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@article{187024,
        author = {A. Abhinav R B and B. Prof. K Satyanarayan Reddy},
        title = {A Comprehensive study on Advanced Machine and Deep Learning Frameworks for Automated Detection and Classification of Postural Kyphosis Using Sagittal Radiographs},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {3884-3892},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=187024},
        abstract = {Postural deviations, particularly postural kyphosis, have become increasingly prevalent in today's sedentary lifestyle, leading to health challenges beyond aesthetics, such as chronic pain and joint degeneration. Despite growing awareness, current diagnostic and intervention methods often lack real-world applicability and scalability. This survey explores the need for advanced, automated detection and classification of postural kyphosis using sagittal radiographs and machine learning-based approaches. We identify significant research gaps, including limited datasets, lack of longitudinal studies, and real-time feedback mechanisms. Our objective is to develop a robust machine learning framework capable of early detection and classification of thoracic spinal deviations, supported by standardized datasets, automated feature extraction, and integration with real-world clinical settings. The proposed work aims to assist orthopedists and radiologists, improve diagnostic accuracy, and enhance intervention outcomes through scalable, AI-driven posture analysis.},
        keywords = {Postural Deviations, Postural Kyphosis, Chronic Pain, Thoracic Spinal Deviations.},
        month = {November},
        }