Three-Dimensional Quantification of Macular OCT Alterations Improves the Diagnostic Performance of Artificial Intelligence Models
The presented approach can streamline clinical workflows by reducing the time and effort required for manual annotations, ultimately supporting more efficient and accurate monitoring of AMD progression and treatment response. We provide open-source access to the model weights, annotation instructions and sample data.
Authors
- Fabian Hörst (ORCID: https://orcid.org/0000-0002-8906-7644)
- Daniel Pauleikhoff (ORCID: https://orcid.org/0000-0003-3691-291X)
- M. J. Lever (ORCID: https://orcid.org/0000-0002-3244-6782)
- Nikolaos Emmanouel Bechrakis (ORCID: https://orcid.org/0000-0002-0180-1112)
- Constantin Seibold (ORCID: https://orcid.org/0000-0001-6042-8437)
- Laurenz Pauleikhoff (ORCID: https://orcid.org/0000-0001-7671-8391)
- Lukas Heine (ORCID: https://orcid.org/0009-0004-3405-0556)
- Anna Vahldiek
- Benja Vahldiek
Institutions
- RWI – Leibniz Institute for Economic Research (DE)
- Universität Hamburg (DE)
- Akademie Mode & Design (DE)
- University Medical Center Hamburg-Eppendorf (DE)
- Essen University Hospital (DE)
Publication Details
- Journal
- Translational Vision Science & Technology
- Published
- 2025-07-16
- DOI
- https://doi.org/10.1167/tvst.14.7.8
- Citations
- 1
- Primary Topic
- Retinal Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 1.36
Funders
- Universität Duisburg-Essen
- Heidelberg Engineering
- European Regional Development Fund