Utilizing Deep Learning for Osteoporosis Diagnosis through Knee X-Ray Analysis

DOI

10.2991/978-94-6463-512-6_58How to use a DOI?

Keywords

Osteoporosis; Deep Learning; Knee X-Rays; VGG

Abstract

A common progressive disease called osteoporosis is defined by a steady decline of bone density that weakens bones and raises the risk of fractures. This condition significantly impacts the quality of life of affected individuals, particularly among the elderly. The goal of this study is to identify osteoporosis by analyzing knee x-rays with powerful deep learning models. By leveraging artificial intelligence technology, this approach aims to enhance diagnostic accuracy and efficiency, providing a more convenient and non-invasive method for early detection and treatment of osteoporosis. Ultimately, this can help lower the risk of fractures and enhance the overall health outcomes for patients. Specifically, this paper employed the Visual Geometry Group (VGG) 19 model, known for its ability to extract detailed features from 2D images. Using datasets from Kaggle and Mendeley, the model achieved an accuracy of 89% after 17 epochs of training, demonstrating its effectiveness in identifying osteoporosis traits in knee x-rays. This approach provides an alternative to the traditional hip x-ray diagnosis, potentially easing the diagnostic process for patients. Furthermore, this method could help in the early detection and intervention of osteoporosis, thereby reducing fracture risks. The outcomes of the study highlight the possibilities of deep learning models in improving diagnostic accuracy and patient outcomes in osteoporosis management.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Mengyuan Shen
PY  - 2024
DA  - 2024/09/23
TI  - Utilizing Deep Learning for Osteoporosis Diagnosis through Knee X-Ray Analysis
BT  - Proceedings of the 2024 International Conference on Artificial Intelligence and Communication (ICAIC 2024)
PB  - Atlantis Press
SP  - 553
EP  - 560
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-512-6_58
DO  - 10.2991/978-94-6463-512-6_58
ID  - Shen2024
ER  -