Journal article
Predicting Visual Field Worsening with Longitudinal Optical Coherence Tomography Data Using a Gated Transformer Network (OPHTHA-D-22-01964)s
Ophthalmology (Rochester, Minn.), Vol.130(8), pp.854-862
08/2023
DOI: 10.1016/j.ophtha.2023.03.019
PMCID: PMC10524436
PMID: 37003520
Abstract
To identify visual field (VF) worsening from longitudinal optical coherence tomography (OCT) data using a Gated Transformer Network (GTN), and to examine how GTN performance varies for different definitions of visual field worsening and different stages of glaucoma severity at baseline.
A total of 4,211 eyes (2,666 patients) followed at the Johns Hopkins Wilmer Eye Institute with at least five reliable VFs and one reliable OCT within one year of each reliable VF.
Retrospective longitudinal cohort study.
For each eye, we used three trend-based methods (MD, VFI, and PLR slope) and three event-based methods (GPA, CIGTS, and AGIS) to define VF worsening. Additionally, we created an algorithm, called M6, that classifies an eye as worsening if 4 or more of the 6 aforementioned methods classifies the eye as worsening. Using these 7 reference standards for VF worsening, we trained 7 GTNs that accepts a series of at least 5 as input OCT scans and provides as output a probability of VF worsening. GTN performance was compared to non-deep learning models with the same serial OCT input from previous studies - linear mixed effects models (MEM) and naïve Bayes classifiers (NBC) - using the same training sets and reference standards as for the GTN. The effect of glaucoma severity at baseline VF on GTN performance was also investigated through stratified analysis.
Area under the receiver operating characteristic curve (AUC) and F1 score.
The M6 algorithm labeled 63 eyes (1.50%) as worsening. The GTN achieved an AUC (95% CI) of 0.97 (0.88, 1.00) when trained with M6. GTNs trained and optimized with the other 6 reference standards had AUC ranging from 0.78 (MD slope) to 0.89 (AGIS). The 7 GTNs outperformed all 7 MEMs and all 7 NBCs accordingly. GTN performance was worse for eyes with more severe glaucoma at baseline.
GTN models trained with OCT data may be used to identify VF worsening. After further validation, implementing such models in clinical practice may allow us to track functional worsening of glaucoma with less onerous structural testing.
Details
- Title: Subtitle
- Predicting Visual Field Worsening with Longitudinal Optical Coherence Tomography Data Using a Gated Transformer Network (OPHTHA-D-22-01964)s
- Creators
- Kaihua Hou - Johns Hopkins UniversityChris Bradley - Johns Hopkins MedicinePatrick Herbert - Johns Hopkins MedicineChris Johnson - University of Iowa, Iowa City, Iowa, USAMichael Wall - University of IowaPradeep Y Ramulu - Johns Hopkins MedicineMathias UnberathJithin Yohannan
- Resource Type
- Journal article
- Publication Details
- Ophthalmology (Rochester, Minn.), Vol.130(8), pp.854-862
- DOI
- 10.1016/j.ophtha.2023.03.019
- PMID
- 37003520
- PMCID
- PMC10524436
- eISSN
- 1549-4713
- Grant note
- DOI: 10.13039/100000002, name: National Institutes of Health; DOI: 10.13039/100001818, name: Research to Prevent Blindness
- Language
- English
- Electronic publication date
- 03/30/2023
- Date published
- 08/2023
- Academic Unit
- Neurology; Ophthalmology and Visual Sciences
- Record Identifier
- 9984385054702771
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