Faculty Bibliography

Purpose : Falls risk increases with glaucoma. Gait impairments occur in people with glaucoma; however, they do not fully explain why greater visual field loss results in higher fall rates (Mihailovic 2020). Harmonic ratios, which are derived based on the frequency analysis of trunk acceleration signals, quantify step-to-step gait symmetry and have been associated with falls risk in older adults (Bellanca 2013). Harmonic ratios have not been examined in glaucoma. This pilot study tested the hypothesis that gait symmetry will be reduced with worse visual field loss in glaucoma, particularly under sensory challenging conditions and increased attention demands. Methods : Ten adults with glaucoma (6F/4M, 67+/-8 years), instrumented with motion capture markers (Vicon Motion Systems Ltd, UK) and accelerometers (Delsys Inc., MA), were asked to walk in 8 conditions with varying floor (hard floor, carpet), light (well-lit, dim) and concurrent information processing (IP) task conditions (no task, auditory IP choice reaction time task). Glaucoma severity was determined using the visual field mean deviation (VF MD) assessed by automated Humphrey perimetry (Zeiss, CA). The VF MD measured in the better/worse eye and averaged across the 10 participants was equal to -2.9+/-3.8 / -7.5+/-6.7 dB, respectively. The analyses used mixed linear models with the fixed effects including VF MD either in the better eye or worse eye, floor/light condition, IP condition and the first order interaction of these factors. Subject was added as a random effect. The dependent gait measure was the anteroposterior (AP) harmonic ratio (Bellanca 2013). Statistical significance was set at 0.05. Results : Visual field MD in the better eye was associated with the AP harmonic ratio (F(1,8)=11.9, p=0.009), i.e. step-to-step gait symmetry in the anteroposterior direction was reduced with worse visual field loss in the better eye. In addition, the main effect of floor/light condition was associated with the AP harmonic ratio (F(3,58)=8.4, p<0.0001). Post-hoc analyses found this effect was driven by a change in floor condition. All other effects included in the analyses did not reach statistical significance (p>0.1). Conclusions : Gait symmetry is reduced in glaucoma and thus may be an important metric. Further research is needed to determine if reduced gait symmetry is associated with increased falls risk in this population

Purpose : Current Optical Coherence Tomography (OCT) denoising techniques mainly focus on denoising 2-dimensional (2D) B-scans, especially for deep learning (DL) methods, which assume spatial integrity among neighboring samplings. However, OCT signal is essentially one dimensional (1D), and eye movements during scanning often violate the assumption. The purpose of this study was to see if 1D denoising is a feasible approach for clinical OCT imaging. Methods : 3D SD-OCT data within 6x6x2mm volumes centered on optic nerve head were obtained from 121 eyes (79 patients). Clean reference scans were constructed by registering and averaging 6 OCT scans obtained on the same day using ANTs software. As shown in Figure 1, we used a 5-layer U-shape net (UNet) for both 2D denoiser (Figure 1.(a)) and 1D denoiser (Figure1.(b)). In addition, both 2D and 1D approaches are combined by using the 2D denoised B-scan as a mask to selectively remove high signal peaks in the 1D denoised B-scan (Figure 1.(c)). Peak signal-to-noise ratio (PSNR) and contrast-to-noise ratio (CNR) were calculated to compare the performance. Results : Subjectively, the 2D denoiser generated smoother edges but tended to oversmooth textual information within the retinal layers, while the 1D denoiser preserved more textual information but caused more jittering at retinal edges due to the lack of structural information from neighboring A-scans. Quantitatively, the 1D denoiser showed similar PSNR to the 2D denoiser, while outperforming in CNR (PSNR: 31.20 (1D) V.S. 31.20 dB (2D), p=0.963; CNR: 4.23 (1D) V.S. 3.90 dB (2D), p<0.001, paired t-test, Table 1). The combined denoiser further improved CNR (4.39 (combined) V.S. 3.90 dB (2D), p<0.001). Combining 1D and 2D denoisers, the denoised B-scan showed more continuous edges compared to the 1D denoiser and did not lose the texture information compared to the 2D denoiser (Figure 2). Conclusions : Quantitatively, 1D denoiser performance is as good as 2D denoiser or even better. A combination of 1D and 2D approaches may provide well-balanced image enhancement in clinical applications

Purpose : To improve the performance of the feature agnostic AI-based glaucoma detection algorithm by evaluating an uncertainty score for each prediction. Methods : We previously developed a 5-layer 3D Convolutional Neural Network (CNN) in using the OCT scans from both eyes of 134 healthy, 779 glaucoma patients on a Cirrus HDOCT scanner (200x200 ONH Cubes; Zeiss, Dublin CA). In our analysis, we excluded scans with signal strength less than 7 and downsampled the volumes to 64x64x128 voxels. Uncertainty of AI models can be estimated by computing the effect of randomly ignoring a set of parameters within the network. We randomly zeroed 5% of each of the 5 convolutional layers and computed the entropy in the final score over 20 forward passes. The performance of the approach was assessed using a 10-fold cross validation study. Results : Over the 10-folds, the model showed an AUC of 0.91+/-0.027. In analysing the uncertainty and the probabilistic scores generated by the model (Softmax function) for one fold (see Fig. 1), we observed that a threshold of 0.8 can be used to flag 75% of the false positives and false negatives for further review. On the other hand, only 25% of the healthy controls and 20% of glaucoma patients showed an uncertainty score above that threshold. Fig. 2 summarises the overall uncertainties scores and indicates that low scores are associated with the correctly identified cases while the errors show higher uncertainty scores. Conclusions : The quantitative uncertainty measure provides supplementary information to clinicians and can be used to flag difficult cases automatically. Given that the dataset used in this work is highly imbalanced (more positive cases compared to normal cases) the uncertainty score for true negative cases is significantly higher compared to true positive cases. We expect to achieve lower uncertainty scores for normal cases if more data for normal eyes are available. The uncertainty analysis presented here may aid clinical interpretations of AI-based glaucoma detection outcomes. A separate study will be run to measure this improvement and compare the result with experts' level of uncertainty

Purpose : To assess the repeatability of estimates of mean deviation (MD) and visual field index (VFI) obtained from an automated deep-learning approach that analysed raw OCT volumes. Methods : OCT scans were acquired from both eyes of 138 healthy, 743 glaucoma suspects and 941 glaucoma patients (Cirrus HD-OCT scanner, 200x200 ONH Cubes, Zeiss, Dublin CA). The scans were acquired at multiple visits, with two or more scans acquired at each visit. Scans with signal strength < 7 were discarded, giving us a total of 19,208 OCT scans. A subset of 5207 eyes (total of 10,414 scans) had repeat scans of that met the inclusion criteria. 24-2 Humphrey visual field (VF) tests were administered at each visit. A single convolutional neural network was trained to estimate the MD and VFI (dual outputs) from downsampled OCT volumes (50x50x128 voxels). The network consisted of 5 convolutional layers, followed by a global average pooling layer and dual outputs to enable the simultaneous estimation of MD and VFI. A mean squared error loss was used to train the network using an Adam optimiser over a total of 200 epochs. A 10-fold cross-validation scheme was used, where the dataset was divided into 10 non-overlapping folds (~182 subjects per fold) - trained on 8-folds, validated on one and tested on one. Each subject was limited to a unique fold. The performance of the method was assessed by computing the median error and interquartile range. The repeatability was assessed using a set of 5207 OCT scans that had repeats available. Results : The median absolute error (Q1, Q3) for the estimates of MD and VFI were 1.66 (0.79, 2.99) dB and 3.01 (1.48, 6.63) %, respectively. In the reproducibility test, the Pearson's correlation coefficient was 0.91 (CI: [0.91, 0.92]) and 0.91 (CI: [0.90, 0.92]), for MD and VFI, respectively. The median absolute difference between the repeated estimates for MD and VFI were 0.53 (0.21, 0.51) dB and 1.17 (0.45, 1.14)%, respectively. Conclusions : The deep-learning based approach for estimating visual field test parameters shows repeatability better than expected test-to-test variability

Purpose : Rhesus macaques are a common animal model in ophthalmology because of the high similarity of their eyes and visual pathway to human. The characterization of optic nerve head (ONH) and peripapillary region in monkeys reported so far mostly involved a manual process which is laborious and subjected to operator errors. It is also usually generated from a cohort of similar age group. In this cross-sectional observational study, we deploy automated and manual segmentations to evaluate the OCT retinal nerve fiber layer (RNFL) thickness, ONH and lamina cribrosa (LC) microstructure parameters in a cohort of free roaming macaques. Methods : In-vivo ONH spectral-domain OCT scans (Leica, Chicago, IL) were obtained by a single experienced operator after excluding eyes with any retinal pathologies. The margins of the optic disc were drawn manually and the resultant scans were analyzed using an automated segmentation software of our own design. The LC microstructure parameters were obtained through a previously described segmentation algorithm. The other parameters of ONH, namely the cup-to-disc (C/D) ratio and minimum rim width (MRW) were assessed manually. Wilcoxon rank sum test was used to test the association of LC parameters, C/D ratio and MRW with age, while the rest of the parameters were analyzed using mixed effects model accounting for age, sex and intra-subject correlation. Results : 29 eyes from 19 monkeys (11 females, 8 males) with age ranging from 4.2 to 23.8 years were analyzed. Males were overall bigger and significantly heavier than females in our cohort (Table 1). Superior RNFL was thicker in male and is the only RNFL parameter that was associated with age or sex in this healthy cohort. No significant association was detected for any of the ONH parameters with age or sex. LC was more visible and thicker in male with higher beam to pore ratio and connective tissue fraction than in female. Conclusions : The characterization of normal macaque eyes from a cohort of free roaming animals is useful as a standard reference to assess pathological changes in future experimental studies

Purpose : Prevalence and severity of glaucoma varies between ethnicities. It has been previously shown that ocular vessel density (VD) varies among healthy subjects of different ethnicities. To further elucidate the potential role of VD in glaucoma we examined ocular VD in Caucasian, African American (AA), and Latin at similar stages of glaucoma severity. Methods : 150 glaucoma eyes of which 46 eyes (30 subjects) were Caucasian, 71 eyes (43 subjects) African American, and 33 eyes (19 subjects) Latin were included in the analysis. Comorbidities known to affect the systemic or local micro-or macro-vasculature and medications that are known to modify vessel diameter were excluded. Disease severity distribution was similar across ethnicity groups. All eyes had comprehensive ophthalmic examination, Cirrus HD-OCT (Zeiss, Dublin, CA) and OCT angiography (OCTA; Angioplex, Zeiss) qualified scans of the macula and optic nerve head regions (200x200 OCT cube scans and 3x3mm / 6x6mm OCTA scans). VD as provided by the device's native software in the inner vascular plexus was used for the analysis. Statistical analysis was performed using mixed-effects models accounting for ethnicity, age, axial length, visual field mean deviation (MD), OCT signal strength (SS), disc area and intra-subject correlation. Tukeyadjusted p-values for pairwise ethnicity comparisons were obtained.Results : No significant difference was detected in age and MD among ethnicities (Table 1). Caucasian subjects had the longest AL and thinnest RNFL, and Latin subjects had the largest disc area and cup-to-disc ratio (CDR; Table 1). No significant differences were detected among ethnicities in ONH VD in any of the scan types and regions. In the macula, Caucasians had significantly higher VD in the center of both scan sizes in comparison with both AA and Latin (Table 2). Caucasian eyes also had significantly higher VD in the full 3x3 scan in comparison with AA eyes. There were no significant differences in the rest of the macular VD measurements among the 3 groups. Conclusions : Macular VD in glaucoma subjects varies among ethnicities and might play a role in the varying disease behavior among ethnicities. Differences in foveal avascular zone size might explain our findings but further investigation is warranted

Purpose : Mutations in optineurin (OPTN) are associated with familial normal tension glaucoma and other neurodegenerative diseases. It remains unclear how OPTN loss or mutation alters visual function during aging. Here, we used transgenic mouse models and in vivo assessments to test the hypothesis that OPTN dysfunction contributes to progressive visual impairment through a toxic gain of function mechanism. Methods : Mice with C57BL/6 background were used (Fig 1): wildtype (WT; n=19), homozygous OPTN knock-out (mOPTN-KO; n=13), hemizygous mouse E50K OPTN knock-in (mE50K-het; n=8), homozygous mouse E50K OPTN knock-in (mE50K homoz; n=10), and human E50K OPTN bacterial artificial chromosome overexpression (hE50K BAC; n=6) (PMID: 31076632, 25818176). Intraocular pressure (IOP), total retinal thickness (TRT), visual acuity (VA), and contrast sensitivity (CS) were measured at 6, 12, and 18 months of age in the same mice using the TonoLab rebound tonometer, Bioptigen spectral-domain optical te uses cookies. By continuing to use our website, you are agreeing to coherence tomography imaging, and OptoMotry optokinetic virtual reality system respectively. Left and right eye data were averaged and analyzed using ANOVAs followed by posthoc tests between genotype and age groups, as well as linear regressions for VA versus contrast threshold (CT). Results : Our longitudinal study of the same mice during the aging process showed that IOP remained normal between 10-15 mmHg (Fig 2A). Small to no difference in TRT over time or compared to WT was observed (Fig 2B). mE50K-homoz, mE50K-het, and hE50K BAC mice exhibited greater age-dependent decline in VA and CT than WT or mOPTN-KO mice (Fig 2C, 2D, 2E). In contrast, mOPTN-KO mice showed preservation of VA and CT over time compared to WT. Consistently, mice with one copy of E50K OPTN (mE50K het) experienced less deterioration of VA and CT compared to mice with two copies (mE50K homoz) or mild overexpression (hE50K BAC). Conclusions : Depsite limited IOP and TRT changes between age and genotype groups, E50K OPTN was associated with differential age-dependent visual impairment (greater for CS than VA). Surprisingly, OPTN deficiency preserved visual function such that CS in knockout mice was better than WT mice. Our results suggest visual loss associated with E50K OPTN is due to a toxic gain of function mechanism, and that suppression of OPTN might constitute a therapeutic strategy for glaucomatous neurodegeneration

Purpose : The lamina cribrosa (LC) is considered to play an important role in the pathogenesis of glaucoma. In this study, we investigate the shape variation (SV) of the LC pores as a potential biomarker for quantifying the morphological irregularity in vivo. Methods : 36 healthy and 14 glaucomatous (GL) eyes (total: 39 subjects) underwent a comprehensive ophthalmic examination and scanning of the optic nerve head with sweptsource OCT (Table 1). Images were converted to isotropic and pores were segmented using ImageJ. SV was defined as the mean-squared error of the pore pattern with respect to a solid circle (Figure 1(a)) with SV of a circle marked as zero, and higher SV value with increasing shape irregularity. SV of each pore was automatically calculated by a MATLAB code. The overall SV value was generated as the average of SV in the stack of individual slices. Age effect on SV was examined in all healthy eyes and a subset of 14 eyes was selected for age-matched comparison with the glaucomatous eyes (Table 1). Results : No significant correlation was detected between SV and age (p=0.145; Spearman correlation) in all healthy subjects. Examining the effect of depth on the difference between SV of glaucomatous and healthy eyes, the posterior third of the LC had significantly lower than other sections (p=0.007; Figure 1(b)). SV in glaucoma eyes was significantly higher than in the healthy group (p=0.008; Figure 1(c)). Conclusions : We demonstrated morphological differences in pore shape variation between healthy and glaucoma eyes that is mostly affecting the anterior 2/3 of the LC. Further studies are warranted to assess the use of SV as a structural biomarker in glaucoma

Purpose : The uncertainty quantification of segmentation results is critical for understanding the reliability of the segmentation model. The purpose of this study is to investigate the effect of deep learning-based segmentation with uncertainty measurement in the relationship between RNFL thickness and visual field mean deviation (MD) Methods : Optical coherence tomography (OCT) scans were acquired from both eyes on 634 glaucoma patients, 404 glaucoma suspects, and 49 healthy controls using commercial OCT device (Cirrus HD-OCT, 200x200 Optic Disc Cubes; Zeiss, Dublin, CA). All subjects had visual field (VF) tests at each visit (Humphrey VF, SITA 24-2 test; Zeiss). A segmentation model was trained using Bayesian deep learning for voxel-wise segmentation of RNFL layer in OCT volume and compute the voxel-wise uncertainty of the segmentation output. The higher uncertainty denotes the unreliability of the segmentation and vice versa and it allows the determination of erroneous segmentation at test time. Uncertainty-guided global mean of the RNLF thickness (RNFL-Umean) was then computed by discarding the voxels with erroneous segmentation labels with higher uncertainty during the thickness computation. Also, the global mean of the RNLF thickness (RNFLmean;) was computed without taking uncertainty into account. Pearson correlation coefficient between RNFLU and MD was computed and compared with the Pearson correlation coefficient between RNFLmean;and MD. Results : The proposed RNFL-Umean;gave stronger correlation with MD than RNFLmean;The Pearson correlation coefficients were (0.67 (RNFL-Umean;) vs 0.63 (RNFL mean;; p<0.001) for glaucoma subjects, (0.56 vs 0.53 ;p=0.01) for glaucoma suspects and (0.08 vs 0.01; p=0.21) for normal subjects. Conclusions : The proposed uncertainty-guided computation of RNFL thickness showed improved correlation with the visual field MD. This demonstrates that segmentation uncertainty can be used to reduce the effect of inaccurate segmentation in computing the RNFL thickness. This also shows that uncertainty-guided computation of RNFL thickness is a better predictor of visual function than the normal RNFL thickness computed without using uncertainty

Purpose : Repetitive transorbital alternating current stimulation (rtACS) is an application of weak electric current near the eyes used in vision rehabilitation of optic neuropathies (ON). Conceptually rtACS entrains neuronal oscillations, augmenting neuronal function. In subjects with ON we evaluated whether rtACS influenced visual structure and function. Methods : 34 subjects with ON enrolled in a prospective trial underwent comprehensive ophthalmic evaluation, visual field (VF) 24-2 and 10-2 tests (Humphrey Field Analyzer) and OCT (Cirrus HD-OCT) retinal nerve fiber layer (RNFL) and ganglion cell inner plexiform layer (GCIPL) thicknesses at baseline and follow-up (FU) visits. Subjects received rtACS 30-to 45-minutes daily for 10 days. Sham subjects (n=4) underwent the same procedures but received no current. Point-by-point analyses of VF total deviation (TD) values were conducted between rtACS and sham groups. Regression analyses determined rate of change for each TD point per eye (significant points with positive rate of change defined as improved, negative rate of change as progressed; insignificant rate of change as no change) and the association between RNFL and GCIPL between groups. Results : The number of FU visits with VF tests ranged 2 to 7, with no significant differences detected between rtACS vs sham groups' FU duration. No significant differences were detected between groups' baseline VF 24-2 and 10-2 mean deviation (MD) values (Table 1). The average numbers of improved points (VF 10-2) and progressed points (VF 24-2) were greater for rtACS while the average number of no change points was greater for sham (VF 24-2, p0.05, Table 1). Further analysis of FU duration determined a significant interaction with rtACS; number of improved points (VF 10-2) and progressed points (VF 24-2, p<0.02) were not sustained over time. No significant differences were detected in average RNFL and GCIPL thicknesses between groups. Conclusions : Preliminary analyses of the effect of rtACS in ON indicate initial improvement but not a clear benefit over time. Detection of differences between rtACS vs sham groups may be biased due to the small sham sample and range of FU duration as VF test-to-test variability is known to increase with worsening VF MD. Future analyses will assess interim effect at early vs late FU time points to evaluate the role of rtACS in vision rehabilitation