Using 3D Convolutional Neural Network and Corvis ST Corneal Dynamic Video for Detecting Forme Fruste Keratoconus
Combining 3D CNN with Corvis ST corneal dynamic videos provides a new method for distinguishing between FFKC and normal corneas. This could offer valuable clinical insights and recommendations for detecting FFKC. Nevertheless, the generalizability of the model is still a concern, and external validation is required prior to its clinical implementation. <b>[<i>J Refract Surg</i>. 2025;41(4):e356-e364.]</b>.
Authors
- Bei Du (ORCID: https://orcid.org/0000-0002-0947-1541)
- Nan Gao (ORCID: https://orcid.org/0000-0003-0259-3316)
- Jiamei Hu
- Ruihua Wei (ORCID: https://orcid.org/0000-0002-9708-0355)
- Ziwen Sun (ORCID: https://orcid.org/0009-0001-1714-8939)
- Chea‐su Kee (ORCID: https://orcid.org/0000-0002-5177-129X)
- Hua Rong (ORCID: https://orcid.org/0000-0002-4189-0893)
- Yanling Wang (ORCID: https://orcid.org/0000-0002-3487-0122)
- Guihua Liu (ORCID: https://orcid.org/0000-0002-9669-3774)
Institutions
- Nankai University (CN)
Publication Details
- Journal
- Journal of Refractive Surgery
- Published
- 2025-04-01
- DOI
- https://doi.org/10.3928/1081597x-20250226-01
- Citations
- 2
- Primary Topic
- Corneal surgery and disorders
- Type
- article
- Field-Weighted Citation Impact
- 2.72