A deep learning model incorporating spatial and temporal information successfully detects visual field worsening using a consensus based approach

Jasdeep Sabharwal; Kaihua Hou; Patrick Herbert; Chris Bradley; Chris A Johnson; Michael Wall; Pradeep Y Ramulu; Mathias Unberath; Jithin Yohannan

doi:10.1038/s41598-023-28003-6

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A deep learning model incorporating spatial and temporal information successfully detects visual field worsening using a consensus based approach

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A deep learning model incorporating spatial and temporal information successfully detects visual field worsening using a consensus based approach

Jasdeep Sabharwal, Kaihua Hou, Patrick Herbert, Chris Bradley, Chris A Johnson, Michael Wall, Pradeep Y Ramulu, Mathias Unberath and Jithin Yohannan

Scientific reports, Vol.13(1), 1041

01/19/2023

DOI: 10.1038/s41598-023-28003-6

PMCID: PMC9852268

PMID: 36658309

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Abstract

Glaucoma is a leading cause of irreversible blindness, and its worsening is most often monitored with visual field (VF) testing. Deep learning models (DLM) may help identify VF worsening consistently and reproducibly. In this study, we developed and investigated the performance of a DLM on a large population of glaucoma patients. We included 5099 patients (8705 eyes) seen at one institute from June 1990 to June 2020 that had VF testing as well as clinician assessment of VF worsening. Since there is no gold standard to identify VF worsening, we used a consensus of six commonly used algorithmic methods which include global regressions as well as point-wise change in the VFs. We used the consensus decision as a reference standard to train/test the DLM and evaluate clinician performance. 80%, 10%, and 10% of patients were included in training, validation, and test sets, respectively. Of the 873 eyes in the test set, 309 [60.6%] were from females and the median age was 62.4; (IQR 54.8-68.9). The DLM achieved an AUC of 0.94 (95% CI 0.93-0.99). Even after removing the 6 most recent VFs, providing fewer data points to the model, the DLM successfully identified worsening with an AUC of 0.78 (95% CI 0.72-0.84). Clinician assessment of worsening (based on documentation from the health record at the time of the final VF in each eye) had an AUC of 0.64 (95% CI 0.63-0.66). Both the DLM and clinician performed worse when the initial disease was more severe. This data shows that a DLM trained on a consensus of methods to define worsening successfully identified VF worsening and could help guide clinicians during routine clinical care.

Details

Title: Subtitle: A deep learning model incorporating spatial and temporal information successfully detects visual field worsening using a consensus based approach
Creators: Jasdeep Sabharwal - Johns Hopkins University School of Medicine
Kaihua Hou - Johns Hopkins University
Patrick Herbert - Johns Hopkins University
Chris Bradley - Johns Hopkins University School of Medicine
Chris A Johnson - University of Iowa
Michael Wall - University of Iowa
Pradeep Y Ramulu - Johns Hopkins University School of Medicine
Mathias Unberath - Johns Hopkins University
Jithin Yohannan - Johns Hopkins University
Resource Type: Journal article
Publication Details: Scientific reports, Vol.13(1), 1041
DOI: 10.1038/s41598-023-28003-6
PMID: 36658309
PMCID: PMC9852268
NLM abbreviation: Sci Rep
ISSN: 2045-2322
eISSN: 2045-2322
Grant note: K 23 EY032204-02 / NIH HHS
Language: English
Date published: 01/19/2023
Academic Unit: Neurology; Ophthalmology and Visual Sciences
Record Identifier: 9984360857602771

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