Synthetic Data-Enhanced Classification of Prevalent Osteoporotic Fractures Using Dual-Energy X-Ray Absorptiometry-Based Geometric and Material Parameters
The proposed methodology demonstrates that current bone health can be assessed through post-processed results from 2D-DXA analysis. Moreover, it was also shown that synthetic data can help stabilize uneven databases by balancing majority and minority classes, thereby significantly improving classification performance.
Authors
- Yoon‐Sok Chung (ORCID: https://orcid.org/0000-0003-0179-4386)
- Luca Quagliato (ORCID: https://orcid.org/0000-0002-5379-8306)
- Taeyong Lee
- Jin Yong Hong (ORCID: https://orcid.org/0000-0002-2476-3737)
- Jiin Seo
Institutions
- Ewha Womans University (KR)
- Ajou University (KR)
Publication Details
- Journal
- Endocrinology and Metabolism
- Published
- 2025-05-14
- DOI
- https://doi.org/10.3803/enm.2024.2211
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36
Funders
- Korea Medical Device Development Fund
- Ewha Womans University
- Ministry of Trade, Industry and Energy
- Ministry of Food and Drug Safety
- Ministry of Science and ICT, South Korea