Prediction of Central Visual Field Measures From Macular OCT Volume Scans With Deep Learning.

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.

Prediction of visual field progression with serial optic disc photographs using deep learning.

AIM: We tested the hypothesis that visual field (VF) progression can be predicted with a deep learning model based on longitudinal pairs of optic disc photographs (ODP) acquired at earlier time points during follow-up. METHODS: 3919 eyes (2259 patients) with ≥2 ODPs at least 2 years apart, and ≥5 24-2 VF exams spanning ≥3 years of follow-up were included. Serial VF mean deviation (MD) rates of change were estimated starting at the fifth visit and subsequently by adding visits until final visit. VF progression was defined as a statistically significant negative slope at two consecutive visits and final visit. We built a twin-neural network with ResNet50-backbone. A pair of ODPs acquired up to a year before the VF progression date or the last VF in non-progressing eyes were included as input. Primary outcome measures were area under the receiver operating characteristic curve (AUC) and model accuracy. RESULTS: The average (SD) follow-up time and baseline VF MD were 8.1 (4.8) years and -3.3 (4.9) dB, respectively. VF progression was identified in 761 eyes (19%). The median (IQR) time to progression in progressing eyes was 7.3 (4.5-11.1) years. The AUC and accuracy for predicting VF progression were 0.862 (0.812-0.913) and 80.0% (73.9%-84.6%). When only fast-progressing eyes were considered (MD rate < -1.0 dB/year), AUC increased to 0.926 (0.857-0.994). CONCLUSIONS: A deep learning model can predict subsequent glaucoma progression from longitudinal ODPs with clinically relevant accuracy. This model may be implemented, after validation, for predicting glaucoma progression in the clinical setting.

Comparison of Ganglion Cell Layer and Ganglion Cell/Inner Plexiform Layer Measures for Detection of Early Glaucoma.

PURPOSE: To test the hypothesis that macular ganglion cell layer (GCL) measurements detect early glaucoma with higher accuracy than ganglion cell/inner plexiform layer (GCIPL) thickness measurements. DESIGN: Cross-sectional study. PARTICIPANTS: The first cohort included 58 glaucomatous eyes with visual field mean deviation (MD) ≥ -6 dB and 125 normal eyes. The second cohort included 72 glaucomatous and 73 normal/glaucoma suspect (GS) eyes with scans able to create GCL/GCIPL deviation maps. METHODS: In the first cohort, 8 × 8 GCL and GCIPL grids were exported and 5 superior and inferior sectors were defined. Global and sectoral GCL and GCIPL measures were used to predict glaucoma. In the second cohort, proportions of scan areas with abnormal (< 5% and < 1% cutoffs) and supernormal (> 95% and > 99% cutoffs) thicknesses on deviation maps were calculated. The extents of GCL and GCIPL abnormal areas were used to predict glaucoma. MAIN OUTCOME MEASURES: Extents of abnormal GCL/GCIPL regions and areas under receiver operating characteristic curves (AUROC) for prediction of glaucoma were compared between GCL or GCIPL measures. RESULTS: The average ± standard deviation MDs were -3.7 ± 1.6 dB and -2.7 ± 1.8 dB in glaucomatous eyes in the first and second cohorts, respectively. Global GCIPL thickness measures (central 18° × 18° macular region) performed better than GCL for early detection of glaucoma (AUROC, 0.928 vs. 0.884, respectively; P = 0.004). Superior and inferior sector 3 thickness measures provided the best discrimination with both GCL and GCIPL (inferior GCL AUROC, 0.860 vs. GCIPL AUROC, 0.916 [P = 0.001]; superior GCL AUROC, 0.916 vs. GCIPL AUROC, 0.900 [P = 0.24]). The extents of abnormal GCL regions at a 1% cutoff in the central elliptical area were 17.5 ± 22.2% and 6.4 ± 10.8% in glaucomatous and normal/GS eyes, respectively, versus 17.0 ± 22.2% and 5.7 ± 10.5%, respectively, for GCIPL (P = 0.06 for GCL and 0.002 for GCIPL). The extents of GCL and GCIPL supernormal regions were mostly similar in glaucomatous and normal eyes. The best performance for prediction of glaucoma in the second cohort was detected at a P value of < 1% within the entire scan for both GCL and GCIPL (AUC, 0.681 vs. 0.668, respectively; P = 0.29). CONCLUSIONS: Macular GCL and GCIPL thicknesses are equivalent for identifying early glaucoma with current OCT technology. This is likely explained by limitations of inner macular layer segmentation and concurrent changes within the inner plexiform layer in early glaucoma.

Detection of Longitudinal Ganglion Cell/Inner Plexiform Layer Change: Comparison of Two Spectral-Domain Optical Coherence Tomography Devices.

PURPOSE: We compared rates of change of macular ganglion cell/inner plexiform (GCIPL) thickness and proportion of worsening and improving rates from 2 optical coherence tomography (OCT) devices in a cohort of eyes with glaucoma. DESIGN: Longitudinal cohort study. METHODS: In a tertiary glaucoma clinic we evaluated 68 glaucoma eyes with ≥2 years of follow-up and ≥4 OCT images. Macular volume scans from 2 OCT devices were exported, coregistered, and segmented. Global and sectoral GCIPL data from the central 4.8 × 4.0-mm region were extracted. GCIPL rates of change were estimated with linear regression. Permutation analyses were used to control specificity with the 2.5 percentile cutoff point used to define "true" worsening. Main outcome measures included differences in global/sectoral GCIPL rates of change between 2 OCT devices and the proportion of negative vs positive rates of change (P < .05). RESULTS: Average (standard deviation) 24-2 visual field mean deviation, median (interquartile range) follow-up time, and number of OCT images were -9.4 (6.1) dB, 3.8 (3.3-4.2) years, and 6 (5-8), respectively. GCIPL rates of thinning from Spectralis OCT were faster (more negative) compared with Cirrus OCT; differences were significant in superonasal (P = .03) and superotemporal (P = .04) sectors. A higher proportion of significant negative rates was observed with Spectralis OCT both globally and in inferotemporal/superotemporal sectors (P < .04). Permutation analyses confirmed the higher proportion of global and sectoral negative rates of change with Spectralis OCT (P < .001). CONCLUSIONS: Changes in macular GCIPL were detected more frequently on Spectralis' longitudinal volume scans than those of Cirrus OCT. OCT devices are not interchangeable with regard to detection of macular structural progression.

Vision Loss After Glaucoma Surgery: Progressive Macular Thinning as a Sign of Snuff-Out Phenomenon.

We herein report 2 eyes with significant loss of vision after glaucoma filtering surgery that was accompanied by progressive macular thinning detected on macular optical coherence tomography imaging. The findings provide evidence that progressive retinal ganglion cell loss is one of the causes of visual loss after uncomplicated glaucoma surgery.