Proceedings of the Global Conference on Sustainable Energy Systems, Smart Electronics and Intelligent Computing (GCSESEIC 2025)

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A Multi-Modal Approach for Automated Osteoporosis Diagnosis Using Knee X-ray Imaging Data

Authors
Geetha Ramamoorthy1, Arulselvi Subramanian1, Tamilselvi Rajendran2, *, Parisa BehamMohamed2, Nandhineeswari2
1Dept. of Electronics and Communication Engineering, Bharath Institute of Higher Education and Research, Chennai, India
2Department of Electronics and Communication Engineering, Sethu Institute of Technology, Viruthunagar, India
*Corresponding author. Email: tamilselvi@sethu.ac.in
Corresponding Author
Tamilselvi Rajendran
Available Online 24 April 2026.
DOI
10.2991/978-94-6239-654-8_40How to use a DOI?
Keywords
3D bone reconstruction; fracture risk prediction; knee X-ray; multi-task learning; osteoporosis classification
Abstract

Osteoporosis is a degenerative skeletal condition associated with decreased bone mineral density (BMD) that increases the risk of osteoporotic fractures in older individuals. Nevertheless, traditional BMD measurement with DXA does not consistently provide operator-independent repeatability and is only somewhat sensitive to microarchitectural alterations. We propose here a novel diagnostic approach driven by deep learning, which will assist in carrying out automatic osteoporosis screening on knee X-ray images. The system combines 2D X-ray image analysis and 3D bone density reconstruction with fracture risk calculation. By integrating patient metadata, such as age, body mass index, and menopausal status, the proposed model allows for customized stratification of fracture risk according to FRAX thresholds. This tool enables, on one hand, early pharmacological interventions based on bisphosphonates or ho⁠rmone⁠ therapy, with a classi⁠fication accur⁠acy of 9⁠4.8% and an AUC of 0.92. On the other hand, this tool enhances clinical decision-making and ensures timely treatment planning⁠, Future work will consider the validation on multi-center datasets and its inclusion in hospital workflows.

Copyright
© 2026 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.

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Volume Title
Proceedings of the Global Conference on Sustainable Energy Systems, Smart Electronics and Intelligent Computing (GCSESEIC 2025)
Series
Advances in Engineering Research
Publication Date
24 April 2026
ISBN
978-94-6239-654-8
ISSN
2352-5401
DOI
10.2991/978-94-6239-654-8_40How to use a DOI?
Copyright
© 2026 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.

Cite this article

risenwbib
TY  - CONF
AU  - Geetha Ramamoorthy
AU  - Arulselvi Subramanian
AU  - Tamilselvi Rajendran
AU  - Parisa BehamMohamed
AU  - Nandhineeswari
PY  - 2026
DA  - 2026/04/24
TI  - A Multi-Modal Approach for Automated Osteoporosis Diagnosis Using Knee X-ray Imaging Data
BT  - Proceedings of the Global Conference on Sustainable Energy Systems, Smart Electronics and Intelligent Computing (GCSESEIC 2025)
PB  - Atlantis Press
SP  - 492
EP  - 507
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6239-654-8_40
DO  - 10.2991/978-94-6239-654-8_40
ID  - Ramamoorthy2026
ER  -