Prediction of osteoporosis through deep learning with the help of MRI

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dc.contributor.authorMazhar Javed Awan
dc.date.accessioned2025-10-29T13:05:49Z
dc.date.available2025-10-29T13:05:49Z
dc.date.issued2019
dc.description.abstractOsteoporosis is a disease that weakens the bone strength causing fractures and life-threatening complications. It has been estimated to affect more than 200 million people worldwide. Improving diagnostic technology and assessment facilities have made it possible to detect the disease before it causes fractures. Early prediction of fracture risk enables preventive actions and lifestyle changes that can improve the patient’s quality of life and save costs to society. Diagnosis of osteoporosis is based on measuring the bone density using MRI. This technique produces an image representing bone density at the scanned site. However, bone density itself is only a moderate predictor of fracture risk, which creates demand for alternative prediction models. Machine learning, and especially convolutional neural networks, has been the leading image analysis approach in recent years. It has produced good results also in medical image analysis, including some orthopedically problems. This study seeks to discover if convolutional neural networks can predict osteoporotic fractures from spine MRI images. By experimenting with different network architectures, the study aims to gain an understanding of the most promising design directions of such prediction models. In this context, also some practical machine learning challenges such as low training speed and lack of transparency are addressed.
dc.identifier.urihttps://escholar.umt.edu.pk/handle/123456789/9743
dc.language.isoen
dc.publisherUMT, Lahore
dc.titlePrediction of osteoporosis through deep learning with the help of MRI
dc.typeThesis
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