Skeletal Fragility Detection using Deep Learning

In the field of diagnosing health there has been a shift, in focus from simply identifying bone fractures to a broader evaluation of skeletal fragility. Skeletal fragility involves not detecting fractures but also assessing the health and resilience of the skeletal system, which is crucial for preve...

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Bibliographic Details
Main Authors: Reddy, K. Rama Krishna, Reddy, V. Sravya, Sathvik, V. Sriram, Muthukumaran, N., Subhani, Shaik Mahaboob
Format: Conference Proceeding
Language:English
Subjects:
Bone Health
Bones
Data augmentation
Deep learning
DNN
Fractures
Image processing
Manuals
Noise
Surgery
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Summary:In the field of diagnosing health there has been a shift, in focus from simply identifying bone fractures to a broader evaluation of skeletal fragility. Skeletal fragility involves not detecting fractures but also assessing the health and resilience of the skeletal system, which is crucial for preventing future fractures. While an automated network (DNN) model has achieved impressive accuracy of 92.44% in distinguishing between healthy and fractured bones iti s important to consider additional factors when measuring skeletal fragility. These supplementary factors include bone density, composition, and comprehensive markers of bone health like vitamin D levels. To create a framework for assessing skeletal fragility and not just bone fractures it is necessary to expand the model's scope by incorporating these essential additional factors and indicators. This holistic approach has the potential to provide a nuanced perspective, on bone health and the likelihood of future fractures. Ultimately this can significantly improve care by facilitating measures.
ISSN:2767-7788
DOI:10.1109/ICICT60155.2024.10544871