Investigating the capability of deep learning models to predict age and biological sex from anterior segment ophthalmic imaging: a multi-centre retrospective study
Our study demonstrates that deep learning models can extract age and gender information from anterior segment images. These findings support the concept that the anterior segment, like the retina, encodes important biological information. Future research should explore whether these models can predict specific systemic conditions.
Authors
- Pearse A. Keane (ORCID: https://orcid.org/0000-0002-9239-745X)
- Daniel M. Gore (ORCID: https://orcid.org/0000-0001-5851-8150)
- Bruce Allan (ORCID: https://orcid.org/0000-0002-8503-4482)
- Ajmal Khan (ORCID: https://orcid.org/0000-0002-4727-3554)
- Shafi Balal (ORCID: https://orcid.org/0000-0001-7683-2741)
- Marcello Leucci (ORCID: https://orcid.org/0000-0002-3998-6415)
- Nikolas Pontikos (ORCID: https://orcid.org/0000-0003-1782-4711)
- Laurence Cox (ORCID: https://orcid.org/0000-0001-6658-8503)
- Lynn Kandakji (ORCID: https://orcid.org/0000-0003-2947-6232)
Institutions
- Moorfields Eye Hospital NHS Foundation Trust (GB)
- Moorfields Eye Hospital (GB)
- University College London (GB)
- Anglia Ruskin University (GB)
Publication Details
- Journal
- BMJ Open
- Published
- 2025-10-01
- DOI
- https://doi.org/10.1136/bmjopen-2025-107196
- Primary Topic
- Retinal Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Institute for Health and Care Research
- University College London