A Systematic Review and Meta-Analysis on Diagnosis of Osteoporosis Risk in Real Time Using Machine Learning and Deep Learning Techniques

What the paper says

Osteoporosis is a rare bone disease leading to loss of bone tissue and mass. Osteoporosis disease makes the bones weak by reducing the bone strength and leads to fractures. Osteoporosis commonly affects middle-aged women. Osteoporosis disease is diagnosed using Dual Energy X-ray Absorptiometry (DEXA) as it helps in defining the bone mineral density; however, the process is expensive. DEXA has set its margin as the golden standard in osteoporosis disease diagnosis. The latest research performed in osteoporosis disease to verify the effectualness and analyse different diagnosis factors through clinical validation is limited. Therefore, this work provides a survey on the diagnosis of osteoporosis risk using deep learning techniques and also focuses on exploring the different applications-oriented technologies defined in the medical field for eliminating osteoporosis risk. This survey investigates how deep learning methods enhance the early detection and prediction of osteoporosis, a condition characterised by low bone density and increased fracture risk. Deep learning techniques have shown promising results in various medical applications, including image analysis and disease diagnosis. By leveraging these techniques, the survey helps in developing a model that can analyse bone density scans and other relevant data in real time to assess the risk of osteoporosis accurately. The survey delves into the details of deep learner training on large datasets of bone scans, patient information and other medical data to enable the automated detection of osteoporosis risk factors. Furthermore, it explores the challenges and opportunities associated with implementing deep learning techniques in osteoporosis risk assessment. In addition, it assesses the potential benefits of incorporating additional data modalities, such as genetic information or lifestyle factors, into the deep learning model to enhance the overall predictive power for osteoporosis risk assessment. By exploring the fusion of diverse data sources through deep learning frameworks, the survey aims to uncover synergies that lead to more comprehensive and personalised risk evaluations. Finally, this research endeavours to pave the way for more effective and personalised healthcare interventions in the field of bone health. The results show that the CNN achieved an accuracy of 99.23% and the MCNN attained 99% accuracy leading to faster and more accurate diagnosis and personalised treatment plans in osteoporosis diagnosis, which permits for quicker mediation when required and reduces the possible difficulties.