City Research Online

Abstract

Purpose: To develop and validate a deep learning (DL) system for predicting each point on visual fields (VFs) from disc and OCT imaging and derive a structure–function mapping.

Design: Retrospective, cross-sectional database study.

Participants: A total of 6437 patients undergoing routine care for glaucoma in 3 clinical sites in the United Kingdom.

Methods: OCT and infrared reflectance (IR) optic disc imaging were paired with the closest VF within 7 days. EfficientNet B2 was used to train 2 single-modality DL models to predict each of the 52 sensitivity points on the 24-2 VF pattern. A policy DL model was designed and trained to fuse the 2 model predictions.

Main Outcome Measures: Pointwise mean absolute error (PMAE).

Results: A total of 5078 imaging scans to VF pairs were used as a held-out test set to measure the final performance. The improvement in PMAE with the policy model was 0.485 (0.438, 0.533) decibels (dB) compared with the IR image of the disc alone and 0.060 (0.047, 0.073) dB with to the OCT alone. The improvement with the policy fusion model was statistically significant (P < 0.0001). Occlusion masking shows that the DL models learned the correct structure–function mapping in a data-driven, feature agnostic fashion.

Conclusions: The multimodal, policy DL model performed the best; it provided explainable maps of its confidence in fusing data from single modalities and provides a pathway for probing the structure–function relationship in glaucoma.

Publication Type:	Article
Additional Information:	© 2022. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/(opens in new tab/window)
Publisher Keywords:	Artificial intelligence, Deep learning, Glaucoma, OCT, Perimetry, Structure-function, Visual field, Artificial intelligence, Deep learning, Glaucoma, OCT, Perimetry, Structure-function, Visual field, Optic Disk, Humans, Optic Nerve Diseases, Glaucoma, Tomography, Optical Coherence, Retrospective Studies, Intraocular Pressure, Visual Fields, Policy, Visual Field Tests, Deep Learning, Artificial intelligence, Deep learning, Glaucoma, OCT, Perimetry, Structure–function, Visual field, Deep Learning, Glaucoma, Humans, Intraocular Pressure, Optic Disk, Optic Nerve Diseases, Policy, Retrospective Studies, Tomography, Optical Coherence, Visual Field Tests, Visual Fields, 1103 Clinical Sciences, 1113 Opthalmology and Optometry, 1117 Public Health and Health Services, Ophthalmology & Optometry
Subjects:	R Medicine > RE Ophthalmology
Departments:	School of Health & Psychological Sciences School of Health & Psychological Sciences > Optometry & Visual Sciences
SWORD Depositor:	Symplectic Administrator

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