Deep-learning-based prediction of glaucoma conversion in normotensive glaucoma suspects.

Journal: The British journal of ophthalmology

PMID: 37918891

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.

Authors

Ahnul Ha

Department of Ophthalmology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea.
Sukkyu Sun
Young Kook Kim

Department of Ophthalmology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea.
Jin Wook Jeoung

Department of Ophthalmology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea.
Hee Chan Kim

Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Korea.
Ki Ho Park

Department of Ophthalmology.

Keywords

Aged Algorithms Blood Pressure Deep Learning Disease Progression Female Follow-Up Studies Humans Intraocular Pressure Low Tension Glaucoma Male Middle Aged Nerve Fibers Ocular Hypertension Optic Disk Retinal Ganglion Cells Retrospective Studies ROC Curve Tonometry, Ocular Visual Fields

External Resources

View on PubMed Access via DOI PubMed (37918891)

Deep-learning-based prediction of glaucoma conversion in normotensive glaucoma suspects.

Abstract

Authors

Keywords

External Resources

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