A developmental stage-aware graph transformer framework for automated bone-age assessment
By leveraging a skeletal graph structure based on anatomical relationships and incorporating a developmental stage-aware processing mechanism, the DSGTF framework provides an accurate and efficient automated solution for clinical bone-age assessment. The proposed approach exhibits strong stability and consistency across different age groups and sexes, making it a reliable tool for real-world medical applications. This model effectively simulates the clinical assessment process by dynamically adjusting attention to different skeletal regions based on developmental stages, mirroring the decision-making process of radiologists.
Authors
- Hoekyung Jung
- C T Liu
- Lini Duan (ORCID: https://orcid.org/0000-0002-3667-2453)
- Kerang Cao
- Yufei Ma (ORCID: https://orcid.org/0000-0001-9421-869X)
- Lli Li
- Jiaming Du
Institutions
- Pai Chai University (KR)
- Dalian University of Technology (CN)
- Shenyang University of Chemical Technology (CN)
Publication Details
- Journal
- Quantitative Imaging in Medicine and Surgery
- Published
- 2025-12-31
- DOI
- https://doi.org/10.21037/qims-2025-998
- Primary Topic
- Forensic Anthropology and Bioarchaeology Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00