including; cervical, thoracic and lumber. (3) C shaped and double C shape scoliosis. (4) mild, moderate and sever scoliotic degrees. (5) Adolescents with mean age 17(6) Both gender(male and female). 2. Purposed methodology 2.1 Data Augmentation To address the issue of limited data, we employed data augmentation to artificially expand our dataset. This technique involves generating new training samples by applying various transformations to the existing images, such as flipping, scaling, and rotating. These augmentations not only increase the dataset size but also play a crucial role in enhancing the model ability to generalize by exposing it to a wider variety of image variations. By simulating different viewing conditions and distortions, data augmentation helps the model become more robust to changes in image orientation, scale, and other variations that it may encounter in real-world scenarios. This process is particularly important in medical image analysis, where acquiring large, diverse datasets can be challenging. Data augmentation ensures that the model does not overfit to the limited original dataset and instead learns to recognize the underlying patterns that are indicative of scoliosis, ultimately improving its performance and reliability.

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