Faculty Bibliography

Excitotoxicity from the impairment of glutamate uptake constitutes an important mechanism in neurodegenerative diseases such as Alzheimer's, multiple sclerosis, and Parkinson's disease. Within the eye, excitotoxicity is thought to play a critical role in retinal ganglion cell death in glaucoma, diabetic retinopathy, retinal ischemia, and optic nerve injury, yet how excitotoxic injury impacts different retinal layers is not well understood. Here, we investigated the longitudinal effects of N-methyl-D-aspartate (NMDA)-induced excitotoxic retinal injury in a rat model using deep learning-assisted retinal layer thickness estimation. Before and after unilateral intravitreal NMDA injection in nine adult Long Evans rats, spectral-domain optical coherence tomography (OCT) was used to acquire volumetric retinal images in both eyes over 4 weeks. Ten retinal layers were automatically segmented from the OCT data using our deep learning-based algorithm. Retinal degeneration was evaluated using layer-specific retinal thickness changes at each time point (before, and at 3, 7, and 28 days after NMDA injection). Within the inner retina, our OCT results showed that retinal thinning occurred first in the inner plexiform layer at 3 days after NMDA injection, followed by the inner nuclear layer at 7 days post-injury. In contrast, the retinal nerve fiber layer exhibited an initial thickening 3 days after NMDA injection, followed by normalization and thinning up to 4 weeks post-injury. Our results demonstrated the pathological cascades of NMDA-induced neurotoxicity across different layers of the retina. The early inner plexiform layer thinning suggests early dendritic shrinkage, whereas the initial retinal nerve fiber layer thickening before subsequent normalization and thinning indicates early inflammation before axonal loss and cell death. These findings implicate the inner plexiform layer as an early imaging biomarker of excitotoxic retinal degeneration, whereas caution is warranted when interpreting the ganglion cell complex combining retinal nerve fiber layer, ganglion cell layer, and inner plexiform layer thicknesses in conventional OCT measures. Deep learning-assisted retinal layer segmentation and longitudinal OCT monitoring can help evaluate the different phases of retinal layer damage upon excitotoxicity.

The cellular-level visualization of retinal microstructures such as blood vessel wall components, not available with other imaging modalities, is provided with unprecedented details by dark-field imaging configurations; however, the interpretation of such images alone is sometimes difficult since multiple structural disturbances may be present in the same time. Particularly in eyes with retinal pathology, microstructures may appear in high-resolution retinal images with a wide range of sizes, sharpnesses, and brightnesses. In this paper we show that motion contrast and phase gradient imaging modalities, as well as the simultaneous acquisition of depth-resolved optical coherence tomography (OCT) images, provide additional insight to help understand the retinal neural and vascular structures seen in dark-field images and may enable improved diagnostic and treatment plans.

PURPOSE/UNASSIGNED:Broken stick analysis is a widely used approach for detecting unknown breakpoints where the association between measurements is nonlinear. We propose LIMBARE, an advanced linear mixed-effects breakpoint analysis with robust estimation, especially designed for longitudinal ophthalmic studies. LIMBARE accommodates repeated measurements from both eyes and over time, and it effectively addresses the presence of outliers. METHODS/UNASSIGNED:The model setup of LIMBARE and the computing algorithm for point and confidence interval estimates of the breakpoint were introduced. The performance of LIMBARE and other competing methods was assessed via comprehensive simulation studies and application to a longitudinal ophthalmic study with 216 eyes (145 subjects) followed for an average of 3.7 ± 1.3 years to examine the longitudinal association between structural and functional measurements. RESULTS/UNASSIGNED:In simulation studies, LIMBARE showed the smallest bias and mean squared error for estimating the breakpoint, with an empirical coverage probability of corresponding confidence interval estimates closest to the nominal level for scenarios with and without outlier data points. In the application to the longitudinal ophthalmic study, LIMBARE detected two breakpoints between visual field mean deviation (MD) and retinal nerve fiber layer thickness and one breakpoint between MD and cup-to-disc ratio, whereas the cross-sectional analysis approach detected only one and none, respectively. CONCLUSIONS/UNASSIGNED:LIMBARE enhances breakpoint estimation accuracy in longitudinal ophthalmic studies, and the cross-sectional analysis approach is not recommended for future studies. TRANSLATIONAL RELEVANCE/UNASSIGNED:Our proposed method and companion R package provide a valuable computational tool for advancing longitudinal ophthalmology research and exploring the association relationships among ophthalmic variables.

The cellular-level visualization of retinal microstructures such as blood vessel wall components, not available with other imaging modalities, is provided with unprecedented details by dark-field imaging configurations; however, the interpretation of such images alone is sometimes difficult since multiple structural disturbances may be present in the same time. Particularly in eyes with retinal pathology, microstructures may appear in high-resolution retinal images with a wide range of sizes, sharpnesses, and brightnesses. In this paper we show that motion contrast and phase gradient imaging modalities, as well as the simultaneous acquisition of depth-resolved optical coherence tomography (OCT) images, provide additional insight to help understand the retinal neural and vascular structures seen in dark-field images and may enable improved diagnostic and treatment plans.

OBJECTIVE:To assess the feasibility of remotely training glaucoma patients to take a 10-session clustered virtual reality (VR) visual field (VF) test (Vivid Vision Perimetry [VVP-10]) at home, analyze results for test-retest variability, and assess correspondence with conventional perimetry. DESIGN/METHODS:Cross-sectional study. SUBJECTS/METHODS:Twenty-one subjects with glaucoma were enrolled and included in the feasibility assessment of remote training. Thirty-six eyes were used for test-retest analysis and determination of concordance with the Humphrey Field Analyzer (HFA). METHODS:Subjects were provided with a mobile VR headset containing the VVP-10 test software and trained remotely via video conferencing. Subjects were instructed to complete 10 sessions over a 14-day period. MAIN OUTCOME MEASURES/METHODS:Feasibility was determined by the number of subjects who were able to independently complete VVP-10 over the 14-day period after 1 remote training session. The intraclass correlation coefficient (ICC) for average fraction seen across 10 sessions and the standard error (SE) of the mean were primary outcome measures for assessing test-retest variability. Correlation with HFA mean sensitivity (MS) across eyes, was a secondary outcome measure. RESULTS:Twenty subjects (95%) successfully completed the VVP-10 test series after 1 training session. The ICC for VVP-10 was 0.95 (95% confidence interval [CI], 0.92-0.97). The mean SE in units of fraction seen was 0.012. The Spearman correlations between VVP-10 average fraction seen and HFA MS were 0.87 (95% CI, 0.66-0.98) for moderate-to-advanced glaucoma eyes, and decreased to 0.67 (95% CI, 0.28-0.94) when all eyes were included. CONCLUSIONS:Remote training of patients at home is feasible, and subsequent remote clustered VF testing using VVP-10 by patients on their own, without any further interactions with caregivers or study staff, was possible. At-home VVP-10 results demonstrated low test-retest variability. Future studies must be conducted to determine if VVP-10, taken at home as convenient for the patient, may be a viable supplement to provide equivalent or complementary results to that of standard in-clinic assessment of visual function in glaucoma. FINANCIAL DISCLOSURE(S)/BACKGROUND:Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

OBJECTIVE:To examine the longitudinal postoperative outcomes of open versus closed conjunctiva implantation of the XEN45 gel stent. DESIGN/METHODS:Retrospective multicenter study. SUBJECTS/METHODS:One hundred ninety-three patients with glaucoma underwent XEN45 implantation via an open or closed conjunctiva approach. METHODS:Data on patient demographics; diagnoses; preoperative and postoperative clinical data; outcome measures, including intraocular pressure (IOP); use of glaucoma medications; visual acuity; and complications were collected. Statistical analyses were performed with P < 0.05 as significant. MAIN OUTCOME MEASURES/METHODS:Failure was defined as < 20% reduction in IOP from the medicated baseline or a IOP of > 21 mmHg at 2 consecutive visits at postoperative month 1 and beyond, the need for subsequent operative intervention or additional glaucoma surgery, or a catastrophic event, such as loss of light perception. Eyes that had not failed by these criteria and were not on glaucoma medications were considered complete successes. Overall success was defined as those who achieved success either with or without topical medications. RESULTS:Patients were followed for an average of 17 months. Complete success was achieved in 42.5% and 24.7% of the open and closed groups, respectively (P = 0.01). Overall success was achieved in 64.2% and 37.0% of the open and closed groups, respectively (P < 0.001) at the last follow-up. Bleb needling was performed in 12.4% of eyes in the open group compared with 40% of eyes in the closed group. An IOP spike of ≥ 10 mmHg was twice as likely to occur in the closed group compared with the open group during the postoperative period (40% vs. 18%; P = 0.001). CONCLUSIONS:Implantation of XEN45 with opening of the conjunctiva resulted in a lower IOP with greater success and lower needling rate compared with those achieved with the closed conjunctiva technique. Similar rates of postoperative complications and vision loss were noted in each group. Although both procedures provide substantial IOP reduction, the open technique appears to result in higher success rates and fewer postoperative interventions. FINANCIAL DISCLOSURE(S)/BACKGROUND:Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

PURPOSE/UNASSIGNED:Race disparities in the healthcare system and the resulting inequality in clinical data among different races hinder the ability to generate equitable prediction results. This study aims to reduce healthcare disparities arising from data imbalance by leveraging advanced transfer learning (TL) methods. METHOD/UNASSIGNED:We examined the ophthalmic healthcare disparities at a population level using electronic medical records data from a study cohort (N = 785) receiving care at an academic institute. Regression-based TL models were usesd, transferring valuable information from the dominant racial group (White) to improve visual field mean deviation (MD) rate of change prediction particularly for data-disadvantaged African American (AA) and Asian racial groups. Prediction results of TL models were compared with two conventional approaches. RESULTS/UNASSIGNED:Disparities in socioeconomic status and baseline disease severity were observed among the AA and Asian racial groups. The TL approach achieved marked to comparable improvement in prediction accuracy compared to the two conventional approaches as evident by smaller mean absolute errors or mean square errors. TL identified distinct key features of visual field MD rate of change for each racial group. CONCLUSIONS/UNASSIGNED:The study introduces a novel application of TL that improved reliability of the analysis in comparison with conventional methods, especially in small sample size groups. This can improve assessment of healthcare disparity and subsequent remedy approach. TRANSLATIONAL RELEVANCE/UNASSIGNED:TL offers an equitable and efficient approach to mitigate healthcare disparities analysis by enhancing prediction performance for data-disadvantaged group.

Glaucoma causes progressive visual field deterioration and is the leading cause of blindness worldwide. Glaucomatous damage is irreversible and greatly impacts quality of life. Therefore, it is critically important to detect glaucoma early and closely monitor progression to preserve functional vision. Glaucoma is routinely monitored in the clinical setting using optical coherence tomography (OCT) for derived measures such as the thickness of important visual structures. There is not a consensus of what measures represent the most relevant biomarkers of glaucoma progression. Further, despite the increasing availability of longitudinal OCT data, a quantitative model of 3D structural change over time associated with glaucoma does not exist. In this paper we present an algorithm that will perform hierarchical geodesic modeling at the imaging level, considering 3D OCT images as observations of structural change over time. Hierarchical modeling includes subject-wise trajectories as geodesics in the space of diffeomorphisms and population level (glaucoma vs control) trajectories are also geodesics which explain subject-wise trajectories as deviations from the mean. Our preliminary experiments demonstrate a greater magnitude of structural change associated with glaucoma compared to normal aging. Our algorithm has the potential application in patient-specific monitoring and analysis of glaucoma progression as well as a statistical model of population trends and population variability.

PURPOSE:The presence of imbalanced datasets in medical applications can negatively affect deep learning methods. This study aims to investigate how the performance of convolutional neural networks (CNNs) for glaucoma diagnosis can be improved by addressing imbalanced learning issues through utilizing glaucoma suspect samples, which are often excluded from studies because they are a mixture of healthy and preperimetric glaucomatous eyes, in a semi-supervised learning approach. METHODS:A baseline 3D CNN was developed and trained on a real-world glaucoma dataset, which is naturally imbalanced (like many other real-world medical datasets). Then, three methods, including reweighting samples, data resampling to form balanced batches, and semi-supervised learning on glaucoma suspect data were applied to practically assess their impacts on the performances of the trained methods. RESULTS:The proposed method achieved a mean accuracy of 95.24%, an F1 score of 97.42%, and an area under the curve of receiver operating characteristic (AUC ROC) of 95.64%, whereas the corresponding results for the traditional supervised training using weighted cross-entropy loss were 92.88%, 96.12%, and 92.72%, respectively. The obtained results show statistically significant improvements in all metrics. CONCLUSIONS:Exploiting glaucoma suspect eyes in a semi-supervised learning method coupled with resampling can improve glaucoma diagnosis performance by mitigating imbalanced learning issues. TRANSLATIONAL RELEVANCE:Clinical imbalanced datasets may negatively affect medical applications of deep learning. Utilizing data with uncertain diagnosis, such as glaucoma suspects, through a combination of semi-supervised learning and class-imbalanced learning strategies can partially address the problems of having limited data and learning on imbalanced datasets.

TOPIC/OBJECTIVE:Understanding approaches to sustainability in cataract surgery and their risks and benefits CLINICAL RELEVANCE: In the United States, healthcare is responsible for approximately 8.5% of greenhouse gas (GHG), and cataract surgery is one of the most commonly performed surgical procedures. Ophthalmologists can contribute to reducing GHG emissions, which lead to a steadily increasing list of health concerns ranging from trauma to food instability. METHODS:We conducted a literature review to identify the benefits and risks of sustainability interventions. We then organized these interventions into a decision tree for use by individual surgeons. RESULTS:Identified sustainability interventions fall into the domains of advocacy and education, pharmaceuticals, process, and supplies and waste. Existing literature shows certain interventions may be safe, cost-effective, and environmentally friendly. These include dispensing medications home to patients after surgery, multi-dosing appropriate medications, training staff to properly sort medical waste, reducing the number of supplies used during surgery, and implementing immediate sequential bilateral cataract surgery where clinically appropriate. The literature was lacking on the benefits or risks for some interventions, such as switching specific single use supplies to reusables or implementing a hub-and-spoke style theatre setup. Many of the advocacy and education interventions have inadequate literature specific to ophthalmology but are likely to have minimal risks. CONCLUSIONS:Ophthalmologists can engage in a variety of safe and effective approaches to reduce or eliminate dangerous GHG emissions associated with cataract surgery.