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.

OCT Optic Nerve Head Morphology in Myopia IV: Neural Canal Scleral Flange Remodeling in Highly Myopic Eyes.

PURPOSE: To compare the prevalence, location and magnitude of optic nerve head (ONH) OCT-detected, exposed neural canal (ENC), externally oblique choroidal border tissue (EOCBT) and exposed scleral flange (ESF) regions in 122 highly myopic (Hi-Myo) versus 362 nonhighly myopic healthy (Non-Hi-Myo-Healthy) eyes. DESIGN: Cross-sectional study. METHODS: After OCT radial B-scan, ONH imaging, Bruch's membrane opening (BMO), the anterior scleral canal opening (ASCO), and the scleral flange opening (SFO) were manually segmented in each B-scan and projected to BMO reference plane. The direction and magnitude of BMO/ASCO offset and BMO/SFO offset as well as the location and magnitude of ENC, EOCBT and ESF regions, perineural canal (pNC) retinal nerve fiber layer thickness (RNFLT) and pNC choroidal thickness (CT) were calculated within 30° sectors relative to the Foveal-BMO (FoBMO) axis. Hi-ESF eyes were defined to be those with an ESF region ≥100 µms in at least 1 sector. RESULTS: Hi-Myo eyes more frequently demonstrated Hi-ESF regions (87/122) than Non-Hi-myo-Healthy eyes (73/362) and contained significantly larger ENC, EOCBT, and ESF regions (P < .001) which were greatest in magnitude and prevalence within the inferior-temporal FoBMO sectors where Hi-Myo pNC-RNFLT and pNCCT were thinnest. BMO/ASCO offset and the BMO/SFO offset were both significantly increased (P < .001) in the Hi-Myo eyes, with the latter demonstrating a greater increase. CONCLUSIONS: ENC region tissue remodeling that includes the scleral flange is enhanced in Hi-Myo compared to Non-Hi-Myo-Healthy eyes. Longitudinal studies are necessary to determine whether the presence of an ENC region influences ONH susceptibility to aging and/or glaucoma.

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.

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.

Central macular OCTA parameters in glaucoma.

BACKGROUND/AIMS: To investigate the relationship between the foveal avascular zone (FAZ) parameters assessed by optical coherence tomography angiography (OCTA) and central visual field parameters in glaucoma and healthy subjects. METHODS: One hundred and eighty-eight subjects (248 eyes), including 24 healthy (38 eyes), 37 glaucoma suspect (42 eyes, and 127 primary open angle glaucoma (POAG) patients (168 eyes), underwent imaging using OCTA and standard automated perimetry using the 24-2 and 10-2 Swedish Interactive Thresholding Algorithm. OCTA-based and OCT-based FAZ parameters (superficial FAZ area, FAZ circumference), foveal vessel density (FD300) and foveal thickness were measured. The correlation between FAZ parameters and visual field parameters was assessed using linear mixed model. RESULTS: Axial length adjusted-FAZ area was not different among the three groups (mean (95% CI)): in healthy 0.31 (0.27 to 0.36) mm2, glaucoma suspect 0.29 (0.26 to 0.31) mm2 and POAG eyes 0.28 (0.27 to 0.30) mm2 (p=0.578). FD300 was lower in glaucoma suspect 49.1% (47.9% to 50.4%) and POAG eyes 48.7% (48.1% to 49.4%) than healthy eyes 50.5% (49.3% to 51.7%) though the difference was not statistically significant (p=0.071). Lower FD300 was associated with worse 24-2 and 10-2 visual field mean deviation and foveal threshold in multivariable linear mixed models (all p<0.05). In addition, a smaller FAZ area was associated with lower intraocular pressure (IOP) (p=0.026). CONCLUSIONS: The FD300, but not the FAZ area was correlated with 10° central visual field mean deviation and foveal threshold in healthy, glaucoma suspect and POAG eyes. In contrast, a smaller FAZ area was associated with lower IOP.

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.

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.

Intraocular Pressure Measurement in Childhood Glaucoma under Standardized General Anaesthesia: The Prospective EyeBIS Study.

Objective: We aimed to compare intraocular pressure (IOP) measurements using iCare® PRO rebound tonometry (iCare) and Perkins applanation tonometry (Perkins) in childhood glaucoma subjects and healthy children and the influence of anaesthesia depth, age and corneal thickness. Material: Prospective clinical, case-control study of children who underwent an ophthalmologic examination under general anaesthesia according to our protocol. Children were 45.45 ± 29.76 months old (mean ± SD (standard deviation)). Of all children, 54.05% were female. IOP was taken three times (T1−T3), according to duration and the depth of anaesthesia. The order of measurement alternated, starting with iCare. Agreement between the device measurements was evaluated using Bland−Altman analysis. Results: 53 glaucoma subjects and 22 healthy controls. Glaucoma subjects: IOP measured with iCare was at T1: 27.2 (18.1−33.8), T2: 21.6 (14.8−30.6), T3: 20.4 mmHg (14.5−27.0) and Perkins 17.5 (12.0−23.0), 15.5 (10.5−20.5), 15.0 mmHg (10.5−21.0) (median ± IQR (interquartile range)). Healthy controls: IOP with iCare: T1: 13.3 (11.1−17.0), T2: 10.6 (8.1−12.4), T3: 9.6 mmHg (7.7−11.7) and Perkins 10.3 (8.0−12.0), 7.0 (5.5−10.5), 7.0 mmHg (5.5−8.5) (median ± IQR). The median IOP was statistically significantly higher with iCare than with Perkins (p < 0.001) in both groups. The mean difference (iCare and Perkins) was 6.0 ± 6.1 mmHg for T1−T3, 7.3 at T1, 6.0 at T2, 4.9 mmHg at T3. Conclusion: The IOP was the highest in glaucoma subjects and healthy children at T1 (under sedation), independently of the measurement method. iCare always leads to higher IOP compared to Perkins in glaucoma and healthy subjects, regardless of the duration of anesthesia.

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.

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.

Measurements of OCT Angiography Complement OCT for Diagnosing Early Primary Open-Angle Glaucoma.

PURPOSE: To compare measurements of global and regional circumpapillary capillary density (cpCD) with retinal nerve fiber layer (RNFL) thickness and characterize their relationship with visual function in early primary open-angle glaucoma (POAG). DESIGN: Cross-sectional study. PARTICIPANTS: Eighty healthy eyes, 64 preperimetric eyes, and 184 mild POAG eyes from the Diagnostic Innovations in Glaucoma Study. METHODS: Global and regional RNFL thickness and cpCD measurements were obtained using OCT and OCT angiography (OCTA). For direct comparison at the individual and diagnostic group level, RNFL thickness and capillary density values were converted to a normalized relative loss scale. MAIN OUTCOME MEASURES: Retinal nerve fiber layer thickness and cpCD normalized loss at the individual level and diagnostic group. Global and regional areas under the receiver operating characteristic curve (AUROC) for RNFL thickness and cpCD to detect preperimetric glaucoma and glaucoma, R2 for the strength of associations between RNFL thickness function and capillary density function in diagnostic groups. RESULTS: Both global and regional RNFL thickness and cpCD decreased progressively with increasing glaucoma severity (P < 0.05, except for temporal RNFL thickness). Global and regional cpCD relative loss values were higher than those of RNFL thickness (P < 0.05) in preperimetric glaucoma (except for the superonasal region) and glaucoma (except for the inferonasal and superonasal regions) eyes. Race, intraocular pressure (IOP), and cpCD were associated with greater cpCD than RNFL thickness loss in early glaucoma at the individual level (P < 0.05). Global measurements of capillary density (whole image capillary density and cpCD) had higher diagnostic accuracies than RNFL thickness in detecting preperimetric glaucoma and glaucoma (P < 0.05; except for cpCD/RNFL thickness comparison in glaucoma [P = 0.059]). Visual function was significantly associated with RNFL thickness and cpCD globally and in all regions (P < 0.05, except for temporal RNFL thickness-function association [P = 0.070]). CONCLUSIONS: Associations between capillary density and visual function were found in the regions known to be at highest risk for damage in preperimetric glaucoma eyes and all regions of mild glaucoma eyes. In early glaucoma, capillary density loss was more pronounced than RNFL thickness loss. Individual characteristics influence the relative magnitudes of capillary density loss compared with RNFL thickness loss. Retinal nerve fiber layer thickness and microvascular assessments are complementary and yield valuable information for the detection of early damages seen in POAG.

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.

Bruch Membrane Opening Detection Accuracy in Healthy Eyes and Eyes With Glaucoma With and Without Axial High Myopia in an American and Korean Cohort.

PURPOSE: To determine the predictors of Bruch membrane opening (BMO) location accuracy and the visibility of the BMO location in glaucoma and healthy individuals with and without axial high myopia. DESIGN: Cross-sectional study. METHODS: Healthy eyes and eyes with glaucoma from an American study and a Korean clinic population were classified into 2 groups: those with no axial high myopia (axial length [AL] <26 mm) and those with axial high myopia (AL ≥26 mm). The accuracy of the automated BMO location on optic nerve head Spectralis optical coherence tomography radial scans was assessed by expert reviewers. RESULTS: Four hundred thirty-eight non-highly myopic eyes (263 subjects) and 113 highly myopic eyes (81 subjects) were included. In healthy eyes with and without axial high myopia, 9.1% and 1.7% had indiscernible BMOs while 54.5% and 87.6% were accurately segmented, respectively. More than a third (36.4%) and 10.7% of eyes with indiscernible BMOs were manually correctable (respectively, P = .017). In eyes with glaucoma with and without high myopia, 15.0% and 3.2% had indiscernible BMOs, 55.0% and 38.2% were manually corrected, and 30.0% and 58.7% were accurately segmented without the need for manual correction (respectively, P = .005). Having axial high myopia, a larger AL, a larger BMO tilt angle, a lower BMO ovality index (more oval), and a glaucoma diagnosis were significant predictors of BMO location inaccuracy in multivariable logistic regression analysis. CONCLUSIONS: As BMO location inaccuracy was 2.4 times more likely in eyes with high axial myopia regardless of diagnosis, optical coherence tomography images of high myopes should be reviewed carefully, and when possible, BMO location should be corrected before using optic nerve head scan results for the clinical management of glaucoma.

The influence of axial myopia on optic disc characteristics of glaucoma eyes.

This study characterizes differences in glaucomatous eyes with and without high axial myopia using custom automated analysis of OCT images. 452 eyes of 277 glaucoma patients were stratified into non (n = 145 eyes), mild (n = 214 eyes), and high axial myopia (axial length (AL) > 26 mm, n = 93 eyes). Optic disc ovality index, tilt and rotation angle of Bruch´s membrane opening (BMO) and peripapillary choroidal thickness (PCT) were calculated using automated and deep learning strategies. High myopic optic discs were more oval and had larger BMO tilt than mild and non-myopic discs (both p < 0.001). Mean PCT was thinnest in high myopic eyes followed by mild and non-myopic eyes (p < 0.001). BMO rotation angle, global retinal nerve fiber layer (RNFL) thickness and BMO-minimum rim width (MRW) were similar among groups. Temporal RNFL was thicker and supranasal BMO-MRW was thinner in high myopic eyes. BMO tilt and PCT showed moderate and temporal RNFL and nasal BMO-MRW showed weak but significant associations with AL in multivariable analyses (all p < 0.05). Large BMO tilt angle and thin PCT are characteristics of highly myopic discs and were not associated with severity of glaucoma. Caution should be exercised when using sectoral BMO-MRW and RNFL thickness for glaucoma management decisions in myopic eyes.

Superficial and Deep Macula Vessel Density in Healthy, Glaucoma Suspect, and Glaucoma Eyes.

PRECIS: Macular superficial capillary plexus (SCP) vessel density is more informative than deep capillary plexus (DCP) vessel density for the detection of glaucoma. PURPOSE: The purpose of this study was to characterize optical coherence tomography angiography macular SCP and projection-resolved DCP vessel densities and compare their diagnostic accuracies with ganglion cell complex (GCC) thickness in healthy, glaucoma suspect, and glaucoma eyes. MATERIALS AND METHODS: Sixty-eight eyes of 44 healthy subjects, 26 eyes of 16 preperimetric glaucoma suspects, and 161 eyes of 124 glaucoma patients from the Diagnostics Innovations in Glaucoma Study with good quality high-density 6×6 mm2 macula optical coherence tomography angiography images were included. The diagnostic accuracy of SCP vessel density, projection-resolved DCP vessel density and GCC thickness were compared among groups. RESULTS: Mean whole image vessel density (wiVD; % of area occupied by vessels containing flowing blood) in the SCP layer was highest in healthy eyes (49.7%), followed by glaucoma suspect eyes (46.0%), and glaucoma eyes (40.9%) (P<0.001). Mean wiVD in the DCP layer was similar in healthy (50.6%), glaucoma suspect (47.3%), and glaucoma eyes (45.7%) (P=0.925). Diagnostic accuracy of both GCC thickness and SCP wiVD was significantly higher than DCP wiVD for classifying healthy and glaucoma eyes [adjusted area under the receiver operating characteristic curve (95% confidence interval): GCC=0.86 (0.72, 0.94), SCP=0.80 (0.66, 0.91) and DCP=0.44 (0.30, 0.57)] (P<0.001). CONCLUSIONS: SCP vessel densities have better diagnostic accuracy for detecting glaucoma than DCP vessel densities. Although the diagnostic accuracy of the macula SCP is relatively modest, it is more informative than the DCP.

Deep Learning Estimation of 10-2 and 24-2 Visual Field Metrics Based on Thickness Maps from Macula OCT.

PURPOSE: To develop deep learning (DL) systems estimating visual function from macula-centered spectral-domain (SD) OCT images. DESIGN: Evaluation of a diagnostic technology. PARTICIPANTS: A total of 2408 10-2 visual field (VF) SD OCT pairs and 2999 24-2 VF SD OCT pairs collected from 645 healthy and glaucoma subjects (1222 eyes). METHODS: Deep learning models were trained on thickness maps from Spectralis macula SD OCT to estimate 10-2 and 24-2 VF mean deviation (MD) and pattern standard deviation (PSD). Individual and combined DL models were trained using thickness data from 6 layers (retinal nerve fiber layer [RNFL], ganglion cell layer [GCL], inner plexiform layer [IPL], ganglion cell-IPL [GCIPL], ganglion cell complex [GCC] and retina). Linear regression of mean layer thicknesses were used for comparison. MAIN OUTCOME MEASURES: Deep learning models were evaluated using R2 and mean absolute error (MAE) compared with 10-2 and 24-2 VF measurements. RESULTS: Combined DL models estimating 10-2 achieved R2 of 0.82 (95% confidence interval [CI], 0.68-0.89) for MD and 0.69 (95% CI, 0.55-0.81) for PSD and MAEs of 1.9 dB (95% CI, 1.6-2.4 dB) for MD and 1.5 dB (95% CI, 1.2-1.9 dB) for PSD. This was significantly better than mean thickness estimates for 10-2 MD (0.61 [95% CI, 0.47-0.71] and 3.0 dB [95% CI, 2.5-3.5 dB]) and 10-2 PSD (0.46 [95% CI, 0.31-0.60] and 2.3 dB [95% CI, 1.8-2.7 dB]). Combined DL models estimating 24-2 achieved R2 of 0.79 (95% CI, 0.72-0.84) for MD and 0.68 (95% CI, 0.53-0.79) for PSD and MAEs of 2.1 dB (95% CI, 1.8-2.5 dB) for MD and 1.5 dB (95% CI, 1.3-1.9 dB) for PSD. This was significantly better than mean thickness estimates for 24-2 MD (0.41 [95% CI, 0.26-0.57] and 3.4 dB [95% CI, 2.7-4.5 dB]) and 24-2 PSD (0.38 [95% CI, 0.20-0.57] and 2.4 dB [95% CI, 2.0-2.8 dB]). The GCIPL (R2 = 0.79) and GCC (R2 = 0.75) had the highest performance estimating 10-2 and 24-2 MD, respectively. CONCLUSIONS: Deep learning models improved estimates of functional loss from SD OCT imaging. Accurate estimates can help clinicians to individualize VF testing to patients.

OCT Angiography Artifacts in Glaucoma.

PURPOSE: To determine the prevalence of different types of artifacts seen in OCT angiography (OCTA) images of healthy and glaucoma eyes and evaluate the characteristics associated with poor-quality images. DESIGN: Retrospective study. PARTICIPANTS: A total of 649 eyes of 368 healthy, glaucoma suspect, and glaucoma patients. METHODS: Angiovue (Optovue Inc) high-density (HD) and non-HD optic nerve head and macula OCTA images of participants were evaluated by 4 expert reviewers for the presence of different artifacts, including eye movement, defocus, shadow, decentration, segmentation error, blink, and Z offset in the superficial vascular layer. Each OCTA scan was designated to have good or poor quality based on the presence of artifacts. The association of demographic and ocular characteristics with the likelihood of obtaining poor-quality OCTA images was evaluated. MAIN OUTCOME MEASURES: The prevalence of OCTA artifacts and the factors associated with increased likelihood of capturing poor-quality OCTA images. RESULTS: A total of 5263 OCTA images were evaluated. Overall, 33.9% of the OCTA images had poor quality. The majority of images with acceptable quality scores (QS ≥ 4) had no artifacts (76.6%). Other images had 1 (13.6%) or 2 or more artifacts (9.8%). Older age (P < 0.001), male gender (P = 0.045), worse visual field mean deviation (P < 0.001), absence of eye tracking (P < 0.001), and macular scan area (P < 0.001) were associated with a higher likelihood of obtaining poor-quality images. In images with acceptable QS, the commercially available quality measures including QS and signal strength index had the area under the receiver operating characteristic curves of 0.65 (95% confidence interval [CI], 0.62-0.69) and 0.70 (95% CI, 0.68-0.73) to detect good-quality images, respectively. CONCLUSIONS: OCTA artifacts associated with poor-quality images are frequent, and their prevalence is affected by ocular and patient characteristics. One should not rely solely on the quantitative assessments that are provided automatically by OCTA instruments. A systematic scan review should be conducted to ensure appropriate interpretation of OCTA images. Given the high prevalence of poor-quality OCTA images, the images should be reacquired whenever an apparent and correctable artifact is present on a captured image.

Effects of Study Population, Labeling and Training on Glaucoma Detection Using Deep Learning Algorithms.

Purpose: To compare performance of independently developed deep learning algorithms for detecting glaucoma from fundus photographs and to evaluate strategies for incorporating new data into models. Methods: Two fundus photograph datasets from the Diagnostic Innovations in Glaucoma Study/African Descent and Glaucoma Evaluation Study and Matsue Red Cross Hospital were used to independently develop deep learning algorithms for detection of glaucoma at the University of California, San Diego, and the University of Tokyo. We compared three versions of the University of California, San Diego, and University of Tokyo models: original (no retraining), sequential (retraining only on new data), and combined (training on combined data). Independent datasets were used to test the algorithms. Results: The original University of California, San Diego and University of Tokyo models performed similarly (area under the receiver operating characteristic curve = 0.96 and 0.97, respectively) for detection of glaucoma in the Matsue Red Cross Hospital dataset, but not the Diagnostic Innovations in Glaucoma Study/African Descent and Glaucoma Evaluation Study data (0.79 and 0.92; P < .001), respectively. Model performance was higher when classifying moderate-to-severe compared with mild disease (area under the receiver operating characteristic curve = 0.98 and 0.91; P < .001), respectively. Models trained with the combined strategy generally had better performance across all datasets than the original strategy. Conclusions: Deep learning glaucoma detection can achieve high accuracy across diverse datasets with appropriate training strategies. Because model performance was influenced by the severity of disease, labeling, training strategies, and population characteristics, reporting accuracy stratified by relevant covariates is important for cross study comparisons. Translational Relevance: High sensitivity and specificity of deep learning algorithms for moderate-to-severe glaucoma across diverse populations suggest a role for artificial intelligence in the detection of glaucoma in primary care.