Enhancing the structure-function relationship in glaucoma using anatomical compensation of retinal nerve fibre layer.

BACKGROUND/AIMS: To investigate whether compensating retinal nerve fibre layer (RNFL) thickness measurements for demographic and anatomical ocular factors can strengthen the structure-function relationship in patients with glaucoma. METHODS: 600 eyes from 412 patients with glaucoma (mean deviation of the visual field (MD VF) -6.53±5.55 dB) were included in this cross-sectional study. Participants underwent standard automated perimetry and spectral-domain optical coherence tomography imaging (Cirrus; Carl Zeiss Meditec). Compensated RNFL thickness was computed considering age, refractive error, optic disc parameters and retinal vessel density. The relationship between MD VF and RNFL thickness measurements, with or without demographic and anatomical compensation, was evaluated sectorally and focally. RESULTS: The superior arcuate sector exhibited the highest correlation between measured RNFL and MD VF, with a correlation of 0.49 (95% CI 0.37 to 0.59). Applying the compensated RNFL data increased the correlation substantially to 0.62 (95% CI 0.52 to 0.70; p<0.001). Only 61% of the VF locations showed a significant relationship (Spearman's correlation of at least 0.30) between structural and functional aspects using measured RNFL data, and this increased to 78% with compensated RNFL measurements. In the 10°-20° VF region, the slope below the breakpoint for compensated RNFL thickness demonstrated a more robust correlation (slope=1.66±0.18 µm/dB; p<0.001) than measured RNFL (slope=0.27±0.67 µm/dB; p=0.688). CONCLUSION: Compensated RNFL data improve the correlation between RNFL measurements and VF parameters. This indicates that creating structure-to-function maps that consider anatomical variances may aid in identifying localised structural and functional loss in glaucoma.

Replacing the postoperative week 1 visit after routine phacoemulsification with a telephone consult.

OBJECTIVE: To assess the safety of replacing the postoperative week 1 (POW1) clinic visit with a nurse-conducted telephone call. DESIGN: Retrospective observational study that included cases from January 2019 to June 2021. PARTICIPANTS: Patients who had undergone uncomplicated phacoemulsification surgery with an unremarkable postoperative day 1 (POD1) examination. METHODS: All patients were seen in clinic on POD1 by an ophthalmologist. They then had a telephone conversation with a nurse at POW1 and subsequently an in-person postoperative month 1 (POM1) clinic consultation with an ophthalmologist. Main outcome measure was the incidence of unexpected management changes related to cataract surgery within POM1. Data also were collected on the reasons for unscheduled patient-initiated visits, additional procedures or medications, and postoperative visual acuity worse than 6/12 at POM1. RESULTS: Of the 20,475 patients, 541 patients (2.64%) had an unexpected management change within POM1. There were 565 patients (2.76%) who had self-initiated unscheduled visits between POD1 to POM1. There were 23 patients (0.11%) who required additional surgery within POM1 and 1 patient (0.005%) with endophthalmitis. The most common indication for additional surgical procedures was retained lens material (7 patients, 30.43%). Visual acuity was worse than 6/12 in 1,199 patients (6.22%), with the most common causes attributed to preexisting ocular conditions. CONCLUSIONS: These results suggest that replacing the POW1 visit with a nurse-conducted telephone consult for patients who have undergone uncomplicated phacoemulsification surgery and had a normal POD1 consultation is safe.

Efficiency, Precision, Validity, and Reliability of GlauCAT-Asian Computerized Adaptive Tests in Measuring Glaucoma-Related Quality of Life.

Purpose: To determine the efficiency, precision, and agreement of GlauCAT-Asian and its corresponding validity and reliability. Methods: In this cross-sectional study, 219 participants (mean ± standard deviation age, 66.59 ± 8.61 years; 34% female) across the spectrum of glaucoma severity and 50 glaucoma suspects were recruited from glaucoma clinics in Singapore. Participants answered seven computerized adaptive testing (CAT) evaluations (Ocular Comfort, Activity Limitation, Lighting, Mobility, Concerns, Psychosocial, Glaucoma Management) and underwent eye examinations. Efficiency (mean number of items required for each CAT and time taken for CAT versus full item banks [IBs]), agreement (concordance between CATs and full IB person measures, henceforth referred to as scores), and precision (standard error of measurement [SE]) were evaluated. Other validity and reliability metrics were also assessed. Results: The mean number of items administered ranged from 9 (Mobility/Glaucoma Management) to 12 (Ocular Comfort). Compared to answering the full IBs, CATs provided an average time saving of 38.3% (range, 10% to 70.6% for Lighting and Activity Limitation, respectively). Agreement between scores obtained by CAT versus full IB was high (intracorrelation coefficient ≥0.75), as was precision of score estimates (mean SE range: 0.35 for Psychosocial to 0.29 for Mobility). Scores from Activity Limitation, Mobility, Lighting, and Concerns decreased significantly as glaucoma severity increased (criterion validity; P-trend <0.05). All tests displayed good convergent/divergent validity and test-retest reliability. Conclusions: GlauCAT-Asian provides efficient, precise, accurate, valid, and reliable measurement of the patient-centered impact of glaucoma. Translational Relevance: GlauCAT-Asian may provide a valuable clinical tool for ophthalmologists to monitor impact of disease progression and the effectiveness of therapies.

AI-based clinical assessment of optic nerve head robustness superseding biomechanical testing.

BACKGROUND/AIMS: To use artificial intelligence (AI) to: (1) exploit biomechanical knowledge of the optic nerve head (ONH) from a relatively large population; (2) assess ONH robustness (ie, sensitivity of the ONH to changes in intraocular pressure (IOP)) from a single optical coherence tomography (OCT) volume scan of the ONH without the need for biomechanical testing and (3) identify what critical three-dimensional (3D) structural features dictate ONH robustness. METHODS: 316 subjects had their ONHs imaged with OCT before and after acute IOP elevation through ophthalmo-dynamometry. IOP-induced lamina cribrosa (LC) deformations were then mapped in 3D and used to classify ONHs. Those with an average effective LC strain superior to 4% were considered fragile, while those with a strain inferior to 4% robust. Learning from these data, we compared three AI algorithms to predict ONH robustness strictly from a baseline (undeformed) OCT volume: (1) a random forest classifier; (2) an autoencoder and (3) a dynamic graph convolutional neural network (DGCNN). The latter algorithm also allowed us to identify what critical 3D structural features make a given ONH robust. RESULTS: All three methods were able to predict ONH robustness from a single OCT volume scan alone and without the need to perform biomechanical testing. The DGCNN (area under the curve (AUC): 0.76±0.08) outperformed the autoencoder (AUC: 0.72±0.09) and the random forest classifier (AUC: 0.69±0.05). Interestingly, to assess ONH robustness, the DGCNN mainly used information from the scleral canal and the LC insertion sites. CONCLUSIONS: We propose an AI-driven approach that can assess the robustness of a given ONH solely from a single OCT volume scan of the ONH, and without the need to perform biomechanical testing. Longitudinal studies should establish whether ONH robustness could help us identify fast visual field loss progressors. PRECIS: Using geometric deep learning, we can assess optic nerve head robustness (ie, sensitivity to a change in IOP) from a standard OCT scan that might help to identify fast visual field loss progressors.

Segregation of neuronal and vascular retinal damage in patients with hypertension and diabetes.

This study aimed to examine the impact of diabetes and hypertension on retinal nerve fiber layer (RNFL) thickness components. Optical coherence tomography (OCT) measurements do not consider blood vessel contribution, which this study addressed. We hypothesized that diabetes and/or hypertension would lead to thinner RNFL versus controls due to the vascular component. OCT angiography was used to measure the RNFL in 121 controls, 50 diabetes patients, 371 hypertension patients, and 177 diabetes patients with hypertension. A novel technique separated the RNFL thickness into original (vascular component) and corrected (no vascular component) measurements. Diabetes-only (98 ± 1.7 µm; p = 0.002) and diabetes with hypertension (99 ± 0.8 µm; p = 0.001) patients had thinner original RNFL versus controls (102 ± 0.8 µm). No difference was seen between hypertension-only patients (101 ± 0.5 µm; p = 0.083) and controls. After removing the blood vessel component, diabetes/hypertension groups had thinner corrected RNFL versus controls (p = 0.024). Discrepancies in diabetes/hypertension patients were due to thicker retinal blood vessels within the RNFL thickness (p = 0.002). Our findings suggest that diabetes and/or hypertension independently contribute to neurodegenerative thinning of the RNFL, even in the absence of retinopathy. The differentiation of neuronal and vascular components in RNFL thickness measurements provided by the novel technique highlights the importance of considering vascular changes in individuals with these conditions.

Remote Perimetry in a Virtual Reality Metaverse Environment for Out-of-Hospital Functional Eye Screening Compared Against the Gold Standard Humphrey Visual Fields Perimeter: Proof-of-Concept Pilot Study.

BACKGROUND: The growing global burden of visual impairment necessitates better population eye screening for early detection of eye diseases. However, accessibility to testing is often limited and centralized at in-hospital settings. Furthermore, many eye screening programs were disrupted by the COVID-19 pandemic, presenting an urgent need for out-of-hospital solutions. OBJECTIVE: This study investigates the performance of a novel remote perimetry application designed in a virtual reality metaverse environment to enable functional testing in community-based and primary care settings. METHODS: This was a prospective observational study investigating the performance of a novel remote perimetry solution in comparison with the gold standard Humphrey visual field (HVF) perimeter. Subjects received a comprehensive ophthalmologic assessment, HVF perimetry, and remote perimetry testing. The primary outcome measure was the agreement in the classification of overall perimetry result normality by the HVF (Swedish interactive threshold algorithm-fast) and testing with the novel algorithm. Secondary outcome measures included concordance of individual testing points and perimetry topographic maps. RESULTS: We recruited 10 subjects with an average age of 59.6 (range 28-81) years. Of these, 7 (70%) were male and 3 (30%) were female. The agreement in the classification of overall perimetry results was high (9/10, 90%). The pointwise concordance in the automated classification of individual test points was 83.3% (8.2%; range 75%-100%). In addition, there was good perimetry topographic concordance with the HVF in all subjects. CONCLUSIONS: Remote perimetry in a metaverse environment had good concordance with gold standard perimetry using the HVF and could potentially avail functional eye screening in out-of-hospital settings.

Evaluating the outcome of screening for glaucoma using colour fundus photography-based referral criteria in a teleophthalmology screening programme for diabetic retinopathy.

AIMS: To evaluate the effectiveness of glaucoma screening using glaucoma suspect (GS) referral criteria assessed on colour fundus photographs in Singapore's Integrated Diabetic Retinopathy Programme (SiDRP). METHODS: A case-control study. This study included diabetic subjects who were referred from SiDRP with and without GS between January 2017 and December 2018 and reviewed at Singapore National Eye Centre. The GS referral criteria were based on the presence of a vertical cup-to-disc ratio (VCDR) of ≥0.65 and other GS features. The final glaucoma diagnosis confirmed from electronic medical records was retrospectively matched with GS status. The sensitivity, specificity and positive predictive value (PPV) of the test were evaluated. RESULTS: Of 5023 patients (2625 with GS and 2398 without GS) reviewed for glaucoma, 451 (9.0%, 95% CI 8.2% to 9.8%) were confirmed as glaucoma. The average follow-up time was 21.5±10.2 months. Using our current GS referral criteria, the sensitivity, specificity and PPV were 81.6% (95% CI 77.7% to 85.1%), 50.6% (95% CI 49.2% to 52.1%) and 14.0% (95% CI 13.4% to 14.7%), respectively, resulting in 2257 false positive cases. Increasing the VCDR cut-off for referral to ≥0.80, the specificity increased to 93.9% (95% CI 93.1% to 94.5%) but the sensitivity decreased to 11.3% (95% CI 8.5% to 14.6%), with a PPV of 15.4% (95% CI 12.0% to 19.4%). CONCLUSIONS: Opportunistic screening for glaucoma in a lower VCDR group could result in a high number of unnecessary referrals. If healthcare infrastructures are limited, targeting case findings on a larger VCDR group with high specificity will still be beneficial.

Three-Dimensional Structural Phenotype of the Optic Nerve Head as a Function of Glaucoma Severity.

Importance: The 3-dimensional (3-D) structural phenotype of glaucoma as a function of severity was thoroughly described and analyzed, enhancing understanding of its intricate pathology beyond current clinical knowledge. Objective: To describe the 3-D structural differences in both connective and neural tissues of the optic nerve head (ONH) between different glaucoma stages using traditional and artificial intelligence-driven approaches. Design, Setting, and Participants: This cross-sectional, clinic-based study recruited 541 Chinese individuals receiving standard clinical care at Singapore National Eye Centre, Singapore, and 112 White participants of a prospective observational study at Vilnius University Hospital Santaros Klinikos, Vilnius, Lithuania. The study was conducted from May 2022 to January 2023. All participants had their ONH imaged using spectral-domain optical coherence tomography and had their visual field assessed by standard automated perimetry. Main Outcomes and Measures: (1) Clinician-defined 3-D structural parameters of the ONH and (2) 3-D structural landmarks identified by geometric deep learning that differentiated ONHs among 4 groups: no glaucoma, mild glaucoma (mean deviation [MD], ≥-6.00 dB), moderate glaucoma (MD, -6.01 to -12.00 dB), and advanced glaucoma (MD, <-12.00 dB). Results: Study participants included 213 individuals without glaucoma (mean age, 63.4 years; 95% CI, 62.5-64.3 years; 126 females [59.2%]; 213 Chinese [100%] and 0 White individuals), 204 with mild glaucoma (mean age, 66.9 years; 95% CI, 66.0-67.8 years; 91 females [44.6%]; 178 Chinese [87.3%] and 26 White [12.7%] individuals), 118 with moderate glaucoma (mean age, 68.1 years; 95% CI, 66.8-69.4 years; 49 females [41.5%]; 97 Chinese [82.2%] and 21 White [17.8%] individuals), and 118 with advanced glaucoma (mean age, 68.5 years; 95% CI, 67.1-69.9 years; 43 females [36.4%]; 53 Chinese [44.9%] and 65 White [55.1%] individuals). The majority of ONH structural differences occurred in the early glaucoma stage, followed by a plateau effect in the later stages. Using a deep neural network, 3-D ONH structural differences were found to be present in both neural and connective tissues. Specifically, a mean of 57.4% (95% CI, 54.9%-59.9%, for no to mild glaucoma), 38.7% (95% CI, 36.9%-40.5%, for mild to moderate glaucoma), and 53.1 (95% CI, 50.8%-55.4%, for moderate to advanced glaucoma) of ONH landmarks that showed major structural differences were located in neural tissues with the remaining located in connective tissues. Conclusions and Relevance: This study uncovered complex 3-D structural differences of the ONH in both neural and connective tissues as a function of glaucoma severity. Future longitudinal studies should seek to establish a connection between specific 3-D ONH structural changes and fast visual field deterioration and aim to improve the early detection of patients with rapid visual field loss in routine clinical care.

Neurovascular segregation of the retinal nerve fiber layer in glaucoma.

The imaging data of one eye from 154 healthy and 143 glaucoma participants were acquired to evaluate the contributions of the neuronal and vascular components within the retinal nerve fiber layer (RNFL) for detecting glaucoma and modeling visual field loss through the use of optical coherence tomography (OCT) and OCT angiography. The neuronal and vascular components within the circumpapillary RNFL were independently evaluated. In healthy eyes, the neuronal component showed a stronger association with age (r = -0.52, p < 0.001) compared to measured RNFL thickness (r = -0.46, p < 0.001). Using the neuronal component alone improved detection of glaucoma (AUC: 0.890 ± 0.020) compared to measured RNFL thickness (AUC: 0.877 ± 0.021; χ2 = 5.54, p = 0.019). Inclusion of the capillary components with the sectoral neuronal component resulted in a significant improvement in glaucoma detection (AUC: 0.927 ± 0.015; χ2 = 15.34, p < 0.001). After adjusting for potential confounders, AUC increased to 0.952 ± 0.011. Results from modeling visual field loss in glaucoma eyes suggest that visual field losses associated with neuronal thinning were moderated in eyes with a larger capillary component. These findings suggest that segregation of the neurovascular components could help improve understanding of disease pathophysiology and affect disease management in glaucoma.

Three-year Outcomes of an Expanded Asynchronous Virtual Glaucoma Clinic in Singapore.

PURPOSE: Virtual glaucoma clinics can help increase health care capacity, easing the disease burden of glaucoma. This study assesses the safety, rate of glaucoma progression, time efficiency, and cost-savings of our expanded Glaucoma Observation Clinic (GLOC) at the Singapore National Eye Centre over 3 years. METHODS: All patients seen at GLOC between July 2018 and June 2021 were included. Visual acuity, intraocular pressure, and visual fields or optic nerve head imaging were recorded, followed by a virtual review of the data by an ophthalmologist. An objective review of the management of 100 patients was conducted by 2 senior consultants independently as a safety audit. Patient outcomes including the rate of instability (due to worsening of clinical parameters necessitating a conventional clinic visit), glaucoma progression, the consultation review time efficiency, and cost-savings of GLOC were measured. RESULTS: Of 3458 patients, 16% had glaucoma, and the others had risk factors for glaucoma. The safety audit demonstrated a 95% interobserver agreement. The rate of instability was 14.6%, of which true progression was observed in 3.12%. The time taken for a glaucoma specialist to review a GLOC patient was 5.75±0.75 minutes compared with 13.7±2.3 minutes in a conventional clinic. The per capita manpower cost per GLOC patient per visit was $36.77 compared with $65.62 in the conventional clinic. This translates to a cost-saving of $280.65 over the lifetime of a hypothetical patient. CONCLUSIONS: Our expanded virtual glaucoma clinic is a safe, time-efficient, and cost-effective model with low rates of glaucoma progression, which could allow for significant health care capacity expansion.

Handheld chromatic pupillometry can reliably detect functional glaucomatous damage in eyes with high myopia.

BACKGROUND/AIMS: To assess pupillary light responses (PLRs) in eyes with high myopia (HM) and evaluate the ability of handheld chromatic pupillometry (HCP) to identify glaucomatous functional loss in eyes with HM. METHODS: This prospective, cross-sectional study included 28 emmetropes (EM), 24 high myopes without glaucoma (HM) and 17 high myopes with confirmed glaucoma (HMG), recruited at the Singapore National Eye Center. Monocular PLRs were evaluated using a custom-built handheld pupillometer that recorded changes in horizontal pupil radius in response to 9 s of exponentially increasing blue (469.1 nm) and red (640.1 nm) lights. Fifteen pupillometric features were compared between groups. A logistic regression model (LRM) was used to distinguish HMG eyes from non-glaucomatous eyes (EM and HM). RESULTS: All pupillometric features were similar between EM and HM groups. Phasic constriction to blue (p<0.001) and red (p=0.006) lights, and maximum constriction to blue light (p<0.001) were reduced in HMG compared with EM and HM. Pupillometric features of melanopsin function (postillumination pupillary response, PIPR area under the curve (AUC) 0-12 s (p<0.001) and PIPR 6 s (p=0.01) to blue light) were reduced in HMG. Using only three pupillometric features, the LRM could classify glaucomatous from non-glaucomatous eyes with an AUC of 0.89 (95% CI 0.77 to 1.00), sensitivity 94.1% (95% CI 82.4% to 100.0%) and specificity 78.8% (95% CI 67.3% to 90.4%). CONCLUSION: PLRs to ramping-up light stimuli are unaltered in highly myopic eyes without other diagnosed ocular conditions. Conversely, HCP can distinguish glaucomatous functional loss in eyes with HM and can be a useful tool to detect/confirm the presence of glaucoma in patients with HM.

Measuring glaucoma quality of life in an Asian population using item banking: psychometric evaluation and computerized adaptive testing simulations.

PURPOSE: To assess the psychometric properties of glaucoma-specific health-related quality of life (HRQoL) item banks (IBs), and explore their efficiency using computerized adaptive testing (CAT) simulations. METHODS: In this cross-sectional, clinical study, 300 Asian glaucoma patients answered 221 items within seven IBs: Ocular Comfort Symptoms (OS); Activity Limitation (AL); Lighting (LT); Mobility (MB); Glaucoma Management (GM); Psychosocial (PSY); and Work (WK). Rasch analysis was conducted to assess each IB's psychometric properties (e.g., item "fit" to the construct; unidimensionality) and a set of analytic performance criteria guiding decision making relating to retaining or dropping domains and items was employed. CAT simulations determined the mean number of items for 'high' and 'moderate' measurement precision (stopping rule: SEM 0.3 and 0.387, respectively). RESULTS: Participants' mean age was 67.2 ± 9.2 years (62% male; 87% Chinese). LT, MB, and GM displayed good psychometric properties overall. To optimize AL's psychometric properties, 16 items were deleted due to poor "fit", high missing data, item bias, low discrimination and/or a low clinical/patient importance rating. To resolve multidimensionality in PSY, we rehomed 16 items into a "Concern (CN)" domain. PSY and CN required further amendment, including collapsing of response categories, and removal of poorly functioning items (N = 7). Due to poor measurement precision, low applicability and high ceiling effect, low test information indices, and low item separation index the WK IB was not considered further. In CAT simulations on the final seven IBs (n = 182 items total), an average of 12.1 and 15.7 items per IB were required for moderate and high precision measurement, respectively. CONCLUSIONS: After reengineering our seven IBs, they displayed robust psychometric properties and good efficiency in CAT simulations. Once finalized, GlauCAT™-Asian may enable comprehensive assessment of the HRQoL impact of glaucoma and associated treatments.

Oral Ibuprofen is Associated With Reduced Likelihood of Early Bleb Failure After Trabeculectomy in High-Risk Glaucoma Patients.

PRCIS: Individuals prescribed ibuprofen after trabeculectomy have better postoperative intraocular pressure (IOP) control and a higher chance of bleb survival despite being at a higher risk of scarring. PURPOSE: To investigate the effects of early adjunctive oral ibuprofen treatment on IOP and bleb failure in eyes at high risk of scarring. METHODS: In these retrospective analyses, 288 eyes of 273 patients (mean ± SD age: 68.56 ± 10.47 y; 32.60% females) with primary glaucoma who underwent trabeculectomy/phacotrabeculectomy at the Singapore National Eye Centre between April 2020 and April 2021 with a follow-up duration ≥1 year were included. Of these, 77 (26.7%) eyes deemed to be at high risk of scarring were administered oral ibuprofen ≥3 months postoperatively (mean ± SD ibuprofen administration duration: 4.08 ± 2.28 wk). Participant's IOPs at baseline and at postoperative weeks 1, 2-3; and months 1, 2, 3, 6, and 12 were recorded. Bleb failure was defined as 2 consecutive IOP readings of >21, >18, and >15 mm Hg, and/or requiring remedial postoperative laser or surgery. RESULTS: The ibuprofen group experienced significantly greater postoperative IOP reductions at week 1 [mean difference, 95%CI: -2.89 (-5.22, -0.56) mm Hg] and month 1 [-2.29 (-4.53, -0.05) mm Hg]; and substantially lower odds of bleb failure at the >18 mm Hg [odds ratio, 95% CI: 0.39 (0.20-0.79)] and >15 mm Hg [0.52 (0.29-0.94)] thresholds, compared with the non-ibuprofen group. No differences in adverse ocular hypotony events were observed. CONCLUSION: Early adjunctive oral ibuprofen administered to individuals at high risk of posttrabeculectomy scarring is associated with greater IOP reductions and reduced likelihood of bleb failure. Our results suggest that oral nonsteroidal anti-inflammatory drugs may be a safe way of improving trabeculectomy survival in high-risk eyes.

Assessing the external validity of machine learning-based detection of glaucoma.

Studies using machine learning (ML) approaches have reported high diagnostic accuracies for glaucoma detection. However, none assessed model performance across ethnicities. The aim of the study is to externally validate ML models for glaucoma detection from optical coherence tomography (OCT) data. We performed a prospective, cross-sectional study, where 514 Asians (257 glaucoma/257 controls) were enrolled to construct ML models for glaucoma detection, which was then tested on 356 Asians (183 glaucoma/173 controls) and 138 Caucasians (57 glaucoma/81 controls). We used the retinal nerve fibre layer (RNFL) thickness values produced by the compensation model, which is a multiple regression model fitted on healthy subjects that corrects the RNFL profile for anatomical factors and the original OCT data (measured) to build two classifiers, respectively. Both the ML models (area under the receiver operating [AUC] = 0.96 and accuracy = 92%) outperformed the measured data (AUC = 0.93; P < 0.001) for glaucoma detection in the Asian dataset. However, in the Caucasian dataset, the ML model trained with compensated data (AUC = 0.93 and accuracy = 84%) outperformed the ML model trained with original data (AUC = 0.83 and accuracy = 79%; P < 0.001) and measured data (AUC = 0.82; P < 0.001) for glaucoma detection. The performance with the ML model trained on measured data showed poor reproducibility across different datasets, whereas the performance of the compensated data was maintained. Care must be taken when ML models are applied to patient cohorts of different ethnicities.

Handheld chromatic pupillometry can accurately and rapidly reveal functional loss in glaucoma.

BACKGROUND/AIMS: Early detection and treatment of glaucoma can delay vision loss. In this study, we evaluate the performance of handheld chromatic pupillometry (HCP) for the objective and rapid detection of functional loss in glaucoma. METHODS: In this clinic-based, prospective study, we enrolled 149 patients (median (IQR) years: 68.5 (13.6) years) with confirmed glaucoma and 173 healthy controls (55.2 (26.7) years). Changes in pupil size in response to 9 s of exponentially increasing blue (469 nm) and red (640 nm) light-stimuli were assessed monocularly using a custom-built handheld pupillometer. Pupillometric features were extracted from individual traces and compared between groups. Features with the highest classification potential, selected using a gradient boosting machine technique, were incorporated into a generalised linear model for glaucoma classification. Receiver operating characteristic curve analyses (ROC) were used to compare the performance of HCP, optical coherence tomography (OCT) and Humphrey Visual Field (HVF). RESULTS: Pupillary light responses were altered in glaucoma compared with controls. For glaucoma classification, HCP yielded an area under the ROC curve (AUC) of 0.94 (95% CI 0.91 to 0.96), a sensitivity of 87.9% and specificity of 88.4%. The classification performance of HCP in early-moderate glaucoma (visual field mean deviation (VFMD) > -12 dB; AUC=0.91 (95% CI 0.87 to 0.95)) was similar to HVF (AUC=0.91) and reduced compared with OCT (AUC=0.97; p=0.01). For severe glaucoma (VFMD ≤ -12 dB), HCP had an excellent classification performance (AUC=0.98, 95% CI 0.97 to 1) that was similar to HVF and OCT. CONCLUSION: HCP allows for an accurate, objective and rapid detection of functional loss in glaucomatous eyes of different severities.

Investigating the macular choriocapillaris in early primary open-angle glaucoma using swept-source optical coherence tomography angiography.

Introduction: There has been a growing interest in the role of vascular factors in glaucoma. Studies have looked at the characteristics of macular choriocapillaris in patients with glaucoma but with conflicting results. Our study aims to use swept-source optical coherence tomography angiography (SS-OCTA) to evaluate macular choriocapillaris metrics in normal participants and compare them with patients with early primary open-angle glaucoma (POAG) (mean deviation better than -6dB). Methods: In this prospective, observational, cross-sectional study, 104 normal controls (157 eyes) and 100 patients with POAG (144 eyes) underwent 3 mm × 3mm imaging of the macula using the Plex Elite 9000 (Zeiss Meditec, Dublin, CA, USA). Choriocapillaris OCTA images were extracted from the device's built-in review software and were subsequently evaluated for the density and size of choriocapillaris flow deficits. Results: After adjusting for confounding factors, the density of flow deficits was independently higher in those aged 53 years and above (P ≤ 0.024) whereas the average flow deficit size was significantly larger in those aged 69 years and above (95% CI = 12.39 to 72.91; P = 0.006) in both normal and POAG patients. There were no significant differences in the density of flow deficits (P = 0.453) and average flow deficit size (P = 0.637) between normal and POAG participants. Conclusion: Our study found that macular choriocapillaris microvasculature on SS-OCTA is unaltered by subjects with POAG. This suggests that OCTA macular choriocapillaris may not be potentially helpful in differentiating early glaucoma from healthy eyes.

The development of a glaucoma-specific health-related quality of life item bank supporting a novel computerized adaptive testing system in Asia.

BACKGROUND: A glaucoma-specific health-related quality of life (HRQoL) item bank (IB) and computerized adaptive testing (CAT) system relevant to Asian populations is not currently available. We aimed to develop content for an IB focusing on HRQoL domains important to Asian people with glaucoma; and to compare the content coverage of our new instrument with established glaucoma-specific instruments. METHODS: In this qualitative study of glaucoma patients recruited from the Singapore National Eye Centre (November 2018-November 2019), items/domains were generated from: (1) glaucoma-specific questionnaires; (2) published articles; (3) focus groups/semi-structured interviews with glaucoma patients (n = 27); and (4) feedback from glaucoma experts. Data were analyzed using the constant comparative method. Items were systematically refined to a concise set, and pre-tested using cognitive interviews with 27 additional glaucoma patients. RESULTS: Of the 54 patients (mean ± standard deviation [SD] age 66.9 ± 9.8; 53.7% male), 67 (62.0%), 30 (27.8%), and 11 (10.2%) eyes had primary open angle glaucoma, angle closure glaucoma, and no glaucoma respectively. Eighteen (33.3%), 11 (20.4%), 8 (14.8%), 12 (22.2%), and 5 (9.3%) patients had no, mild, moderate, severe, or advanced/end-stage glaucoma (better eye), respectively. Initially, 311 items within nine HRQoL domains were identified: Visual Symptoms, Ocular Comfort Symptoms, Activity Limitation, Driving, Lighting, Mobility, Psychosocial, Glaucoma management, and Work; however, Driving and Visual Symptoms were subsequently removed during the refinement process. During cognitive interviews, 12, 23 and 10 items were added, dropped and modified, respectively. CONCLUSION: Following a rigorous process, we developed a 221-item, 7-domain Asian glaucoma-specific IB. Once operationalised using CAT, this new instrument will enable precise, rapid, and comprehensive assessment of the HRQoL impact of glaucoma and associated treatment efficacy.

Evaluation of Generative Adversarial Networks for High-Resolution Synthetic Image Generation of Circumpapillary Optical Coherence Tomography Images for Glaucoma.

Importance: Deep learning (DL) networks require large data sets for training, which can be challenging to collect clinically. Generative models could be used to generate large numbers of synthetic optical coherence tomography (OCT) images to train such DL networks for glaucoma detection. Objective: To assess whether generative models can synthesize circumpapillary optic nerve head OCT images of normal and glaucomatous eyes and determine the usability of synthetic images for training DL models for glaucoma detection. Design, Setting, and Participants: Progressively growing generative adversarial network models were trained to generate circumpapillary OCT scans. Image gradeability and authenticity were evaluated on a clinical set of 100 real and 100 synthetic images by 2 clinical experts. DL networks for glaucoma detection were trained with real or synthetic images and evaluated on independent internal and external test data sets of 140 and 300 real images, respectively. Main Outcomes and Measures: Evaluations of the clinical set between the experts were compared. Glaucoma detection performance of the DL networks was assessed using area under the curve (AUC) analysis. Class activation maps provided visualizations of the regions contributing to the respective classifications. Results: A total of 990 normal and 862 glaucomatous eyes were analyzed. Evaluations of the clinical set were similar for gradeability (expert 1: 92.0%; expert 2: 93.0%) and authenticity (expert 1: 51.8%; expert 2: 51.3%). The best-performing DL network trained on synthetic images had AUC scores of 0.97 (95% CI, 0.95-0.99) on the internal test data set and 0.90 (95% CI, 0.87-0.93) on the external test data set, compared with AUCs of 0.96 (95% CI, 0.94-0.99) on the internal test data set and 0.84 (95% CI, 0.80-0.87) on the external test data set for the network trained with real images. An increase in the AUC for the synthetic DL network was observed with the use of larger synthetic data set sizes. Class activation maps showed that the regions of the synthetic images contributing to glaucoma detection were generally similar to that of real images. Conclusions and Relevance: DL networks trained with synthetic OCT images for glaucoma detection were comparable with networks trained with real images. These results suggest potential use of generative models in the training of DL networks and as a means of data sharing across institutions without patient information confidentiality issues.

Comparison of machine learning approaches for structure-function modeling in glaucoma.

To evaluate machine learning (ML) approaches for structure-function modeling to estimate visual field (VF) loss in glaucoma, models from different ML approaches were trained on optical coherence tomography thickness measurements to estimate global VF mean deviation (VF MD) and focal VF loss from 24-2 standard automated perimetry. The models were compared using mean absolute errors (MAEs). Baseline MAEs were obtained from the VF values and their means. Data of 832 eyes from 569 participants were included, with 537 Asian eyes for training, and 148 Asian and 111 Caucasian eyes set aside as the respective test sets. All ML models performed significantly better than baseline. Gradient-boosted trees (XGB) achieved the lowest MAE of 3.01 (95% CI: 2.57, 3.48) dB and 3.04 (95% CI: 2.59, 3.99) dB for VF MD estimation in the Asian and Caucasian test sets, although difference between models was not significant. In focal VF estimation, XGB achieved median MAEs of 4.44 [IQR 3.45-5.17] dB and 3.87 [IQR 3.64-4.22] dB across the 24-2 VF for the Asian and Caucasian test sets and was comparable to VF estimates from support vector regression (SVR) models. VF estimates from both XGB and SVR were significantly better than the other models. These results show that XGB and SVR could potentially be used for both global and focal structure-function modeling in glaucoma.

Detecting visually significant cataract using retinal photograph-based deep learning.

Age-related cataracts are the leading cause of visual impairment among older adults. Many significant cases remain undiagnosed or neglected in communities, due to limited availability or accessibility to cataract screening. In the present study, we report the development and validation of a retinal photograph-based, deep-learning algorithm for automated detection of visually significant cataracts, using more than 25,000 images from population-based studies. In the internal test set, the area under the receiver operating characteristic curve (AUROC) was 96.6%. External testing performed across three studies showed AUROCs of 91.6-96.5%. In a separate test set of 186 eyes, we further compared the algorithm's performance with 4 ophthalmologists' evaluations. The algorithm performed comparably, if not being slightly more superior (sensitivity of 93.3% versus 51.7-96.6% by ophthalmologists and specificity of 99.0% versus 90.7-97.9% by ophthalmologists). Our findings show the potential of a retinal photograph-based screening tool for visually significant cataracts among older adults, providing more appropriate referrals to tertiary eye centers.