@inproceedings{67d583fb311e441cb73b6a1e036a7384,
title = "Visual Field Prediction for Fundus Image with Generative AI",
abstract = "Accurate diagnosis of glaucoma is crucial due to its high risk of blindness, but the long examination time often undermines the reliability of results by examinee's subjective factors. We propose a method to reduce examination time by generating static perimetry results with Conventional Fundus Images (CFIs) utilizing the CFI2GM technique, which leverages multimodal data. Based on data from 3,306 glaucoma patients at Samsung Medical Center in Seoul, we conducted ophthalmic image translation utilizing the Pix2Pix model. Our method, combining cGAN, L1, and SSIM loss, achieved MSE 57.9886 and PSNR 30.6057 dB. Furthermore, we received positive feedback from ophthalmologist regarding the high practical applicability of the images generated by our method. This indicates that CFI2GM can enhance the reliability of glaucoma examination results as well as reduce testing time.",
keywords = "fundus image, generative AI, glaucoma, visual field",
author = "Honggu Kang and Seonghyeon Ko and Juchan Kim and Le, \{Duc Tai\} and Junghyun Bum and Jongchul Han and Hyunseung Choo",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 18th International Conference on Ubiquitous Information Management and Communication, IMCOM 2024 ; Conference date: 03-01-2024 Through 05-01-2024",
year = "2024",
doi = "10.1109/IMCOM60618.2024.10418344",
language = "English",
series = "Proceedings of the 2024 18th International Conference on Ubiquitous Information Management and Communication, IMCOM 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Sukhan Lee and Hyunseung Choo and Roslan Ismail",
booktitle = "Proceedings of the 2024 18th International Conference on Ubiquitous Information Management and Communication, IMCOM 2024",
}