Machine learning classifiers-based prediction of normal-tension glaucoma progression in young myopic patients.

Journal: Japanese journal of ophthalmology

Published Date: Jan 1, 2020

Abstract

PURPOSE: To assess the performance of machine learning classifiers for prediction of progression of normal-tension glaucoma (NTG) in young myopic patients.

Authors

Jinho Lee

Department of Ophthalmology.
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.
Ahnul Ha

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

Department of Ophthalmology, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea.
Ki Ho Park

Department of Ophthalmology.

Keywords

Adult Area Under Curve Cross-Sectional Studies Diagnosis, Computer-Assisted Disease Progression Female Gonioscopy Humans Intraocular Pressure Low Tension Glaucoma Machine Learning Male Myopia Nerve Fibers Retinal Ganglion Cells ROC Curve Slit Lamp Microscopy Tomography, Optical Coherence Visual Field Tests Visual Fields Young Adult

External Resources

View on PubMed Access via DOI PubMed (31848786)

Machine learning classifiers-based prediction of normal-tension glaucoma progression in young myopic patients.

Abstract

Authors

Keywords

External Resources

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