Assessing Glaucoma Progression Using Machine Learning Trained on Longitudinal Visual Field and Clinical Data.

Journal: Ophthalmology

Published Date: Jul 1, 2021

Abstract

PURPOSE: Rule-based approaches to determining glaucoma progression from visual fields (VFs) alone are discordant and have tradeoffs. To detect better when glaucoma progression is occurring, we used a longitudinal data set of merged VF and clinical data to assess the performance of a convolutional long short-term memory (LSTM) neural network.

Authors

Avyuk Dixit

University of Michigan, Ann Arbor, Michigan.
Jithin Yohannan

Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland.
Michael V Boland

Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland.

Keywords

Aged Algorithms Clinical Competence Disease Progression Female Follow-Up Studies Glaucoma Glaucoma, Open-Angle Humans Intraocular Pressure Machine Learning Male Ophthalmologists Retrospective Studies ROC Curve Visual Fields

External Resources

View on PubMed Access via DOI PubMed (33359887)

Assessing Glaucoma Progression Using Machine Learning Trained on Longitudinal Visual Field and Clinical Data.

Abstract

Authors

Keywords

External Resources

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