Diagnostic accuracy of a deep learning model for pterygium detection in Barcelos, Brazilian Amazon
The deep learning model demonstrated high diagnostic performance in identifying pterygium in a remote Amazonian population. These preliminary findings support the potential use of artificial intelligence-based tools to facilitate early detection and screening in underserved regions, thereby enhancing access to ophthalmic care.
Authors
- Rubens Belfort (ORCID: https://orcid.org/0000-0002-8422-3898)
- Paula M. Marinho (ORCID: https://orcid.org/0000-0001-7904-4169)
- Diego Casagrande (ORCID: https://orcid.org/0000-0001-5158-5457)
- Nívea Nunes Ferraz (ORCID: https://orcid.org/0000-0001-7568-6199)
- Arthur Gustavo Fernandes (ORCID: https://orcid.org/0000-0002-7525-1838)
- Solange Rios Salomão (ORCID: https://orcid.org/0000-0002-3436-5599)
- Kevin Waquim Pessoa Carvalho (ORCID: https://orcid.org/0000-0002-3923-0503)
- Marcos Jacob Cohen (ORCID: https://orcid.org/0000-0002-0480-4036)
- Adriana Berezovsky (ORCID: https://orcid.org/0000-0001-6350-8221)
- Mauro César Gobira Guimarães Filho (ORCID: https://orcid.org/0000-0002-0483-8789)
- Marcos Jacob Cohen (ORCID: https://orcid.org/0000-0002-9071-6234)
- Ariane Luttecke-Anders (ORCID: https://orcid.org/0000-0001-8047-4625)
- Beatriz Araujo Stauber (ORCID: https://orcid.org/0009-0005-8605-4337)
Institutions
- Pontificia Universidad Católica de Valparaíso (CL)
- Universidade Federal de São Paulo (BR)
Publication Details
- Journal
- Arquivos Brasileiros de Oftalmologia
- Published
- 2025-01-01
- DOI
- https://doi.org/10.5935/0004-2749.2025-0053
- Primary Topic
- Corneal Surgery and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 0.00