Toward the Envision Portal: Designing a FAIR and AI-Ready Framework for Ophthalmic Imaging Sharing and Discovery
Ophthalmic imaging datasets are a valuable resource for advancing AI/ML models for disease detection, but their reuse is hindered by fragmented sharing and inconsistent structure. In this poster, we introduce the Envision Portal, an open-source platform intended to support the sharing and discovery of FAIR (Findable, Accessible, Interoperable, Reusable) and AI-ready ophthalmic imaging datasets.
Authors
- Sanjay Soundarajan (ORCID: https://orcid.org/0000-0003-2829-8032)
- Bhavesh Patel (ORCID: https://orcid.org/0000-0002-0307-262X)
- Xuebin Dong
- Nahid Zenali
- Dorian Portillo (ORCID: https://orcid.org/0000-0002-4306-4464)
- Cecilia Lee (ORCID: https://orcid.org/0000-0003-1994-7213)
Institutions
- Washington University in St. Louis (US)
- California Medical Innovations Institute (US)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2026-05-27
- DOI
- https://doi.org/10.5281/zenodo.20415742
- Primary Topic
- Retinal Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.00