Self-supervised learning and hybrid deep models for predicting the progression of Fuchs’ endothelial corneal dystrophy after cataract surgery
The proposed methodology provides a robust and interpretable decision-support tool for FECD management and cataract surgery planning.
Authors
- David Mingo‐Botín (ORCID: https://orcid.org/0000-0001-9202-9780)
- Iván González-Díaz (ORCID: https://orcid.org/0000-0003-4644-8479)
- Pedro Arriola‐Villalobos (ORCID: https://orcid.org/0000-0002-3573-0842)
- Jaime Etxebarría (ORCID: https://orcid.org/0000-0002-7650-9132)
- Francisco Arnalich‐Montiel (ORCID: https://orcid.org/0000-0002-1229-2018)
- Ana Boto de los Bueis (ORCID: https://orcid.org/0000-0002-9683-2025)
- Nerea Sáenz-Madrazo (ORCID: https://orcid.org/0000-0001-8912-1287)
- Fernando Díaz-de-María (ORCID: https://orcid.org/0000-0002-6437-914X)
- Martha Stokking (ORCID: https://orcid.org/0000-0001-9661-8515)
- Paola León-Tarife
Institutions
- Hospital Universitario La Paz (ES)
- Hospital General Universitario Gregorio Marañón (ES)
- Hospital Clínico San Carlos (ES)
- BioCruces Health research Institute (ES)
- Hospital Universitario Ramón y Cajal (ES)
- Universidad Carlos III de Madrid (ES)
Publication Details
- Journal
- Computer Methods and Programs in Biomedicine
- Published
- 2025-10-13
- DOI
- https://doi.org/10.1016/j.cmpb.2025.109100
- Primary Topic
- Retinal Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.00