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Volume 20, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603
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Abstract

Osteoporosis causes harmful influences on both men and women of all races. Bone mass, also referred to as “bone density,” is frequently used to assess the health of bone. Humans frequently experience bone fractures as a result of trauma, accidents, metabolic bone diseases, and disorders of bone strength, which are typically led by changes in mineral composition and result in conditions like osteoporosis, osteoarthritis, osteopenia, Artificial intelligence holds a lot of promise for the healthcare system. Data collection and preprocessing seem to be more essential for analysis, so bone images from different modalities, such as X-ray, Computed Tomography (CT), and Magnetic Resonance Imaging (MRI), are taken into consideration that help to recognize, classify, and evaluate the patterns in clinical images. This research presents a comprehensive overview of the performance of various image processing techniques and deep learning approaches used to predict osteoporosis through image segmentation, classification, and fault detection. This survey outlined the proposed domain-based deep learning model for image classification in addition to the initial findings. The outcome identifies the flaws in the existing literature's methodology and lays the way for future work in the deep learning-based image analysis model.

© 2024 Inigo et al.
© 2024 The Author(s). Published by Bentham Science Publisher. This is an open access article published under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/legalcode
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2024-01-01
2025-01-31

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/content/journals/cmir/10.2174/1573405620666230608091911
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A Review on Imaging Techniques and Artificial Intelligence Models for Osteoporosis Prediction
Curr. Med. Imaging 20, E080623217779 (2024); https://doi.org/10.2174/1573405620666230608091911
/content/journals/cmir/10.2174/1573405620666230608091911
/content/journals/cmir/10.2174/1573405620666230608091911
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  • Article Type:     Review Article
Keyword(s): Artificial intelligence; Bone mineral density (BMD); Dataset quality; Deep learning (DL); Image processing; Medical image

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