Improving Osteoporosis Detection Using Deep Learning: A Survey of Techniques, Shortcomings and Implementation Aspects
DOI:
https://doi.org/10.63282/3050-922X.ICAILLMBA-111Keywords:
CNN, Bones Density, Fracture Risk, Osteoporosis Screening, Medical Imaging, Transfer LearningAbstract
Osteoporosis is a severe illness that may be gradually peaked by the reduction of the bone density and increased vulnerability to fracture. Osteoporosis should be identified early to facilitate the disease treatment. In order to discuss and assess deep learning-based methods of screening osteoporosis, point out their mechanisms, constraints and practicability in the context of actual clinical practice. In the recent times, deep learning is a new method that efficiently and accurately offers diagnostic procedures through medical imaging and analysis. This paper will review the general uses of the deep learning technique in identifying osteoporosis and how it can be effectively used in terms of efficiency, limitations, and implementation. The categories of models used in the identification of bone mineral density (BMD) and/or fracture risk include convolutional neural networks (CNN), transfers earning-based, and hybrid-method-based models. Predictive accuracy and robustness of these models are high, although issues with respect to data availability, model generalizability and connecting them to clinical use remain challengeable. The future of such technologies as deep learning in the detection of osteoporosis can contribute to the creation of a clear connection between the technological improvement and the clinical management of osteoporosis, and may translate into positive prognosis with the early diagnosis.
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