Classification of fundus autofluorescence images based on macular function in retinitis pigmentosa using convolutional neural networks
Using FAF images may enable the accurate assessment of central vision function in RP. FAF images may include more parameters than color fundus images that can evaluate central visual function.
Authors
- Koji M. Nishiguchi (ORCID: https://orcid.org/0000-0001-5987-4421)
- Shinji Ueno (ORCID: https://orcid.org/0000-0001-5716-3626)
- Kensaku Mori (ORCID: https://orcid.org/0000-0002-0100-4797)
- Junya Ota (ORCID: https://orcid.org/0009-0007-7124-7595)
- Taiga Inooka (ORCID: https://orcid.org/0000-0003-1912-5316)
- Oda Masahiro
- Taro Kominami (ORCID: https://orcid.org/0009-0000-0612-2576)
Institutions
- Hirosaki University (JP)
- National Institute of Informatics (JP)
- Nagoya University Hospital (JP)
- Nagoya Medical Center (JP)
- Nagoya University (JP)
Publication Details
- Journal
- Japanese Journal of Ophthalmology
- Published
- 2025-02-12
- DOI
- https://doi.org/10.1007/s10384-025-01163-w
- Citations
- 5
- Primary Topic
- Retinal Development and Disorders
- Type
- article
- Field-Weighted Citation Impact
- 4.03
Funders
- Nagoya University
- Japan Society for the Promotion of Science