The pseudotemporal bootstrap for predicting glaucoma from cross-sectional visual field data

Progressive loss of the field of vision is characteristic of a number of eye diseases such as glaucoma, a leading cause of irreversible blindness in the world. Recently, there has been an explosion in the amount of data being stored on patients who suffer from visual deterioration, including visual field (VF) test, retinal image, and frequent intraocular pressure measurements. Like the progression of many biological and medical processes, VF progression is inherently temporal in nature. However, many datasets associated with the study of such processes are often cross sectional and the time dimension is not measured due to the expensive nature of such studies. In this paper, we address this issue by developing a method to build artificial time series, which we call pseudo time series from cross-sectional data. This involves building trajectories through all of the data that can then, in turn, be used to build temporal models for forecasting (which would otherwise be impossible without longitudinal data). Glaucoma, like many diseases, is a family of conditions and it is, therefore, likely that there will be a number of key trajectories that are important in understanding the disease. In order to deal with such situations, we extend the idea of pseudo time series by using resampling techniques to build multiple sequences prior to model building. This approach naturally handles outliers and multiple possible disease trajectories. We demonstrate some key properties of our approach on synthetic data and present very promising results on VF data for predicting glaucoma

A Spatio-Temporal Bayesian Network Classifier for Understanding Visual Field Deterioration

Progressive loss of the field of vision is characteristic of a number of eye diseases such as glaucoma which is a leading cause of irreversible blindness in the world. Recently, there has been an explosion in the amount of data being stored on patients who suffer from visual deterioration including field test data, retinal image data and patient demographic data. However, there has been relatively little work in modelling the spatial and temporal relationships common to such data. In this paper we introduce a novel method for classifying Visual Field (VF) data that explicitly models these spatial and temporal relationships. We carry out an analysis of this method and compare it to a number of classifiers from the machine learning and statistical communities. Results are very encouraging showing that our classifiers are comparable to existing statistical models whilst also facilitating the understanding of underlying spatial and temporal relationships within VF data. The results reveal the potential of using such models for knowledge discovery within ophthalmic databases, such as networks reflecting the ‘nasal step’, an early indicator of the onset of glaucoma. The results outlined in this paper pave the way for a substantial program of study involving many other spatial and temporal datasets, including retinal image and clinical data

Intervals between visual field tests when monitoring the glaucomatous patient: wait-and-see approach

Purpose.: Published recommendations suggest three visual field (VF) tests per year are required to identify rapid progression in a newly diagnosed glaucomatous patient over 2 years. This report aims to determine if identification of progression would be improved by clustering tests at the beginning and end of the 2-year period. Methods.: Computer-simulated “patients” were given a rapid VF (mean deviation [MD]) loss of −2 dB/year with added MD measurement variability. Linear regression of MD against time was used to estimate progression. One group of “patients” was measured every 6 months, another every 4 months, whereas the wait-and-see group were measured either 2 or 3 times at both baseline and at the end of a 2-year period. Stable “patients” (0 dB/year) were generated to examine the effect of the follow-up patterns on false-positive (FP) progression identification. Results.: By 2 years, 58% and 82% of rapidly progressing patients were correctly detected using evenly spaced 6- and 4-month VFs, respectively. This power of detection significantly improved to 62% and 95% with the wait-and-see approach (P < 0.001). When compared with evenly spaced VFs, the rate of MD loss was better estimated by the wait-and-see approach, but average detection time was slightly slower. Evenly spaced testing incurred a significantly higher FP rate: up to 5.9% compared with only 0.4% in wait-and-see (P < 0.001). Conclusions.: Compared with an evenly spaced follow-up, wait-and-see identifies more “patients” with rapid VF progression with fewer FPs, making it particularly applicable to clinical trials. Modeling experiments, as reported here, are useful for investigating and optimizing follow-up schemes

Improving statistical power of glaucoma clinical trials using an ensemble of cyclical generative adversarial networks

Albeit spectral-domain OCT (SDOCT) is now in clinical use for glaucoma management, published clinical trials relied on time-domain OCT (TDOCT) which is characterized by low signal-to-noise ratio, leading to low statistical power. For this reason, such trials require large numbers of patients observed over long intervals and become more costly. We propose a probabilistic ensemble model and a cycle-consistent perceptual loss for improving the statistical power of trials utilizing TDOCT. TDOCT are converted to synthesized SDOCT and segmented via Bayesian fusion of an ensemble of GANs. The final retinal nerve fibre layer segmentation is obtained automatically on an averaged synthesized image using label fusion. We benchmark different networks using i) GAN, ii) Wasserstein GAN (WGAN) (iii) GAN + perceptual loss and iv) WGAN + perceptual loss. For training and validation, an independent dataset is used, while testing is performed on the UK Glaucoma Treatment Study (UKGTS), i.e. a TDOCT-based trial. We quantify the statistical power of the measurements obtained with our method, as compared with those derived from the original TDOCT. The results provide new insights into the UKGTS, showing a significantly better separation between treatment arms, while improving the statistical power of TDOCT on par with visual field measurements

How does glaucoma look?: Patient perception of visual field loss

Objective: To explore patient perception of vision loss in glaucoma and, specifically, to test the hypothesis that patients do not recognize their impairment as a black tunnel effect or as black patches in their field of view. Design: Clinic-based cross-sectional study. Participants: Fifty patients (age range, 52-82 years) with visual acuity better than 20/30 and with a range of glaucomatous visual field (VF) defects in both eyes, excluding those with very advanced disease (perimetrically blind). Methods: Participants underwent monocular VF testing in both eyes using a Humphrey Field Analyzer (HFA; Carl Zeiss Meditec, Dublin, CA; 24-2 Swedish interactive threshold algorithm standard tests) and other tests of visual function. Participants took part in a recorded interview during which they were asked if they were aware of their VF loss; if so, there were encouraged to describe it in their own words. Participants were shown 6 images modified in a variety of ways on a computer monitor and were asked to select the image that most closely represented their perception of their VF loss. Main Outcome Measures: Forced choice of an image best representing glaucomatous vision impairment. Results: Participants had a range of VF defect severity: average HFA mean deviation was -8.7 dB (standard deviation [SD], 5.8 dB) and -10.5 dB (SD, 7.1 dB) in the right and left eyes, respectively. Thirteen patients (26%; 95% confidence interval [CI], 15%-40%) reported being completely unaware of their vision loss. None of the patients chose the images with a distinct black tunnel effect or black patches. Only 2 patients (4%; 95% CI, 0%-14%) chose the image with a tunnel effect with blurred edges. An image depicting blurred patches and another with missing patches was chosen by 54% (95% CI, 39%-68%) and 16% (95% CI, 7%-29%) of the patients, respectively. Content analysis of the transcripts from the recorded interviews indicated a frequent use of descriptors of visual symptoms associated with reported blur and missing features. Conclusions: Patients with glaucoma do not perceive their vision loss as a black tunnel effect or as black patches masking their field of view. These findings are important in the context of depicting the effects of glaucomatous vision loss and raising awareness for glaucoma detection. Financial Disclosure(s): The author(s) have no proprietary or commercial interest in any materials discussed in this article

Quantifying discordance between structure and function measurements in the clinical assessment of glaucoma

Objective: The visual field (VF) may be predicted from retinal nerve fibre layer thickness (RNFLT) using a Bayesian Radial Basis Function (BRBF). This study aimed to evaluate a new methodology to quantify and visualise discordance between structural and functional measurements in glaucomatous eyes. Methods: Five GDxVCC RNFLT scans and 5 Humphrey SITA VF tests were obtained from 50 glaucomatous eyes from 50 patients. A best available estimate of the ‘true’ VF was calculated as the point-wise median of these 5 replications. This ‘true’ VF was compared with every single RNFLT-predicted VF from BRBF and every single measured VF. Predictability of VFs from RNFLT was established from previous data. A structure-function pattern discordance map (PDM) and structure-function discordance index (SFDI; values 0 to 1) were established from the predictability limits for each structure-function measurement pair to quantify and visualise the discordance between the structure-predicted and measured VFs. Results: Mean absolute difference (MAD) between the structure-predicted and ‘true’ VFs was 3.9dB. MAD between single and ‘true’ VFs was 2.6dB. Mean of SFDI was 0.34 (SD 0.11). 39% of the structure-predicted VFs showed significant discordance (SFDI>0.3) from measured VFs. Conclusions: BRBF, on average, predicts the ‘true’ VF from RNFLT slightly less well than a measured VF from the 5 VFs compromising the ‘true’ VF. The PDM highlights locations with structure-function discordance, with the SFDI providing a summary index. These tools may help clinicians trust the mutually confirmatory structure-function measurements with good concordance, or identify unreliable ones with poor concordance

Aligning scan acquisition circles in optical coherence tomography images of the retinal nerve fibre layer

Optical coherence tomography (OCT) is widely used in the assessment of retinal nerve fibre layer thickness (RNFLT) in glaucoma. Images are typically acquired with a circular scan around the optic nerve head. Accurate registration of OCT scans is essential for measurement reproducibility and longitudinal examination. This study developed and evaluated a special image registration algorithm to align the location of the OCT scan circles to the vessel features in the retina using probabilistic modelling that was optimised by an expectation-maximization algorithm. Evaluation of the method on 18 patients undergoing large numbers of scans indicated improved data acquisition and better reproducibility of measured RNFLT when scanning circles were closely matched. The proposed method enables clinicians to consider the RNFLT measurement and its scan circle location on the retina in tandem, reducing RNFLT measurement variability and assisting detection of real change of RNFLT in the longitudinal assessment of glaucoma

Diagnostic accuracy of technologies for glaucoma case-finding in a community setting

DESIGN: Cross-sectional, observational, community-based study. PARTICIPANTS: A total of 505 subjects aged ≥60 years recruited from a community setting using no predefined exclusion criteria. METHODS: Subjects underwent 4 index tests conducted by a technician unaware of subjects' ocular status. FDT and MMDT were used in suprathreshold mode. iVue OCT measured ganglion cell complex and retinal nerve fiber layer (RNFL) thickness. Reference standard was full ophthalmic examination by an experienced clinician who was masked to index test results. Subjects were classified as POAG (open drainage angle, glaucomatous optic neuropathy, and glaucomatous field defect), glaucoma suspect, ocular hypertension, or non-POAG/nonocular hypertension. MAIN OUTCOME MEASURES: Test performance evaluated the individual as the unit of analysis. Diagnostic accuracy was assessed using predefined cutoffs for abnormality, generating sensitivity, specificity, and likelihood ratios. Continuous data were used to derive estimates of sensitivity at 90% specificity and partial area under the receiver operating characteristic curve (AUROC) plots from 90% to 100% specificity. RESULTS: From the reference standard examination, 26 subjects (5.1%) had POAG and 32 subjects (6.4%) were glaucoma suspects. Sensitivity (95% confidence interval) at 90% specificity for detection of glaucoma suspect/POAG combined was 41% (28-55) for FDT, 35% (21-48) for MMDT, and 57% (44-70) for best-performing OCT parameter (inferior quadrant RNFL thickness); for POAG, sensitivity was 62% (39-84) for FDT, 58% (37-78) for MMDT, and 83% (68-98) for inferior quadrant RNFL thickness. Partial AUROC was significantly greater for inferior RNFL thickness than visual-function tests (P < 0.001). Post-test probability of glaucoma suspect/POAG combined and definite POAG increased substantially when best-performing criteria were combined for FDT or MMDT, iVue OCT, and ORA. CONCLUSIONS: Diagnostic performance of individual tests gave acceptable accuracy for POAG detection. Low specificity of visual-function tests precludes their use in isolation, but case detection improves by combining RNFL thickness analysis with visual function tests

Evaluation of Visual Field and Imaging Outcomes for Glaucoma Clinical Trials (An American Ophthalomological Society Thesis)

PURPOSE: To evaluate the ability of various visual field (VF) analysis methods to discriminate treatment groups in glaucoma clinical trials and establish the value of time-domain optical coherence tomography (TD OCT) imaging as an additional outcome. METHODS: VFs and retinal nerve fibre layer thickness (RNFLT) measurements (acquired by TD OCT) from 373 glaucoma patients in the UK Glaucoma Treatment Study (UKGTS) at up to 11 scheduled visits over a 2 year interval formed the cohort to assess the sensitivity of progression analysis methods. Specificity was assessed in 78 glaucoma patients with up to 11 repeated VF and OCT RNFLT measurements over a 3 month interval. Growth curve models assessed the difference in VF and RNFLT rate of change between treatment groups. Incident progression was identified by 3 VF-based methods: Guided Progression Analysis (GPA), 'ANSWERS' and 'PoPLR', and one based on VFs and RNFLT: 'sANSWERS'. Sensitivity, specificity and discrimination between treatment groups were evaluated. RESULTS: The rate of VF change was significantly faster in the placebo, compared to active treatment, group (-0.29 vs +0.03 dB/year, P<.001); the rate of RNFLT change was not different (-1.7 vs -1.1 dB/year, P=.14). After 18 months and at 95% specificity, the sensitivity of ANSWERS and PoPLR was similar (35%); sANSWERS achieved a sensitivity of 70%. GPA, ANSWERS and PoPLR discriminated treatment groups with similar statistical significance; sANSWERS did not discriminate treatment groups. CONCLUSIONS: Although the VF progression-detection method including VF and RNFLT measurements is more sensitive, it does not improve discrimination between treatment arms