Deep-learning-based prediction of glaucoma conversion in normotensive glaucoma suspects
- Published on 07/10/2024
- Reading time: 5 min.
Ahnul Ha1, Sukkyu Sun2, Young Kook Kim3,4, Jin Wook Jeoung3,4, Hee Chan Kim5, Ki Ho Park3,4
1
Department of Ophthalmology, Jeju National University, Jeju, Korea (the Republic of)
2
Department of AI Software Convergence, Dongguk University, Seoul, Korea (the Republic of)
3
Department of Ophthalmology, Seoul National University Hospital, Seoul, South Korea
4
Department of Ophthalmology, Seoul National University College of Medicine, Seoul, Korea (the Republic of)
5
Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Korea (the Republic of)
Correspondence to Professor Ki Ho Park, Department of Ophthalmology, Seoul National University Hospital, Jongno-gu, 03080, Korea (the Republic of); kihopark@snu.ac.kr; Professor Hee Chan Kim; hckim@snu.ac.kr
Abstract
Background/aims To assess the performance of deep-learning (DL) models for prediction of conversion to normal-tension glaucoma (NTG) in normotensive glaucoma suspect (GS) patients.
Methods Datasets of 12 458 GS eyes were reviewed. Two hundred and ten eyes (105 eyes showing NTG conversion and 105 without conversion), followed up for a minimum of 7 years during which intraocular pressure (IOP) was lower than 21 mm Hg, were included. The features...
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