digiBONE: an automated tool for segmental Greulich-Pyle bone age assessment of Indian children and adolescents
The full-hand model captures how a child's overall maturity aligns with population norms, while, the segmental models explain how individuals of the same GP age differ biologically, improving interpretability and personalisation. digiBONE demonstrates that integrating biologically relevant segmental information into deep learning pipelines offers a scalable, automated bone age assessment solution applicable to Indian populations.
Authors
- Chirantap Oza (ORCID: https://orcid.org/0000-0001-9832-7033)
- Tim Aeppli (ORCID: https://orcid.org/0000-0002-5499-4763)
- Khadilkar
- Lars Sävendahl (ORCID: https://orcid.org/0000-0003-1067-4976)
- Shruti Mondkar (ORCID: https://orcid.org/0000-0002-4754-6209)
- Shreya Chakladar
- Anuradha Khadilkar
- Pranay Goel
Institutions
- Karolinska University Hospital (SE)
- Indian Institute of Science Education and Research Pune (IN)
- Jehangir Hospital (IN)
- Karolinska Institutet (SE)
- Savitribai Phule Pune University (IN)
Publication Details
- Journal
- Frontiers in Endocrinology
- Published
- 2026-03-11
- DOI
- https://doi.org/10.3389/fendo.2026.1757571
- Primary Topic
- Forensic Anthropology and Bioarchaeology Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Indian Institute of Science
- Indian Institute of Science Education and Research Pune
- Indian Institute of Science Education and Research Mohali