Department(s)
Natural Science
Document Type
Article
Version Deposited
Published version
Publication Date
11-2023
Keywords
artificial intelligence, central visual field, convolutional neural network, deep learning, macular OCT, structure–function
Abstract
Purpose: Predict central 10° global and local visual field (VF) measurements from macular optical coherence tomography (OCT) volume scans with deep learning (DL). Methods: This study included 1121 OCT volume scans and 10-2 VFs from 289 eyes (257 patients). Macular scans were used to estimate 10-2 VF mean deviation (MD), threshold sensitivities (TS), and total deviation (TD) values at 68 locations. A three-dimensional (3D) convolutional neural network based on the 3D DenseNet121 architecture was used for prediction. We compared DL predictions to those from baseline linear models. We carried out 10-fold stratified cross-validation to optimize generalizability. The performance of the DL and baseline models was compared based on correlations between ground truth and predicted VF measures and mean absolute error (MAE; ground truth – predicted values). Results: Average (SD) MD was −9.3 (7.7) dB. Average (SD) correlations between predicted and ground truth MD and MD MAE were 0.74 (0.09) and 3.5 (0.4) dB, respectively. Estimation accuracy deteriorated with worsening MD. Average (SD) Pearson correlations between predicted and ground truth TS and MAEs for DL and baseline model were 0.71 (0.05) and 0.52 (0.05) (P < 0.001) and 6.5 (0.6) and 7.5 (0.5) dB (P < 0.001), respectively. For TD, correlation (SD) and MAE (SD) for DL and baseline models were 0.69 (0.02) and 0.48 (0.05) (P < 0.001) and 6.1 (0.5) and 7.8 (0.5) dB (P < 0.001), respectively. Conclusions: Macular OCT volume scans can be used to predict global central VF parameters with clinically relevant accuracy. Translational Relevance: Macular OCT imaging may be used to confirm and supplement central VF findings using deep learning.
Publication Title
Translational Vision Science and Technology
E-ISSN
21642591
Volume
12
Issue
11
DOI
10.1167/tvst.12.11.5
PubMed ID
37917086
Recommended Citation
Mohammadzadeh V, Vepa A, Li C, Wu S, Chew L, Mahmoudinezhad G, Maltz E, Sahin S, Mylavarapu A, Edalati K, Martinyan J, Yalzadeh D, Scalzo F, Caprioli J, Nouri-Mahdavi K. Prediction of Central Visual Field Measures From Macular OCT Volume Scans With Deep Learning. Transl Vis Sci Technol. 2023 Nov 1;12(11):5. doi: 10.1167/tvst.12.11.5. PMID: 37917086; PMCID: PMC10627306.
Comments
Publication can be accessed at this link: https://10.1167/tvst.12.11.5