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Improving the Accuracy and Speed of Visual Field Testing in Glaucoma With Structural Information and Deep Learning

Montesano, G; Lazaridis, G; Ometto, G; Crabb, DP; Garway-Heath, DF; (2023) Improving the Accuracy and Speed of Visual Field Testing in Glaucoma With Structural Information and Deep Learning. Translational Vision Science and Technology , 12 (10) , Article 10. 10.1167/tvst.12.10.10. Green open access

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Abstract

Purpose: To assess the performance of a perimetric strategy using structure–function predictions from a deep learning (DL) model. Methods: Visual field test–retest data from 146 eyes (75 patients) with glaucoma with (median [5th–95th percentile]) 10 [7, 10] tests per eye were used. Structure–function predictions were generated with a previously described DL model using cicumpapillary optical coherence tomography (OCT) scans. Structurally informed prior distributions were built grouping the observed measured sensitivities for each predicted value and recalculated for each subject with a leave-one-out approach. A zippy estimation by sequential testing (ZEST) strategy was used for the simulations (1000 per eye). Groundtruth sensitivities for each eye were the medians of the test–retest values. Two variations of ZEST were compared in terms of speed (average total number of presentations [NP] per eye) and accuracy (average mean absolute error [MAE] per eye), using either a combination of normal and abnormal thresholds (ZEST) or the calculated structural distributions (S-ZEST) as prior information. Two additional versions of these strategies employing spatial correlations were tested. Results: S-ZEST was significantly faster, with a mean average NP of 213.87 (SD = 28.18), than ZEST, with a mean average NP of 255.65 (SD = 50.27) (P < 0.001). The average MAE was smaller for S-ZEST (1.98; SD = 2.37) than ZEST (2.43; SD = 2.69) (P < 0.001). Spatial correlations further improved both strategies (P < 0.001), but the differences between ZEST and S-ZEST remained significant (P < 0.001). Conclusions: DL structure–function predictions can significantly improve perimetric tests. Translational Relevance: DL structure–function predictions from clinically available OCT scans can improve perimetry in glaucoma patients.

Type: Article
Title: Improving the Accuracy and Speed of Visual Field Testing in Glaucoma With Structural Information and Deep Learning
Location: United States
Open access status: An open access version is available from UCL Discovery
DOI: 10.1167/tvst.12.10.10
Publisher version: https://doi.org/10.1167/tvst.12.10.10
Language: English
Additional information: This work is licensed under a Creative Commons Attribution 4.0 International License.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Institute of Ophthalmology
URI: https://discovery.ucl.ac.uk/id/eprint/10180171
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