Prediction of Visual Field Progression with Baseline and Longitudinal Structural Measurements Using Deep Learning.

PURPOSE: Identifying glaucoma patients at high risk of progression based on widely available structural data is an unmet task in clinical practice. We test the hypothesis that baseline or serial structural measures can predict visual field (VF) progression with deep learning (DL). DESIGN: Development of a DL algorithm to predict VF progression. METHODS: 3,079 eyes (1,765 patients) with various types of glaucoma and ≥5 VFs, and ≥3 years of follow-up from a tertiary academic center were included. Serial VF mean deviation (MD) rates of change were estimated with linear-regression. VF progression was defined as negative MD slope with p<0.05. A Siamese Neural Network with ResNet-152 backbone pre-trained on ImageNet was designed to predict VF progression using serial optic-disc photographs (ODP), and baseline retinal nerve fiber layer (RNFL) thickness. We tested the model on a separate dataset (427 eyes) with RNFL data from different OCT. The Main Outcome Measure was Area under ROC curve (AUC). RESULTS: Baseline average (SD) MD was 3.4 (4.9)dB. VF progression was detected in 900 eyes (29%). AUC (95% CI) for model incorporating baseline ODP and RNFL thickness was 0.813 (0.757-0.869). After adding the second and third ODPs, AUC increased to 0.860 and 0.894, respectively (p<0.027). This model also had highest AUC (0.911) for predicting fast progression (MD rate <1.0 dB/year). Model's performance was similar when applied to second dataset using RNFL data from another OCT device (AUC=0.893; 0.837-0.948). CONCLUSIONS: DL model predicted VF progression with clinically relevant accuracy using baseline RNFL thickness and serial ODPs and can be implemented as a clinical tool after further validation.

A Bayesian Hierarchical Spatial Longitudinal Model Improves Estimation of Local Macular Rates of Change in Glaucomatous Eyes.

Purpose: Demonstrate that a novel Bayesian hierarchical spatial longitudinal (HSL) model improves estimation of local macular ganglion cell complex (GCC) rates of change compared to simple linear regression (SLR) and a conditional autoregressive (CAR) model. Methods: We analyzed GCC thickness measurements within 49 macular superpixels in 111 eyes (111 patients) with four or more macular optical coherence tomography scans and two or more years of follow-up. We compared superpixel-patient-specific estimates and their posterior variances derived from the latest version of a recently developed Bayesian HSL model, CAR, and SLR. We performed a simulation study to compare the accuracy of intercept and slope estimates in individual superpixels. Results: HSL identified a significantly higher proportion of significant negative slopes in 13/49 superpixels and a significantly lower proportion of significant positive slopes in 21/49 superpixels than SLR. In the simulation study, the median (tenth, ninetieth percentile) ratio of mean squared error of SLR [CAR] over HSL for intercepts and slopes were 1.91 (1.23, 2.75) [1.51 (1.05, 2.20)] and 3.25 (1.40, 10.14) [2.36 (1.17, 5.56)], respectively. Conclusions: A novel Bayesian HSL model improves estimation accuracy of patient-specific local GCC rates of change. The proposed model is more than twice as efficient as SLR for estimating superpixel-patient slopes and identifies a higher proportion of deteriorating superpixels than SLR while minimizing false-positive detection rates. Translational Relevance: The proposed HSL model can be used to model macular structural measurements to detect individual glaucoma progression earlier and more efficiently in clinical and research settings.

A novel designed nanofibrous mat based on hydroxypropyl methyl cellulose incorporating mango peel extract for potential use in wound care system.

Skin tissue is damaged by factors such as burns, physical injuries and diseases namely diabetes. Infection and non-healing of burn wounds and lack of angiogenesis in diabetic wounds lead to extensive injuries and death. Therefore, the design of wound dressings with antibacterial and restorative capabilities is very important. In this study, nanofibers (NFs) including polyurethane (PU) and hydroxypropyl methyl cellulose (HPMC) were prepared with different ratios and Mango peel extract (MPE) loaded into NFs by electrospinning method. The morphology, chemical structure, porosity, degradation, water vapor permeability, mechanical properties, wettability, antioxidant activity and some cell studies and evaluation of their antibacterial properties were investigated. The optimal mat (PU90/HPMC10) had a defect-free morphology with homogeneous NFs. Furthermore, it showed improved biodegradability, water vapor permeability and porosity compared to other Mats. All NFs were non-toxic with hydrophilic behavior in the cellular environment and had acceptable hemocompatibility. The PU90/HPMC10/20 % optimal scaffold had significantly higher cell viability and proliferation than other samples and also had a higher antibacterial ability against pathogenic bacteria S. aureus (17 mm) and E. coli (11 mm). All these findings confirm that the produced NF mats, especially those loaded with MPE, have a high potential to be used as an effective wound dressing.

Detecting Fast Progressors: Comparing a Bayesian Longitudinal Model to Linear Regression for Detecting Structural Changes in Glaucoma.

PURPOSE: Demonstrate that a novel Bayesian hierarchical spatial longitudinal (HSL) model identifies macular superpixels with rapidly deteriorating ganglion cell complex (GCC) thickness more efficiently than simple linear regression (SLR). DESIGN: Prospective cohort study. SETTING: Tertiary Glaucoma Center. SUBJECTS: One hundred eleven eyes (111 patients) with moderate to severe glaucoma at baseline and ≥4 macular optical coherence tomography scans and ≥2 years of follow-up. OBSERVATION PROCEDURE: Superpixel-patient-specific GCC slopes and their posterior variances in 49 superpixels were derived from our latest Bayesian HSL model and Bayesian SLR. A simulation cohort was created with known intercepts, slopes, and residual variances in individual superpixels. MAIN OUTCOME MEASURES: We compared HSL and SLR in the fastest progressing deciles on (1) proportion of superpixels identified as significantly progressing in the simulation study and compared to SLR slopes in cohort data; (2) root mean square error (RMSE), and SLR/HSL RMSE ratios. RESULTS: Cohort- In the fastest decile of slopes per SLR, 77% and 80% of superpixels progressed significantly according to SLR and HSL, respectively. The SLR/HSL posterior SD ratio had a median of 1.83, with 90% of ratios favoring HSL. Simulation- HSL identified 89% significant negative slopes in the fastest progressing decile vs 64% for SLR. SLR/HSL RMSE ratio was 1.36 for the fastest decile of slopes, with 83% of RMSE ratios favoring HSL. CONCLUSION: The Bayesian HSL model improves the estimation efficiency of local GCC rates of change regardless of underlying true rates of change, particularly in fast progressors.

Efficacy of Smoothing Algorithms to Enhance Detection of Visual Field Progression in Glaucoma.

Purpose: To evaluate and compare the effectiveness of nearest neighbor (NN)- and variational autoencoder (VAE)-smoothing algorithms to reduce variability and enhance the performance of glaucoma visual field (VF) progression models. Design: Longitudinal cohort study. Subjects: 7150 eyes (4232 patients), with ≥ 5 years of follow-up and ≥ 6 visits. Methods: Vsual field thresholds were smoothed with the NN and VAE algorithms. The mean total deviation (mTD) and VF index rates, pointwise linear regression (PLR), permutation of PLR (PoPLR), and the glaucoma rate index were applied to the unsmoothed and smoothed data. Main Outcome Measures: The proportion of progressing eyes and the conversion to progression were compared between the smoothed and unsmoothed data. A simulation series of noiseless VFs with various patterns of glaucoma damage was used to evaluate the specificity of the smoothing models. Results: The mean values of age and follow-up time were 62.8 (standard deviation: 12.6) years and 10.4 (standard deviation: 4.7) years, respectively. The proportion of progression was significantly higher for the NN and VAE smoothed data compared with the unsmoothed data. VF progression occurred significantly earlier with both smoothed data compared with unsmoothed data based on mTD rates, PLR, and PoPLR methods. The ability to detect the progressing eyes was similar for the unsmoothed and smoothed data in the simulation data. Conclusions: Smoothing VF data with NN and VAE algorithms improves the signal-to-noise ratio for detection of change, results in earlier detection of VF progression, and could help monitor glaucoma progression more effectively in the clinical setting. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

An Artificial Intelligence Enabled System for Retinal Nerve Fiber Layer Thickness Damage Severity Staging.

Purpose: To develop an objective glaucoma damage severity classification system based on OCT-derived retinal nerve fiber layer (RNFL) thickness measurements. Design: Algorithm development for RNFL damage severity classification based on multicenter OCT data. Subjects and Participants: A total of 6561 circumpapillary RNFL profiles from 2269 eyes of 1171 subjects to develop models, and 2505 RNFL profiles from 1099 eyes of 900 subjects to validate models. Methods: We developed an unsupervised k-means model to identify clusters of eyes with similar RNFL thickness profiles. We annotated the clusters based on their respective global RNFL thickness. We computed the optimal global RNFL thickness thresholds that discriminated different severity levels based on Bayes' minimum error principle. We validated the proposed pipeline based on an independent validation dataset with 2505 RNFL profiles from 1099 eyes of 900 subjects. Main Outcome Measures: Accuracy, area under the receiver operating characteristic curve, and confusion matrix. Results: The k-means clustering discovered 4 clusters with 1382, 1613, 1727, and 1839 samples with mean (standard deviation) global RNFL thickness of 58.3 (8.9) µm, 78.9 (6.7) µm, 87.7 (8.2) µm, and 101.5 (7.9) µm. The Bayes' minimum error classifier identified optimal global RNFL values of > 95 µm, 86 to 95 µm, 70 to 85 µm, and < 70 µm for discriminating normal eyes and eyes at the early, moderate, and advanced stages of RNFL thickness loss, respectively. About 4% of normal eyes and 98% of eyes with advanced RNFL loss had either global, or ≥ 1 quadrant, RNFL thickness outside of normal limits provided by the OCT instrument. Conclusions: Unsupervised machine learning discovered that the optimal RNFL thresholds for separating normal eyes and eyes with early, moderate, and advanced RNFL loss were 95 µm, 85 µm, and 70 µm, respectively. This RNFL loss classification system is unbiased as there was no preassumption or human expert intervention in the development process. Additionally, it is objective, easy to use, and consistent, which may augment glaucoma research and day-to-day clinical practice. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

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.

The effect of κ-carrageenan and ursolic acid on the physicochemical properties of the electrospun nanofibrous mat for biomedical application.

Wound dressing materials such as nanofiber (NF) mats have gained a lot of attention in recent years owing to their wonderful effect on accelerating the healing process and protection of wounds. In this regard, three different types of NF mats were fabricated using pure polyvinylpyrrolidone (PVP), PVP/κ-carrageenan (KG), and ursolic acid (UA) in the optimal PVP/KG ratio by electrospinning method to apply them as wound dressings. The morphology, chemical structure, degradation, porosity, mechanical properties and antioxidant activity of the produced NFs were investigated. Moreover, cell studies (e.g., cell proliferation, adhesion, and migration) and their antibacterial properties were evaluated. Adding KG and UA reduced the mean diameter size of the PVP-based NFs to ∼98 nm in the optimal sample, with defect-free morphology. The PVP/KG/UA 0.25 % exhibited the highest porosity, hydrophilicity, and degradation rate and a wound closure rate of 60 %, 2.5 times higher than that of the control group. Furthermore, this sample's proliferation and antibacterial ability were significantly higher than the other groups. These findings confirmed that the produced UA-loaded NFs have excellent properties as wound dressing.

Comparing Rates of Change in Moderate to Advanced Glaucoma: Retinal Nerve Fiber Layer Versus Bruch Membrane Opening-Minimum Rim Width.

PURPOSE: To compare rates of change (RoC) of peripapillary retinal nerve fiber layer (RNFL) and Bruch membrane opening-based minimum rim width (BMO-MRW) thickness in moderate-to-advanced glaucoma. DESIGN: Prospective cohort study. METHODS: Longitudinal optical coherence tomography (OCT) optic nerve head volume scans of 113 eyes of 113 glaucoma patients with moderate-to-advanced or central damage were exported. This study estimated and compared global and sectoral RoC with linear mixed effects models and simple linear regression (SLR) of RNFL and BMO-MRW thickness. Permutation analyses were used to test significance of RoC in the SLR model. It also compared longitudinal signal-to-noise ratios (LSNR) defined as RoC divided by residual standard deviation (SD) between the two groups. RESULTS: Mean (SD) follow-up and median (IQR) OCT scan sessions were 5.2 (1.3) years and 10 (8-11), respectively. Baseline average (SD) visual field mean deviation was -9.2 (5.8) dB. Based on SLR, a higher proportion of significant negative RNFL RoC was observed compared to BMO-MRW in the inferotemporal (35% vs 20%; P = .015) and inferonasal (42% vs 17%; P < .001) sectors. Permutation analyses also demonstrated a higher proportion of worsening RNFL RoC than BMO-MRW in the inferotemporal (P = .026) and inferonasal (P < .001) sectors along with overall lower positive RoC. Longitudinal signal-to-noise ratios for RNFL were significantly more negative than for BMO-MRW globally, and in the inferotemporal, inferonasal, and superonasal sectors (P ≤ .01). CONCLUSIONS: Longitudinal RNFL OCT measurements are more likely to detect structural change and demonstrate better LSNR compared with BMO-MRW in eyes with central or moderate-to-advanced glaucoma damage at baseline.

Association of Rates of Ganglion Cell and Inner Plexiform Thinning With Development of Glaucoma in Eyes With Suspected Glaucoma.

Importance: In eyes with suspected glaucoma, it is clinically relevant to find diagnostic tests for the risk of development of perimetric glaucoma. Objective: To investigate the association between rates of ganglion cell/inner plexiform layer (GCIPL) and circumpapillary retinal nerve fiber layer (cpRNFL) thinning and the development of perimetric glaucoma in eyes with suspected glaucoma. Design, Setting, and Participants: This observational cohort study used data collected in December 2021 from a tertiary center study and a multicenter study. Participants with suspected glaucoma were followed up for 3.1 years. The study was designed in December 2021 and finalized in August 2022. Exposures: Development of perimetric glaucoma was defined as having 3 consecutive results showing abnormal visual fields. Using linear mixed-effect models, rates of GCIPL were compared between eyes with suspected glaucoma that did and did not develop perimetric glaucoma. A joint longitudinal multivariable survival model was used to investigate the performance of rates of GCIPL and cpRNFL thinning in predicting the risk of developing perimetric glaucoma. Main Outcomes and Measures: Rates of GCIPL thinning and hazard ratio (HR) of developing perimetric glaucoma. Results: Among a total of 462 participants, the mean (SD) age was 63.3 (11.1) years, and 275 patients (60%) were female. Of 658 eyes, 153 eyes (23%) developed perimetric glaucoma. The mean rates of GCIPL thinning were faster in eyes that developed perimetric glaucoma (-1.28 vs -0.66 µm/y for minimum GCIPL thinning; difference, -0.62; 95% CI, -1.07 to -0.16; P = .02). Based on the joint longitudinal survival model, every 1-µm/y faster rate of minimum GCIPL and rate of global cpRNFL thinning were associated with a 2.4 and 1.9 higher risk of developing perimetric glaucoma, respectively (HR, 2.4; 95% CI, 1.8 to 3.2, and HR, 1.99; 95% CI, 1.76 to 2.22, respectively; P < .001). Among the predictive factors, African American race (HR, 1.56; 95% CI, 1.05 to 2.34; P = .02), male sex (HR, 1.47; 95% CI, 1.02 to 2.15; P = .03), 1-dB higher baseline visual field pattern standard deviation (HR, 1.73; 95% CI, 1.56 to 1.91; P < .001), and 1-mm Hg higher mean intraocular pressure during follow-up (HR, 1.11; 95% CI, 1.05 to 1.17; P < .001) were associated with higher risk of developing perimetric glaucoma. Conclusions and Relevance: This study found that faster rates of GCIPL and cpRNFL thinning were associated with higher risks of developing perimetric glaucoma. Rates of cpRNFL thinning and specifically GCIPL thinning may be useful measures for monitoring eyes with suspected glaucoma.

Association of Blood Pressure With Rates of Macular Ganglion Cell Complex Thinning in Patients With Glaucoma.

Importance: There are scarce data on the association of blood pressure measures with subsequent macular structural rates of change in patients with glaucoma. Objective: To investigate the association of baseline blood pressure measures with rates of change of the macular ganglion cell complex in patients with central or moderate to advanced glaucoma damage at baseline. Design, Setting, and Participants: This prospective cohort study, conducted from August 2021 to August 2022, used data from patients in the Advanced Glaucoma Progression Study at the University of California, Los Angeles. Participants were between 39 and 80 years of age and had more than 4 macular imaging tests and 2 or more years of follow-up. Exposures: A diagnosis of glaucoma with either central damage or a visual field mean deviation worse than -6 dB. Main Outcomes and Measures: The main outcome was the association of blood pressure measures with ganglion cell complex rates of change. Macular ganglion cell complex thickness rates of change were estimated with a bayesian hierarchical model. This model included relevant demographic and clinical factors. Blood pressure measures, intraocular pressure, and their interactions were added to the model to assess the association of baseline blood pressure measures with global ganglion cell complex rates of change. Results: The cohort included 105 eyes from 105 participants. The mean (SD) age, 10-2 visual field mean deviation, and follow-up time were 66.9 (8.5) years, -8.3 (5.3) dB, and 3.6 (0.4) years, respectively, and 67 patients (63.8%) were female. The racial and ethnic makeup of the cohort was 15 African American (14.3%), 23 Asian (21.9%), 12 Hispanic (11.4%), and 55 White (52.4%) individuals based on patient self-report. In multivariable analyses, female sex, history of taking blood pressure medications, higher intraocular pressure, thicker central corneal thickness, shorter axial length, higher contrast sensitivity at 12 cycles per degree, and higher baseline 10-2 visual field mean deviation were associated with faster ganglion cell complex thinning. Lower diastolic blood pressure was associated with faster rates of ganglion cell complex thinning at higher intraocular pressures. For intraocular pressures of 8 and of 16 mm Hg (10% and 90% quantiles, respectively), every 10 mm Hg-lower increment of diastolic blood pressure was associated with 0.011 µm/y slower and -0.130 µm/y faster rates of ganglion cell complex thinning, respectively. Conclusions and Relevance: In this cohort study, a combination of lower diastolic blood pressure and higher intraocular pressure at baseline was associated with faster rates of ganglion cell complex thinning. These findings support consideration of evaluating and addressing diastolic blood pressure as a therapeutic measure in patients with glaucoma if supported by appropriate clinical trials.

Comparison of Ganglion Cell Layer and Inner Plexiform Layer Rates of Change in Suspected and Established Glaucoma.

PURPOSE: We compared ganglion cell layer (GCL) and inner plexiform layer (IPL) rates of change (RoC) in patients with glaucoma suspect (GS) and established glaucoma (EG) to test the hypothesis that IPL thickness changes would occur earlier than GCL changes in eyes with early damage. DESIGN: Prospective, cohort study. METHODS: A total of 64 GS eyes (46 patients) and 112 EG eyes (112 patients) with ≥2 years of follow-up and ≥3 macular optical coherence tomography scans were included. GCL and IPL superpixel thickness measurements were exported. A Bayesian hierarchical model with random intercepts/slopes and random residual variances was fitted to estimate RoC in individual superpixels. Normalized RoC and proportions of superpixels with significantly negative and positive GCL and IPL RoC were compared within the groups. RESULTS: The average (SD) follow-up time and number of scans were 3.5 (0.7) years and 4.2 (1.0), respectively, in the GS group and 3.6 (0.4) years and 7.3 (1.1) in the EG group. Mean (SD) normalized RoC was faster for GCL than IPL (-0.69 [0.05] vs -0.33 [0.04]) in the GS group, whereas it was faster for IPL (-0.47 [0.03] vs -0.28 [0.02]) in EG eyes. GCL RoC were significantly negative in 24 of 36 superpixels compared with 8 of 36 for IPL (P < .001) in GS eyes. In the EG group, 23 of 36 superpixels had significant negative IPL RoC compared with 13 of 36 superpixels for GCL (P = .006). CONCLUSIONS: GCL thickness is more likely to demonstrate change over time compared with IPL in glaucoma suspects. There is no evidence of preferential IPL thinning in eyes with suspected early glaucoma damage.

Association Between Rate of Ganglion Cell Complex Thinning and Rate of Central Visual Field Loss.

Importance: Whether rapid ganglion cell complex (GCC) thinning during an initial follow-up period is associated with rates of central visual field loss over time is unclear but important to understand because risk of glaucoma progression can help guide treatment intensity. Objective: To investigate the association between the rate of GCC thinning during initial follow-up and the rate of central visual field loss. Design, Setting, and Participants: This retrospective cohort study assessed patients older than 18 years with glaucoma at a tertiary glaucoma center who were followed up from June 18, 2014, to January 11, 2019. Data analysis for the current study was undertaken in March 2022. Main Outcomes and Measures: Initial rates of GCC thinning were obtained from global GCC thickness values of the first 3 optical coherence tomography (OCT) scans. Rates of central visual field loss were assessed as the change in central (10-2) visual field mean deviation during the 4.7-year follow-up period by univariable and multivariable linear mixed-effects models. Eyes were categorized as slow (>-1 µm/y) or fast (≤-1 µm/y) progressors based on rates of GCC thinning. Results: The cohort consisted of 202 eyes of 139 patients (mean [SD] age, 68.7 [10.0] years; 72 male [51.8%]); 44 African American patients (31.7%), 13 Asian patients (9.4%), 80 White patients (57.6%), and 2 patients who identified as other race and ethnicity (1.4%) were analyzed. The rate of GCC change was -0.56 µm/y (95% CI, -0.66 to -0.46 µm/y) during a mean initial follow-up of 1.8 years (95% CI, 1.7-2.0 years). A total of 163 eyes (80.7%) were slow OCT progressors, and 39 (19.3%) were fast OCT progressors, with rates of GCC thinning of -0.3 µm/y (95% CI, -0.4 to -0.2 µm/y) and -1.6 µm/y (-1.8 to -1.3 µm/y), respectively. The rates of 10-2 visual field mean deviation worsening among slow and fast OCT progressors were -0.10 dB/y (95% CI, -0.16 to 0.00 dB/y) and -0.34 dB/y (95% CI, -0.51 to -0.16 dB/y), respectively (difference, -0.26 dB/y; 95% CI, -0.45 to -0.07 dB/y; P = .008). Conclusions and Relevance: In this cohort study, rapid GCC thinning during an initial follow-up period was associated with faster rates of central visual field decline. These findings support use of longitudinal macular OCT scans assisting clinical decision-making for glaucoma and also may guide possible intensification of therapy in high-risk patients.

Vision-Related Quality of Life Among Healthy, Preperimetric Glaucoma, and Perimetric Glaucoma Patients.

PURPOSE: To investigate the association of vision-related quality of life (VRQOL) with the central visual field and macular ganglion cell complex (GCC) thickness in healthy control participants, patients with preperimetric glaucoma, and patients with perimetric glaucoma. DESIGN: Retrospective cross-sectional study. METHODS: A total of 39 healthy, 34 preperimetric glaucoma, and 145 perimetric glaucoma patients completed the 25-item National Eye Institute Visual Function Questionnaire (NEI-VFQ). A linear mixed-effect models was used to investigate the association between the glaucoma stage as measured by binocular 10-2 visual field mean sensitivity (VFMS) and GCC thickness with the Rasch-calibrated NEI-VFQ score. RESULTS: A total of 436 eyes from 218 participants (mean age = 67.2 [95% CI = 65.1 to 69.2] years) were enrolled. VRQOL calculated by the NEI-VFQ Rasch-calibrated score was worst for patients with perimetric glaucoma (50.7 [95% CI = 47.2 to 54.2]), followed by patients with preperimetric glaucoma (41.2 [95% CI = 34.5 to 47.9]) and healthy controls (29.3 [95% CI = 24.0 to 34.7]. Worse VRQOL had a moderate association with a worse global binocular 10-2 VFMS (-3.4 [95% CI = -5.0 to -1.9] dB per 1 score; P < .001; adjusted R2 = 0.27), but not with a thinner global GCC in the better eye (-0.1 [95% CI = -0.2 to 0.1] µm per 1 score; P =.0485; adjusted R2 = 0.17). CONCLUSIONS: These findings suggest that patients with perimetric and preperimetric glaucoma have worse VRQOL than patients with healthy eyes. As compared to macular thickness measurements, the central visual field is more strongly associated with VRQOL and may better help to identify patients in need of intervention.

Combining Optical Coherence Tomography and Optical Coherence Tomography Angiography Longitudinal Data for the Detection of Visual Field Progression in Glaucoma.

PURPOSE: To use longitudinal optical coherence tomography (OCT) and OCT angiography (OCTA) data to detect glaucomatous visual field (VF) progression with a supervised machine learning approach. DESIGN: Prospective cohort study. METHODS: One hundred ten eyes of patients with suspected glaucoma (33.6%) and patients with glaucoma (66.4%) with a minimum of 5 24-2 VF tests and 3 optic nerve head and macula images over an average follow-up duration of 4.1 years were included. VF progression was defined using a composite measure including either a "likely progression event" on Guided Progression Analysis, a statistically significant negative slope of VF mean deviation or VF index, or a positive pointwise linear regression event. Feature-based gradient boosting classifiers were developed using different subsets of baseline and longitudinal OCT and OCTA summary parameters. The area under the receiver operating characteristic curve (AUROC) was used to compare the classification performance of different models. RESULTS: VF progression was detected in 28 eyes (25.5%). The model with combined baseline and longitudinal OCT and OCTA parameters at the global and hemifield levels had the best classification accuracy to detect VF progression (AUROC = 0.89). Models including combined OCT and OCTA parameters had higher classification accuracy compared with those with individual subsets of OCT or OCTA features alone. Including hemifield measurements significantly improved the models' classification accuracy compared with using global measurements alone. Including longitudinal rates of change of OCT and OCTA parameters (AUROCs = 0.80-0.89) considerably increased the classification accuracy of the models with baseline measurements alone (AUROCs = 0.60-0.63). CONCLUSIONS: Longitudinal OCTA measurements complement OCT-derived structural metrics for the evaluation of functional VF loss in patients with glaucoma.

Perimetric Comparison Between the IMOvifa and Humphrey Field Analyzer.

PRCIS: IMO visual function analyzer (IMOvifa), a binocular perimeter, has similar output to the Humphrey Field Analyzer (HFA), but reduced the measurement time. PURPOSE: The purpose of this study is to evaluate the performance of IMOvifa, a perimeter that performs binocular visual field (VF) testing, and to compare its results with standard automated perimetry. METHODS: All patients underwent HFA 24-2 SITA-Fast and IMOvifa 24-2 AIZE-Rapid on the same day. Mean deviation (MD), pattern SD (PSD), foveal threshold, and visual field index (VFI) were compared between the 2 perimeters using Wilcoxon signed-rank tests, Pearson correlation, and Bland-Altman plot. Measurement time for performing VF for both eyes was also collected for each device. RESULTS: In this cross-sectional study, 138 eyes (including 25 healthy, 48 glaucoma suspects, and 65 primary open angle glaucoma) of 69 patients were evaluated. Measurement time was significantly faster for IMOvifa compared with HFA (256 vs. 419 s, P <0.001). No significant differences were seen in MD and VFI between HFA and IMOvifa (both P >0.05). Significant differences were seen in mean PSD 3.2 (2.7, 3.6) dB for HFA versus 4.1 (3.5, 4.6) for IMOvifa ( P <0.001), and foveal threshold 33.9 (33.1, 34.6) dB for HFA versus 30.6 (29.3, 31.9) dB for IMOvifa ( P <0.001). Pearson r was strong for MD ( r =0.90, P <0.001), PSD ( r =0.78, P <0.001), and VFI ( r =0.94, P <0.001). The mean difference (95% limits of agreement) was -0.1 (-3.8, 3.5) dB for MD, -0.4 (-3.4, 2.5) dB for PSD, and 0.1 (-8.9, 9.1) dB for VFI, respectively. CONCLUSIONS: IMOvifa reduced measurement time by 39%. MD, PSD, and VFI values for IMOvifa showed good agreement with HFA SITA-Fast strategy. This perimeter reduced fatigue for both patient and examiner. Additional studies are needed to determine whether it will be useful for routine VF testing.

Measurement of the Inner Macular Layers for Monitoring of Glaucoma: Confounding Effects of Age-Related Macular Degeneration.

OBJECTIVE: To investigate the confounding effect of nonexudative age-related macular degeneration (AMD), specifically drusen and outer retinal atrophy, on the architecture and automated segmentation of the inner retinal layers as measured with OCT. DESIGN: Observational cross-sectional study. SUBJECTS: Two hundred sixty-three consecutive eyes with nonexudative AMD were identified through a retrospective chart review. Exclusion criteria were a diagnosis of glaucoma or glaucoma suspect, other retinal pathology affecting the macula, axial length > 26.5 mm or spherical equivalent less than -6 diopters, any other optic nerve or neurologic disorders, or poor image quality. METHODS: Drusen were automatically segmented on macular OCT B-scans with a publicly available and validated deep learning approach. Automated segmentation of the inner plexiform layer (IPL)/inner nuclear layer (INL) boundary was carried out with the device's proprietary software. MAIN OUTCOME MEASURES: Quality of segmentation of the IPL/INL boundary as a function of drusen size and presence of inner retinal layer displacement in the area of macular pathology (drusen or atrophy). RESULTS: One hundred twenty-five eyes (65 patients) met the inclusion criteria. Drusen size varied between 16 and 272 µm (mean, 118 µm). Automated segmentation had a 22% chance of failure if the drusen height was between 145 and 185 µm and was most likely to fail with drusen heights above 185 µm. When drusen height was normalized by total retinal thickness, segmentation failed 36% of the time when the drusen to total retinal thickness ratio was 0.45 or above. Images were likely to show displacement of inner retinal layers with drusen heights above 176 µm and a normalized drusen height ratio of 0.5 or higher. Eighty-seven percent of images with outer retinal atrophy displayed incorrect segmentation. CONCLUSIONS: Outer retinal diseases can alter the retinal topography and affect the segmentation accuracy of the inner retinal layers. Large drusen may cause segmentation error and compression of the inner macular layers. Geographic atrophy confounds automated segmentation in a high proportion of eyes. Clinicians should be cognizant of the effects of outer retinal disease on the inner retinal layer measurements when interpreting the results of macular OCT imaging in patients with glaucoma.

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.

Factors associated with choroidal microvascular dropout change.

BACKGROUND/AIMS: To investigate the factors associated with choroidal microvasculature drop-out (MvD) enlargement detected by optical coherence tomography angiography (OCT-A) in glaucomatous eyes. METHODS: Ninety-one eyes of 68 primary open-angle glaucoma patients were enrolled. Only eyes with a minimum of four good quality OCT-A and OCT scans of the optic nerve head acquired at least and with a minimum of 2 years follow-up were included. Area and angular circumference of MvD were analysed on en face images. Univariable and multivariable mixed effects models were constructed to identify the factors contributing to MvD area and angular circumference change over time. RESULTS: Peripapillary MvD was detected in 53 (58.2%) eyes at baseline and in an additional 17 (18.6%) eyes during follow-up, whereas MvD was not detected in 21 (23.0 %) eyes during the entire follow-up period. In multivariable analysis, worse baseline visual field (VF) mean deviation (MD) (ß=0.27, 95% CI 0.10 to 0.44, p=0.002), greater intraocular pressure (IOP) fluctuations (ß=0.86, 95% CI 0.24 to 1.48, p=0.007), higher peak IOP (ß=0.17, 95% CI -0.01 to 0.35, p=0.067) and greater number of IOP lowering medications (ß=1.36, 95% CI 0.67 to 2.05, p<0.001) were associated with faster MvD area enlargement. Worse baseline VF MD and greater IOP fluctuation were also associated with significantly faster MvD circumferential enlargement in multivariable models. CONCLUSION: Greater IOP fluctuation, higher peak IOP, worse baseline VF MD and greater number of glaucoma medications were significantly associated with MvD enlargement in glaucomatous eyes. The identification of factors associated with MvD enlargement may improve our understanding of the role of choroidal vasculature in glaucoma.