Diagnostic accuracy of optic nerve head and macula OCT parameters for detecting glaucoma in eyes with and without high axial myopia.

PURPOSE: To characterize structural differences and assess the diagnostic accuracy of optic nerve head (ONH) and macula optical coherence tomography (OCT) parameters to detect glaucoma in eyes with and without high axial myopia. DESIGN: Cross-sectional study METHODS: 368 glaucoma and 411 healthy eyes with no axial myopia, 393 glaucoma and 271 healthy eyes with mild axial myopia and 124 glaucoma and 85 healthy eyes with high axial myopia were included. Global and sectoral peripapillary retinal nerve fiber layer thickness (pRNFLT), Bruch's membrane opening minimum rim width (BMO-MRW), ganglion cell inner plexiform layer thickness (GCIPLT), and macula RNFLT (mRNFLT) were compared and the diagnostic accuracy for glaucoma detection was evaluated using adjusted area under the receiver operating characteristic curve (AUC). RESULTS: Diagnostic accuracy for ONH and macula parameters to detect glaucoma was generally high and differed by myopia group. For ONH parameters the diagnostic accuracy was highest for global (AUC=0.95) and inferotemporal (AUC=0.91) pRNFLT for high myopes and global BMO-MRW for non-myopes (AUC=1.0) and mild myopes (AUC=0.97). For macula parameters, the diagnostic accuracy was higher in high myopes with 6 of the 11 GCIPLT global/sectors having adjusted AUCs > 0.90 compared to non-high myopes with no AUCs > 0.90. In all myopia groups, mRNFLT had lower AUCs than GCIPLT. CONCLUSIONS: The diagnostic accuracy for pRNFL and GCIPL was high for high axial myopic eyes and shows promise for glaucoma detection in high myopes. Further analysis is needed to determine whether the high diagnostic accuracy can be confirmed in other populations.

Racial Differences in Diagnostic Accuracy of Retinal Nerve Fiber Layer Thickness in Primary Open-Angle Glaucoma.

Purpose: To evaluate the diagnostic accuracy of retinal nerve fiber layer thickness (RNFLT) by spectral-domain optical coherence tomography (OCT) in primary open-angle glaucoma (POAG) in eyes of African (AD) and European descent (ED). Design: Comparative diagnostic accuracy analysis by race. Participants: 379 healthy eyes (125 AD and 254 ED) and 442 glaucomatous eyes (226 AD and 216 ED) from the Diagnostic Innovations in Glaucoma Study and the African Descent and Glaucoma Evaluation Study. Methods: Spectralis (Heidelberg Engineering GmbH) and Cirrus (Carl Zeiss Meditec) OCT scans were taken within one year from each other. Main Outcome Measures: Diagnostic accuracy of RNFLT measurements. Results: Diagnostic accuracy for Spectralis-RNFLT was significantly lower in eyes of AD compared to those of ED (area under the receiver operating curve [AUROC]: 0.85 and 0.91, respectively, P=0.04). Results for Cirrus-RNFLT were similar but did not reach statistical significance (AUROC: 0.86 and 0.90 in AD and ED, respectively, P =0.33). Adjustments for age, central corneal thickness, axial length, disc area, visual field mean deviation, and intraocular pressure yielded similar results. Conclusions: OCT-RNFLT has lower diagnostic accuracy in eyes of AD compared to those of ED. This finding was generally robust across two OCT instruments and remained after adjustment for many potential confounders. Further studies are needed to explore the potential sources of this difference.

Proactive Decision Support for Glaucoma Treatment: Predicting Surgical Interventions with Clinically Available Data.

A longitudinal ophthalmic dataset was used to investigate multi-modal machine learning (ML) models incorporating patient demographics and history, clinical measurements, optical coherence tomography (OCT), and visual field (VF) testing in predicting glaucoma surgical interventions. The cohort included 369 patients who underwent glaucoma surgery and 592 patients who did not undergo surgery. The data types used for prediction included patient demographics, history of systemic conditions, medication history, ophthalmic measurements, 24-2 VF results, and thickness measurements from OCT imaging. The ML models were trained to predict surgical interventions and evaluated on independent data collected at a separate study site. The models were evaluated based on their ability to predict surgeries at varying lengths of time prior to surgical intervention. The highest performing predictions achieved an AUC of 0.93, 0.92, and 0.93 in predicting surgical intervention at 1 year, 2 years, and 3 years, respectively. The models were also able to achieve high sensitivity (0.89, 0.77, 0.86 at 1, 2, and 3 years, respectively) and specificity (0.85, 0.90, and 0.91 at 1, 2, and 3 years, respectively) at an 0.80 level of precision. The multi-modal models trained on a combination of data types predicted surgical interventions with high accuracy up to three years prior to surgery and could provide an important tool to predict the need for glaucoma intervention.

Deep Learning Identifies High-Quality Fundus Photographs and Increases Accuracy in Automated Primary Open Angle Glaucoma Detection.

Purpose: To develop and evaluate a deep learning (DL) model to assess fundus photograph quality, and quantitatively measure its impact on automated POAG detection in independent study populations. Methods: Image quality ground truth was determined by manual review of 2815 fundus photographs of healthy and POAG eyes from the Diagnostic Innovations in Glaucoma Study and African Descent and Glaucoma Evaluation Study (DIGS/ADAGES), as well as 11,350 from the Ocular Hypertension Treatment Study (OHTS). Human experts assessed a photograph as high quality if of sufficient quality to determine POAG status and poor quality if not. A DL quality model was trained on photographs from DIGS/ADAGES and tested on OHTS. The effect of DL quality assessment on DL POAG detection was measured using area under the receiver operating characteristic (AUROC). Results: The DL quality model yielded an AUROC of 0.97 for differentiating between high- and low-quality photographs; qualitative human review affirmed high model performance. Diagnostic accuracy of the DL POAG model was significantly greater (P < 0.001) in good (AUROC, 0.87; 95% CI, 0.80-0.92) compared with poor quality photographs (AUROC, 0.77; 95% CI, 0.67-0.88). Conclusions: The DL quality model was able to accurately assess fundus photograph quality. Using automated quality assessment to filter out low-quality photographs increased the accuracy of a DL POAG detection model. Translational Relevance: Incorporating DL quality assessment into automated review of fundus photographs can help to decrease the burden of manual review and improve accuracy for automated DL POAG detection.

Central visual field damage in glaucoma eyes with choroidal microvasculature dropout with and without high axial myopia.

PURPOSE: To characterise the relationship between a deep-layer microvasculature dropout (MvD) and central visual field (VF) damage in primary open-angle glaucoma (POAG) patients with and without high axial myopia. DESIGN: Cross-sectional study. METHODS: Seventy-one eyes (49 patients) with high axial myopia and POAG and 125 non-highly myopic POAG eyes (97 patients) were enrolled. Presence, area and angular circumference of juxtapapillary MvD were evaluated on optical coherence tomography angiography B-scans and en-face choroidal images. RESULTS: Juxtapapillary MvD was detected more often in the highly myopic POAG eyes (43 eyes, 86%) than in the non-highly myopic eyes (73 eyes, 61.9%; p=0.002). In eyes with MvD, MvD area and angular circumference (95% CI) were significantly larger in the highly myopic eyes compared with the non-highly myopic eyes (area: (0.69 (0.40, 0.98) mm2 vs 0.31 (0.19, 0.42) mm2, p=0.011) and (angular circumference: 84.3 (62.9, 105.8) vs 74.5 (58.3, 90.9) degrees, p<0.001), respectively. 24-2 VF mean deviation (MD) was significantly worse in eyes with MvD compared with eyes without MvD in both groups (p<0.001). After adjusting for 24-2 MD VF, central VF defects were more frequently found in eyes with MvD compared with eyes without MvD (82.7% vs 60.9%, p<0.001). In multivariable analysis, higher intraocular pressure, worse 24-2 VF MD, longer axial length and greater MvD area and angular circumference were associated with worse 10-2 VF MD. CONCLUSIONS: MvD was more prevalent and larger in POAG eyes with high myopia than in non-highly myopic POAG eyes. In both groups, eyes with MvD showed worse glaucoma severity and more central VF defects.

One-Vote Veto: Semi-Supervised Learning for Low-Shot Glaucoma Diagnosis.

Convolutional neural networks (CNNs) are a promising technique for automated glaucoma diagnosis from images of the fundus, and these images are routinely acquired as part of an ophthalmic exam. Nevertheless, CNNs typically require a large amount of well-labeled data for training, which may not be available in many biomedical image classification applications, especially when diseases are rare and where labeling by experts is costly. This article makes two contributions to address this issue: 1) It extends the conventional Siamese network and introduces a training method for low-shot learning when labeled data are limited and imbalanced, and 2) it introduces a novel semi-supervised learning strategy that uses additional unlabeled training data to achieve greater accuracy. Our proposed multi-task Siamese network (MTSN) can employ any backbone CNN, and we demonstrate with four backbone CNNs that its accuracy with limited training data approaches the accuracy of backbone CNNs trained with a dataset that is 50 times larger. We also introduce One-Vote Veto (OVV) self-training, a semi-supervised learning strategy that is designed specifically for MTSNs. By taking both self-predictions and contrastive predictions of the unlabeled training data into account, OVV self-training provides additional pseudo labels for fine-tuning a pre-trained MTSN. Using a large (imbalanced) dataset with 66,715 fundus photographs acquired over 15 years, extensive experimental results demonstrate the effectiveness of low-shot learning with MTSN and semi-supervised learning with OVV self-training. Three additional, smaller clinical datasets of fundus images acquired under different conditions (cameras, instruments, locations, populations) are used to demonstrate the generalizability of the proposed methods.

Multimodal Deep Learning Classifier for Primary Open Angle Glaucoma Diagnosis Using Wide-Field Optic Nerve Head Cube Scans in Eyes With and Without High Myopia.

PRCIS: An optical coherence tomography (OCT)-based multimodal deep learning (DL) classification model, including texture information, is introduced that outperforms single-modal models and multimodal models without texture information for glaucoma diagnosis in eyes with and without high myopia. BACKGROUND/AIMS: To evaluate the diagnostic accuracy of a multimodal DL classifier using wide OCT optic nerve head cube scans in eyes with and without axial high myopia. MATERIALS AND METHODS: Three hundred seventy-one primary open angle glaucoma (POAG) eyes and 86 healthy eyes, all without axial high myopia [axial length (AL) ≤ 26 mm] and 92 POAG eyes and 44 healthy eyes, all with axial high myopia (AL > 26 mm) were included. The multimodal DL classifier combined features of 3 individual VGG-16 models: (1) texture-based en face image, (2) retinal nerve fiber layer (RNFL) thickness map image, and (3) confocal scanning laser ophthalmoscope (cSLO) image. Age, AL, and disc area adjusted area under the receiver operating curves were used to compare model accuracy. RESULTS: Adjusted area under the receiver operating curve for the multimodal DL model was 0.91 (95% CI = 0.87, 0.95). This value was significantly higher than the values of individual models [0.83 (0.79, 0.86) for texture-based en face image; 0.84 (0.81, 0.87) for RNFL thickness map; and 0.68 (0.61, 0.74) for cSLO image; all P ≤ 0.05]. Using only highly myopic eyes, the multimodal DL model showed significantly higher diagnostic accuracy [0.89 (0.86, 0.92)] compared with texture en face image [0.83 (0.78, 0.85)], RNFL [0.85 (0.81, 0.86)] and cSLO image models [0.69 (0.63, 0.76)] (all P ≤ 0.05). CONCLUSIONS: Combining OCT-based RNFL thickness maps with texture-based en face images showed a better ability to discriminate between healthy and POAG than thickness maps alone, particularly in high axial myopic eyes.

Detecting Glaucoma from Fundus Photographs Using Deep Learning without Convolutions: Transformer for Improved Generalization.

Purpose: To compare the diagnostic accuracy and explainability of a Vision Transformer deep learning technique, Data-efficient image Transformer (DeiT), and ResNet-50, trained on fundus photographs from the Ocular Hypertension Treatment Study (OHTS) to detect primary open-angle glaucoma (POAG) and identify the salient areas of the photographs most important for each model's decision-making process. Design: Evaluation of a diagnostic technology. Subjects Participants and Controls: Overall 66 715 photographs from 1636 OHTS participants and an additional 5 external datasets of 16 137 photographs of healthy and glaucoma eyes. Methods: Data-efficient image Transformer models were trained to detect 5 ground-truth OHTS POAG classifications: OHTS end point committee POAG determinations because of disc changes (model 1), visual field (VF) changes (model 2), or either disc or VF changes (model 3) and Reading Center determinations based on disc (model 4) and VFs (model 5). The best-performing DeiT models were compared with ResNet-50 models on OHTS and 5 external datasets. Main Outcome Measures: Diagnostic performance was compared using areas under the receiver operating characteristic curve (AUROC) and sensitivities at fixed specificities. The explainability of the DeiT and ResNet-50 models was compared by evaluating the attention maps derived directly from DeiT to 3 gradient-weighted class activation map strategies. Results: Compared with our best-performing ResNet-50 models, the DeiT models demonstrated similar performance on the OHTS test sets for all 5 ground-truth POAG labels; AUROC ranged from 0.82 (model 5) to 0.91 (model 1). Data-efficient image Transformer AUROC was consistently higher than ResNet-50 on the 5 external datasets. For example, AUROC for the main OHTS end point (model 3) was between 0.08 and 0.20 higher in the DeiT than ResNet-50 models. The saliency maps from the DeiT highlight localized areas of the neuroretinal rim, suggesting important rim features for classification. The same maps in the ResNet-50 models show a more diffuse, generalized distribution around the optic disc. Conclusions: Vision Transformers have the potential to improve generalizability and explainability in deep learning models, detecting eye disease and possibly other medical conditions that rely on imaging for clinical diagnosis and management.

A Deep Learning Approach to Improve Retinal Structural Predictions and Aid Glaucoma Neuroprotective Clinical Trial Design.

PURPOSE: To investigate the efficacy of a deep learning regression method to predict macula ganglion cell-inner plexiform layer (GCIPL) and optic nerve head (ONH) retinal nerve fiber layer (RNFL) thickness for use in glaucoma neuroprotection clinical trials. DESIGN: Cross-sectional study. PARTICIPANTS: Glaucoma patients with good quality macula and ONH scans enrolled in 2 longitudinal studies, the African Descent and Glaucoma Evaluation Study and the Diagnostic Innovations in Glaucoma Study. METHODS: Spectralis macula posterior pole scans and ONH circle scans on 3327 pairs of GCIPL/RNFL scans from 1096 eyes (550 patients) were included. Participants were randomly distributed into a training and validation dataset (90%) and a test dataset (10%) by participant. Networks had access to GCIPL and RNFL data from one hemiretina of the probe eye and all data of the fellow eye. The models were then trained to predict the GCIPL or RNFL thickness of the remaining probe eye hemiretina. MAIN OUTCOME MEASURES: Mean absolute error (MAE) and squared Pearson correlation coefficient (r2) were used to evaluate model performance. RESULTS: The deep learning model was able to predict superior and inferior GCIPL thicknesses with a global r2 value of 0.90 and 0.86, r2 of mean of 0.90 and 0.86, and mean MAE of 3.72 µm and 4.2 µm, respectively. For superior and inferior RNFL thickness predictions, model performance was slightly lower, with a global r2 of 0.75 and 0.84, r2 of mean of 0.81 and 0.82, and MAE of 9.31 µm and 8.57 µm, respectively. There was only a modest decrease in model performance when predicting GCIPL and RNFL in more severe disease. Using individualized hemiretinal predictions to account for variability across patients, we estimate that a clinical trial can detect a difference equivalent to a 25% treatment effect over 24 months with an 11-fold reduction in the number of patients compared to a conventional trial. CONCLUSIONS: Our deep learning models were able to accurately estimate both macula GCIPL and ONH RNFL hemiretinal thickness. Using an internal control based on these model predictions may help reduce clinical trial sample size requirements and facilitate investigation of new glaucoma neuroprotection therapies. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found after the references.

Macula structural and vascular differences in glaucoma eyes with and without high axial myopia.

AIMS: To identify clinically relevant parameters for identifying glaucoma in highly myopic eyes, an investigation was conducted of the relationship between the thickness of various retinal layers and the superficial vessel density (sVD) of the macula with axial length (AL) and visual field mean deviation (VFMD). METHODS: 270 glaucoma patients (438 eyes) participating in the Diagnostic Innovations in Glaucoma cross-sectional study representing three axial myopia groups (non-myopia: n=163 eyes; mild myopia: n=218 eyes; high myopia (AL>26 mm): n=57 eyes) who completed macular optical coherence tomography (OCT) and OCT-angiography imaging were included. Associations of AL and VFMD with the thickness of the ganglion cell inner plexiform layer (GCIPL), macular retinal nerve fibre layer (mRNFL), ganglion cell complex (GCC), macular choroidal thickness (mCT) and sVD were evaluated. RESULTS: Thinner Global GCIPL and GCC were significantly associated with worse VFMD (R2=34.5% and R2=32.9%; respectively p<0.001), but not with AL (all p>0.1). Thicker mRNFL showed a weak association with increasing AL (R2=2.4%; p=0.005) and a positive association with VFMD (global R2=19.2%; p<0.001). Lower sVD was weakly associated with increasing AL (R2=1.8%; p=0.028) and more strongly associated with more severe glaucoma VFMD (R2=29.6%; p<0.001). Thinner mCT was associated with increasing AL (R2=15.5% p<0.001) and not associated with VFMD (p=0.194). mRNFL was thickest while mCT was thinnest in all sectors of high myopic eyes. CONCLUSIONS: As thinner GCIPL and GCC were associated with increasing severity of glaucoma but were not significantly associated with AL, they may be useful for monitoring glaucoma in highly myopic eyes.

Long-term reproducibility of optical coherence tomography angiography in healthy and stable glaucomatous eyes.

BACKGROUND/AIMS: To assess and compare long-term reproducibility of optic nerve head (ONH) and macula optical coherence tomography angiography (OCTA) vascular parameters and optical coherence tomography (OCT) thickness parameters in stable primary open-angle glaucoma (POAG), glaucoma suspect and healthy eyes. METHODS: Eighty-eight eyes (15 healthy, 38 glaucoma suspect and 35 non-progressing POAG) of 68 subjects who had at least three visits within 1-1.5 years with OCTA and OCT imaging (Angiovue; Optovue, Fremont, California, USA) on the same day were included. A series of vascular and thickness parameters were measured including macular parafoveal vessel density (pfVD), ONH circumpapillary capillary density (cpCD), macular parafoveal ganglion cell complex (pfGCC) and ONH circumpapillary retinal nerve fibre layer (cpRNFL). A random effects analysis of variance model was used to estimate intraclass correlation (ICC) coefficients and long-term variability estimates. RESULTS: ICC was lower for OCTA (pfVD 0.823 (95% CI 0.736 to 0.888) and cpCD 0.871 (0.818 to 0.912)) compared with OCT (pfGCC 0.995 (0.993 to 0.997) and cpRNFL 0.975 (0.964 to 0.984)). Within-subject test-retest SD was 1.17% and 1.22% for pfVD and cpCD, and 0.57 and 1.22 µm for pfGCC and cpRNFL. Older age and lower signal strength index were associated with decreasing long-term variability of vessel densities. CONCLUSIONS: OCTA-measured macula and ONH vascular parameters have good long-term reproducibility, supporting the use of this instrument for longitudinal analysis. OCTA long-term reproducibility is less than OCT-measured thickness reproducibility. This needs to be taken into consideration when serial OCTA images are evaluated for change. TRIAL REGISTRATION NUMBER: NCT00221897.

Augmenting Kalman Filter Machine Learning Models with Data from OCT to Predict Future Visual Field Loss: An Analysis Using Data from the African Descent and Glaucoma Evaluation Study and the Diagnostic Innovation in Glaucoma Study.

Purpose: To assess whether the predictive accuracy of machine learning algorithms using Kalman filtering for forecasting future values of global indices on perimetry can be enhanced by adding global retinal nerve fiber layer (RNFL) data and whether model performance is influenced by the racial composition of the training and testing sets. Design: Retrospective, longitudinal cohort study. Participants: Patients with open-angle glaucoma (OAG) or glaucoma suspects enrolled in the African Descent and Glaucoma Evaluation Study or Diagnostic Innovation in Glaucoma Study. Methods: We developed a Kalman filter (KF) with tonometry and perimetry data (KF-TP) and another KF with tonometry, perimetry, and global RNFL data (KF-TPO), comparing these models with one another and with 2 linear regression (LR) models for predicting mean deviation (MD) and pattern standard deviation values 36 months into the future for patients with OAG and glaucoma suspects. We also compared KF model performance when trained on individuals of European and African descent and tested on patients of the same versus the other race. Main Outcome Measures: Predictive accuracy (percentage of MD values forecasted within the 95% repeatability interval) differences among the models. Results: Among 362 eligible patients, the mean ± standard deviation age at baseline was 71.3 ± 10.4 years; 196 patients (54.1%) were women; 202 patients (55.8%) were of European descent, and 139 (38.4%) were of African descent. Among patients with OAG (n = 296), the predictive accuracy for 36 months in the future was higher for the KF models (73.5% for KF-TP, 71.2% for KF-TPO) than for the LR models (57.5%, 58.0%). Predictive accuracy did not differ significantly between KF-TP and KF-TPO (P = 0.20). If the races of the training and testing set patients were aligned (versus nonaligned), the mean absolute prediction error of future MD improved 0.39 dB for KF-TP and 0.48 dB for KF-TPO. Conclusions: Adding global RNFL data to existing KFs minimally improved their predictive accuracy. Although KFs attained better predictive accuracy when the races of the training and testing sets were aligned, these improvements were modest. These findings will help to guide implementation of KFs in clinical practice.

Comparison of Optic Disc Ovality Index and Rotation Angle Measurements in Myopic Eyes Using Photography and OCT Based Techniques.

Purpose: To compare optic nerve head (ONH) ovality index and rotation angle measurements based on semi-automated delineation of the clinical ONH margin derived from photographs and automated BMO configuration derived from optical coherence tomography (OCT) images in healthy and glaucomatous eyes with high-, mild- and no axial myopia. Methods: One hundred seventy-five healthy and glaucomatous eyes of 146 study participants enrolled in the Diagnostic Innovations in Glaucoma Study (DIGS) with optic disc photographs and Spectralis OCT ONH scans acquired on the same day were stratified by level of axial myopia (non-myopic [n = 56, axial length (AL) <24 mm], mild-myopic [n = 58, AL 24-26 mm] and high-myopic [n = 32, AL >26 mm]. The clinical disc margin of each photograph was manually annotated, and semi-automated measurements were recorded of the ovality index and rotation angle based on a best-fit ellipse generated using ImageJ software. These semi-automated photograph-based measurements were compared to ovality index and rotation angle generated from custom automated BMO-based analysis using segmented OCT ONH volumes. R 2 values from linear mixed effects models were used to describe the associations between semi-automated, photograph-based and automated OCT-based measurements. Results: Average (95% CI) axial length was 23.3 (23.0, 23.3) mm, 24.8 (24.7, 25.0) mm and 26.8 (26.6, 27.0) mm in non-myopic, mild-myopic and high-myopic eyes, respectively (ANOVA, p ≤ 0.001 for all). The R 2 association (95% CI) between semi-automated photograph-based and automated OCT-based assessment of ONH OI for all eyes was [0.26 (0.16, 0.36); p < 0.001]. This association was weakest in non-myopic eyes [0.09 (0.01, 0.26); p = 0.02], followed by mild-myopic eyes [0.13 (0.02, 0.29); p = 0.004] and strongest in high-myopic eyes [0.40 (0.19, 0.60); p < 0.001]. No significant associations were found between photography- and OCT-based assessment of rotation angle with R 2 values ranging from 0.00 (0.00, 0.08) in non-myopic eyes to 0.03 (0.00, 0.21) in high-myopic eyes (all associations p ≥ 0.33). Conclusions: Agreement between photograph-based and automated OCT-based ONH morphology measurements is limited, suggesting that these methods cannot be used interchangeably for characterizing myopic changes in the ONH.

The Association Between Regional Macula Vessel Density and Central Visual Field Damage in Advanced Glaucoma Eyes.

PRCIS: Both macular superficial vessel density and ganglion cell complex (GCC) thickness measurement are significantly associated with regional and global 10-degree central visual field (VF) sensitivity in advanced glaucoma. PURPOSE: The purpose of this study was to evaluate the regional and global structure-function relationships between macular vessel density (MVD) assessed by optical coherence tomography angiography (OCTA) and 10-2 VF sensitivity in advanced open angle glaucoma eyes. METHODS: Macular OCTA and 10-2 VF sensitivity of 44 patients [mean deviation (MD) <-10 dB] were evaluated. Regional and global VF mean sensitivity (MS) was calculated from total deviation plots. Superficial and deep MVD were obtained from 3 × 3 and 6×6 mm 2 OCTA scans using 2 sectoral definitions. Spectral-domain optical coherence tomography macular GCC thickness was obtained simultaneously from the same scan as the MVD measurements. Linear regression models were used to assess the associations ( R2 ). RESULTS: Lower MS was significantly associated with a reduction in superficial MVD and GCC in each region of both scan sizes for both maps. Associations were weaker in the individual sectors of the whole image grid than the Early Treatment Diabetic Retinopathy Study map. Deep-layer MVD was not associated with central MS. Although 6×6 mm 2 and perifoveal vessel density had better associations with central 10-degree MS compared with GCC thickness (eg, R2 from 25.7 to 48.1 µm and 7.8% to 32.5%, respectively), GCC associations were stronger than MVD associations in the central 5-degree MS. CONCLUSIONS: Given a stronger MVD-central 10-degree VF association compared with GCC, as well as stronger GCC-central 5-degree VF association compared with MVD, MVD and GCC are complementary measurements in eyes with advanced glaucoma. A longitudinal analysis is needed to determine the relative utility of the GCC and MVD measurements.

Diagnostic Accuracy of Macular Thickness Map and Texture En Face Images for Detecting Glaucoma in Eyes With Axial High Myopia.

PURPOSE: To evaluate the diagnostic accuracy of a novel optical coherence tomography texture-based en face image analysis (SALSA-Texture) that requires segmentation of only 1 retinal layer for glaucoma detection in eyes with axial high myopia, and to compare SALSA-Texture with standard macular ganglion cell-inner plexiform layer (GCIPL) thickness, macular retinal nerve fiber layer (mRNFL) thickness, and ganglion cell complex (GCC) thickness maps. DESIGN: Comparison of diagnostic approaches. METHODS: Cross-sectional data were collected from 92 eyes with primary open-angle glaucoma (POAG) and 44 healthy control eyes with axial high myopia (axial length >26 mm). Optical coherence tomography texture en face images, developed using SALSA-Texture to model the spatial arrangement patterns of the pixel intensities in a region, were generated from 70-µm slabs just below the vitreal border of the inner limiting membrane. Areas under the receiver operating characteristic curves (AUROCs) and areas under the precision recall curves (AUPRCs) adjusted for both eyes, axial length, age, disc area, and image quality were used to compare different approaches. RESULTS: The best parameter-adjusted AUROCs (95% confidence intervals) for differentiating between healthy and glaucoma high myopic eyes were 0.92 (0.88-0.94) for texture en face images, 0.88 (0.86-0.91) for macular RNFL thickness, 0.87 (0.83-0.89) for macula GCIPL thickness, and 0.87 (0.84-0.89) for GCC thickness. A subset analysis of highly advanced myopic eyes (axial length ≥27 mm; 38 glaucomatous eyes and 22 healthy eyes) showed the best AUROC was 0.92 (0.89-0.94) for texture en face images compared with 0.86 (0.84-0.88) for macular GCIPL, 0.86 (0.84-0.88) for GCC, and 0.84 (0.81-0.87) for RNFL thickness (P ≤ .02 compared with texture for all comparisons). CONCLUSION: The current results suggest that our novel en face texture-based analysis method can improve on most investigated macular tissue thickness measurements for discriminating between highly myopic glaucomatous and highly myopic healthy eyes. While further investigation is needed, texture en face images show promise for improving the detection of glaucoma in eyes with high myopia where traditional retinal layer segmentation often is challenging.

OCT-Angiography Face Mask-Associated Artifacts During the COVID-19 Pandemic.

PRCIS: Face mask wearing has no significant effects on artifacts or vessel density measurements in optic nerve head (ONH) and macular optical coherence tomography-angiography (OCT-A) scans. PURPOSE: The aim was to assess the difference in area of artifacts observed in optical OCT-A scans with and without face mask wear and to verify if mask wear interferes with OCT-A vessel density measurements. SUBJECTS AND CONTROLS: A total of 64 eyes of 10 healthy subjects, 4 ocular hypertensive, 8 glaucoma suspects, and 17 glaucoma patients were included. MATERIALS AND METHODS: High-density ONH and macula OCT-A scans were obtained in patients with and without surgical masks. Seven different artifacts (motion, decentration, defocus, shadow, segmentation failure, blink, and Z-offset) were quantitatively evaluated by 2 trained graders. The changes in the area (% of scan area) of artifacts, without and with mask wearing, and differences of vessel density were evaluated. RESULTS: Trends of increasing motion artifact area for the ONH scans [4.23 (-0.52, 8.98) %, P=0.08] and defocus artifact area for the macular scans [1.06 (-0.14, 2.26) %, P=0.08] were found with face mask wear. However, there were no significant differences in the mean % area of any artifacts (P>0.05 for all). Further, the estimated mean difference in vessel density in images acquired without and with masks was not significant for any type of artifact. CONCLUSION: Face mask wearing had no significant effect on area of artifacts or vessel density measurements. OCT-A vessel density measurements can be acquired reliably with face mask wear during the pandemic.

Detecting Glaucoma in the Ocular Hypertension Study Using Deep Learning.

Importance: Automated deep learning (DL) analyses of fundus photographs potentially can reduce the cost and improve the efficiency of reading center assessment of end points in clinical trials. Objective: To investigate the diagnostic accuracy of DL algorithms trained on fundus photographs from the Ocular Hypertension Treatment Study (OHTS) to detect primary open-angle glaucoma (POAG). Design, Setting, and Participants: In this diagnostic study, 1636 OHTS participants from 22 sites with a mean (range) follow-up of 10.7 (0-14.3) years. A total of 66 715 photographs from 3272 eyes were used to train and test a ResNet-50 model to detect the OHTS Endpoint Committee POAG determination based on optic disc (287 eyes, 3502 photographs) and/or visual field (198 eyes, 2300 visual fields) changes. Three independent test sets were used to evaluate the generalizability of the model. Main Outcomes and Measures: Areas under the receiver operating characteristic curve (AUROC) and sensitivities at fixed specificities were calculated to compare model performance. Evaluation of false-positive rates was used to determine whether the DL model detected POAG before the OHTS Endpoint Committee POAG determination. Results: A total of 1147 participants were included in the training set (661 [57.6%] female; mean age, 57.2 years; 95% CI, 56.6-57.8), 167 in the validation set (97 [58.1%] female; mean age, 57.1 years; 95% CI, 55.6-58.7), and 322 in the test set (173 [53.7%] female; mean age, 57.2 years; 95% CI, 56.1-58.2). The DL model achieved an AUROC of 0.88 (95% CI, 0.82-0.92) for the OHTS Endpoint Committee determination of optic disc or VF changes. For the OHTS end points based on optic disc changes or visual field changes, AUROCs were 0.91 (95% CI, 0.88-0.94) and 0.86 (95% CI, 0.76-0.93), respectively. False-positive rates (at 90% specificity) were higher in photographs of eyes that later developed POAG by disc or visual field (27.5% [56 of 204]) compared with eyes that did not develop POAG (11.4% [50 of 440]) during follow-up. The diagnostic accuracy of the DL model developed on the optic disc end point applied to 3 independent data sets was lower, with AUROCs ranging from 0.74 (95% CI, 0.70-0.77) to 0.79 (95% CI, 0.78-0.81). Conclusions and Relevance: The model's high diagnostic accuracy using OHTS photographs suggests that DL has the potential to standardize and automate POAG determination for clinical trials and management. In addition, the higher false-positive rate in early photographs of eyes that later developed POAG suggests that DL models detected POAG in some eyes earlier than the OHTS Endpoint Committee, reflecting the OHTS design that emphasized a high specificity for POAG determination by requiring a clinically significant change from baseline.

Agreement between Compass Fundus Perimeter New Grid and 10-2 Testing Protocols for Detecting Central Visual Field Defects.

PURPOSE: To evaluate the agreement between Compass New Grid (NG) and 10-2 test protocols for detecting early glaucomatous defects in the central 10 degrees of the visual field (CVFD). DESIGN: Cross-sectional study. PARTICIPANTS: A total of 123 eyes of 14 healthy individuals, 17 glaucoma suspects, and 32 glaucoma patients were enrolled. METHODS: Subjects performed NG and 10-2 Compass automated perimetry testing within 1 week. For both test protocols, total deviation (TD) and pattern deviation (PD) plot CVFDs were defined by 3 contiguous points with probabilities of <5%, <2%, <2% or <5%, <1%, <1%. Cohen's Kappa statistic was used to assess agreement between NG and 10-2 for identifying CVFDs. The Spectralis GMPE Hood Glaucoma Report (investigational software version) macula deviation analysis obtained within 1 year was used for calculating sensitivities and specificities of test protocols. MAIN OUTCOME MEASURES: Protocols' agreement, sensitivity, and specificity. RESULTS: Fair to moderate agreement was observed between NG and 10-2 protocols for detecting presence of superior CVFDs on TD (k = 0.57) and PD (k = 0.26) plots and for detecting inferior CVFDs on TD (0.49) and PD (0.27) plots. With the use of OCT macula deviation maps, specificity for detecting CVFD was consistently higher with NG than 10-2 tests for TD plots of the superior hemifield (0.82 and 0.65), inferior hemifield (0.92 and 0.84), and PD plots of the superior hemifield (0.81 and 0.36) and inferior hemifield (0.86 and 0.52). Sensitivity of NG was consistently lower than TD plots of the superior hemifield (0.48 and 0.72), inferior hemifield (0.28 and 0.46), and PD plots of the superior hemifield (0.48 and 0.78) and inferior hemifield (0.20 and 0.52). By using pattern standard deviation (PSD) criterion, the mean PSD values for 10-2 and NG VF tests were 1.61 (95% confidence interval [CI], 1.26-1.96) and 1.81 (95% CI, 1.45-2.17) (P < 0.001), respectively. CONCLUSIONS: Although the Compass NG detected fewer CVFDs than the 10-2 test protocol, it did detect CVFDs that were not observed in the Compass 24-2 test in patients with early glaucoma. Therefore, NG may be particularly useful in clinical situations when higher specificity is desired or PSD criterion is used.

Deep Learning Image Analysis of Optical Coherence Tomography Angiography Measured Vessel Density Improves Classification of Healthy and Glaucoma Eyes.

PURPOSE: To compare convolutional neural network (CNN) analysis of en face vessel density images to gradient boosting classifier (GBC) analysis of instrument-provided, feature-based optical coherence tomography angiography (OCTA) vessel density measurements and OCT retinal nerve fiber layer (RNFL) thickness measurements for classifying healthy and glaucomatous eyes. DESIGN: Comparison of diagnostic approaches. METHODS: A total of 130 eyes of 80 healthy individuals and 275 eyes of 185 glaucoma patients with optic nerve head (ONH) OCTA and OCT imaging were included. Classification performance of a VGG16 CNN trained and tested on entire en face 4.5 × 4.5-mm radial peripapillary capillary OCTA ONH images was compared to the performance of separate GBC models trained and tested on standard OCTA and OCT measurements. Five-fold cross-validation was used to test predictions for CNNs and GBCs. Areas under the precision recall curves (AUPRC) were calculated to control for training/test set size imbalance and were compared. RESULTS: Adjusted AUPRCs for GBC models were 0.89 (95% CI = 0.82, 0.92) for whole image vessel density GBC, 0.89 (0.83, 0.92) for whole image capillary density GBC, 0.91 (0.88, 0.93) for combined whole image vessel and whole image capillary density GBC, and 0.93 (0.91, 095) for RNFL thickness GBC. The adjusted AUPRC using CNN analysis of en face vessel density images was 0.97 (0.95, 0.99) resulting in significantly improved classification compared to GBC OCTA-based results and GBC OCT-based results (P ≤ 0.01 for all comparisons). CONCLUSION: Deep learning en face image analysis improves on feature-based GBC models for classifying healthy and glaucoma eyes.