Optic disc classification by the Heidelberg Retina Tomograph and by physicians with varying experience of glaucoma

PurposeTo compare the diagnostic accuracy of the Heidelberg Retina Tomograph's (HRT) Moorfields regression analysis (MRA) and glaucoma probability score (GPS) with that of subjective grading of optic disc photographs performed by ophthalmologists with varying experience of glaucoma and by ophthalmology residents.MethodsDigitized disc photographs and HRT images from 97 glaucoma patients with visual field defects and 138 healthy individuals were classified as either within normal limits (WNL), borderline (BL), or outside normal limits (ONL). Sensitivity and specificity were compared for MRA, GPS, and the physicians. Analyses were also made according to disc size and for advanced visual field loss.ResultsForty-five physicians participated. When BL results were regarded as normal, sensitivity was significantly higher (P<5%) for both MRA and GPS compared with the average physician, 87%, 79%, and 62%, respectively. Specificity ranged from 86% for MRA to 97% for general ophthalmologists, but the differences were not significant. In eyes with small discs, sensitivity was 75% for MRA, 60% for the average doctor, and 25% for GPS; in eyes with large discs, sensitivity was 100% for both GPS and MRA, but only 68% for physicians.ConclusionOur results suggest that sensitivity of MRA is superior to that of the average physician, but not that of glaucoma experts. MRA correctly classified all eyes with advanced glaucoma and showed the best sensitivity in eyes with small optic discs

Development and clinical deployment of a smartphone-based visual field deep learning system for glaucoma detection

By 2040, ~100 million people will have glaucoma. To date, there are a lack of high-efficiency glaucoma diagnostic tools based on visual fields (VFs). Herein, we develop and evaluate the performance of ‘iGlaucoma’, a smartphone application-based deep learning system (DLS) in detecting glaucomatous VF changes. A total of 1,614,808 data points of 10,784 VFs (5542 patients) from seven centers in China were included in this study, divided over two phases. In Phase I, 1,581,060 data points from 10,135 VFs of 5105 patients were included to train (8424 VFs), validate (598 VFs) and test (3 independent test sets—200, 406, 507 samples) the diagnostic performance of the DLS. In Phase II, using the same DLS, iGlaucoma cloud-based application further tested on 33,748 data points from 649 VFs of 437 patients from three glaucoma clinics. With reference to three experienced expert glaucomatologists, the diagnostic performance (area under curve [AUC], sensitivity and specificity) of the DLS and six ophthalmologists were evaluated in detecting glaucoma. In Phase I, the DLS outperformed all six ophthalmologists in the three test sets (AUC of 0.834–0.877, with a sensitivity of 0.831–0.922 and a specificity of 0.676–0.709). In Phase II, iGlaucoma had 0.99 accuracy in recognizing different patterns in pattern deviation probability plots region, with corresponding AUC, sensitivity and specificity of 0.966 (0.953–0.979), 0.954 (0.930–0.977), and 0.873 (0.838–0.908), respectively. The ‘iGlaucoma’ is a clinically effective glaucoma diagnostic tool to detect glaucoma from humphrey VFs, although the target population will need to be carefully identified with glaucoma expertise input

Scanning Laser Ophthalmoscopy (SLO)

Since the first scanning laser ophthalmoscope (SLO) was introduced in the early 1980s, this imaging technique has been adapted and optimized for various clinical applications based on different contrast mechanism. Reflectance imaging, where the back scattered light is detected, is widely used for eye tracking and as reference image for OCT applications. But also the reflectance modality itself has several important diagnostic applications: laser scanning tomography (SLT), imaging with different laser wavelengths (Multicolor contrast) and others. Fluorescence imaging channels with different excitation wavelengths were introduced to SLOs for angiography, i.e. for the visualization of the vascular system after intravenously injecting an appropriate dye, as well as for autofluorescence imaging of endogenous fluorophores within the retina

Estimating lead time gained by optical coherence tomography in detecting glaucoma before development of visual field defects

Purpose To estimate the diagnostic accuracy and lead time gained by retinal nerve fiber layer (RNFL) thickness measurements from optical coherence tomography (OCT) for detecting glaucoma before the development of visual field defects. Design Observational cohort study. Participants The study group included 75 eyes of 75 patients suspected of having glaucoma. These eyes had normal standard automated perimetry (SAP) at baseline and demonstrated repeatable (3 consecutive) abnormal tests during a median follow-up of 6.3 years. A control group of 75 eyes of 75 healthy subjects matched by age and number of OCT tests during follow-up was included. Methods The RNFL thickness measurements were obtained at the time of development of the earliest SAP defect (time 0) and also at times -1, -2, -3, and so forth, corresponding to 1 year, 2 years, 3 years, and so forth, before the development of field loss. The OCT measurements at corresponding intervals were analyzed for controls. Time-dependent receiver operating characteristic (ROC) curves were used to evaluate diagnostic accuracy of OCT. Main Outcome Measures Areas and sensitivities of ROC curve at fixed specificities at different times before development of field loss. Results At the date of conversion to the earliest visual field defect (time 0), mean ± standard deviation average RNFL thickness was 75.0±9.8 μm in glaucomatous eyes and 90.6±8.0 μm for controls (P &lt; 0.001). Significant differences were seen up to 8 years before development of visual field defects (86.3±8.2 μm vs. 91.4±7.6 μm, respectively; P = 0.021). The ROC curve areas decreased with increasing times before detection of field defects. At times 0, -4, and -8 years, ROC curve areas were 0.87, 0.77, and 0.65, respectively. At 95% specificity, up to 35% of eyes had abnormal average RNFL thickness 4 years before development of visual field loss and 19% of eyes had abnormal results 8 years before field loss. Conclusions Assessment of RNFL thickness with OCT was able to detect glaucomatous damage before the appearance of visual field defects on SAP. In many subjects, significantly large lead times were seen when applying OCT as an ancillary diagnostic tool

Estimating lead time gained by optical coherence tomography in detecting glaucoma before development of visual field defects

Purpose To estimate the diagnostic accuracy and lead time gained by retinal nerve fiber layer (RNFL) thickness measurements from optical coherence tomography (OCT) for detecting glaucoma before the development of visual field defects. Design Observational cohort study. Participants The study group included 75 eyes of 75 patients suspected of having glaucoma. These eyes had normal standard automated perimetry (SAP) at baseline and demonstrated repeatable (3 consecutive) abnormal tests during a median follow-up of 6.3 years. A control group of 75 eyes of 75 healthy subjects matched by age and number of OCT tests during follow-up was included. Methods The RNFL thickness measurements were obtained at the time of development of the earliest SAP defect (time 0) and also at times -1, -2, -3, and so forth, corresponding to 1 year, 2 years, 3 years, and so forth, before the development of field loss. The OCT measurements at corresponding intervals were analyzed for controls. Time-dependent receiver operating characteristic (ROC) curves were used to evaluate diagnostic accuracy of OCT. Main Outcome Measures Areas and sensitivities of ROC curve at fixed specificities at different times before development of field loss. Results At the date of conversion to the earliest visual field defect (time 0), mean ± standard deviation average RNFL thickness was 75.0±9.8 μm in glaucomatous eyes and 90.6±8.0 μm for controls (P &lt; 0.001). Significant differences were seen up to 8 years before development of visual field defects (86.3±8.2 μm vs. 91.4±7.6 μm, respectively; P = 0.021). The ROC curve areas decreased with increasing times before detection of field defects. At times 0, -4, and -8 years, ROC curve areas were 0.87, 0.77, and 0.65, respectively. At 95% specificity, up to 35% of eyes had abnormal average RNFL thickness 4 years before development of visual field loss and 19% of eyes had abnormal results 8 years before field loss. Conclusions Assessment of RNFL thickness with OCT was able to detect glaucomatous damage before the appearance of visual field defects on SAP. In many subjects, significantly large lead times were seen when applying OCT as an ancillary diagnostic tool

Evaluation of baseline structural factors for predicting glaucomatous visual-field progression using optical coherence tomography, scanning laser polarimetry and confocal scanning laser ophthalmoscopy

PURPOSE: The objective of this study is to assess whether baseline optic nerve head (ONH) topography and retinal nerve fiber layer thickness (RNFLT) are predictive of glaucomatous visual-field progression in glaucoma suspect (GS) and glaucomatous eyes, and to calculate the level of risk associated with each of these parameters. METHODS: Participants with ≥28 months of follow-up were recruited from the longitudinal Advanced Imaging for Glaucoma Study. All eyes underwent standard automated perimetry (SAP), confocal scanning laser ophthalmoscopy (CSLO), time-domain optical coherence tomography (TDOCT), and scanning laser polarimetry using enhanced corneal compensation (SLPECC) every 6 months. Visual-field progression was assessed using pointwise linear-regression analysis of SAP sensitivity values (progressor) and defined as significant sensitivity loss of >1 dB/year at ≥2 adjacent test locations in the same hemifield at P<0.01. Cox proportional hazard ratios (HR) were calculated to determine the predictive ability of baseline ONH and RNFL parameters for SAP progression using univariate and multivariate models. RESULTS: Seventy-three eyes of 73 patients (43 GS and 30 glaucoma, mean age 63.2±9.5 years) were enrolled (mean follow-up 51.5±11.3 months). Four of 43 GS (9.3%) and 6 of 30 (20%) glaucomatous eyes demonstrated progression. Mean time to progression was 50.8±11.4 months. Using multivariate models, abnormal CSLO temporal-inferior Moorfields classification (HR=3.76, 95% confidence interval (CI): 1.02–6.80, P=0.04), SLPECC inferior RNFLT (per −1 μm, HR=1.38, 95% CI: 1.02–2.2, P=0.02), and TDOCT inferior RNFLT (per −1 μm, HR=1.11, 95% CI: 1.04–1.2, P=0.001) had significant HRs for SAP progression. CONCLUSION: Abnormal baseline ONH topography and reduced inferior RNFL are predictive of SAP progression in GS and glaucomatous eyes

Glaucomatous Retinal Nerve Fiber Layer Thickness Loss Is Associated With Slower Reaction Times Under a Divided Attention Task

Purpose: To examine the relationship between glaucomatous structural damage and ability to divide attention during simulated driving. Design: Cross-sectional observational study. Methods: setting : Hamilton Glaucoma Center, University of California San Diego. patient population : Total of 158 subjects from the Diagnostic Innovations in Glaucoma Study, including 82 with glaucoma and 76 similarly aged controls. observation procedure : Ability to divide attention was investigated by measuring reaction times to peripheral stimuli (at low, medium, or high contrast) while concomitantly performing a central driving task (car following or curve negotiation). All subjects had standard automated perimetry (SAP) and optical coherence tomography was used to measure retinal nerve fiber layer (RNFL) thickness. Cognitive ability was assessed using the Montreal Cognitive Assessment and subjects completed a driving history questionnaire. main outcome measures : Reaction times to the driving simulator divided attention task. Results: The mean reaction times to the low-contrast stimulus were 1.05&nbsp;s and 0.64&nbsp;s in glaucoma and controls, respectively, during curve negotiation (P &lt; .001), and 1.19&nbsp;s and 0.77&nbsp;s (P&nbsp;= .025), respectively, during car following. There was a nonlinear relationship between reaction times and RNFL thickness in the better eye. RNFL thickness remained significantly associated with reaction times even after adjusting for age, SAP mean deviation in the better eye, cognitive ability, and central driving task performance. Conclusions: Although worse SAP sensitivity was associated with worse ability to divide attention, RNFL thickness measurements provided additional information. Information from structural tests may improve our ability to determine which patients are likely to have problems performing daily activities, such as driving. © 2014 Elsevier Inc. All rights reserved

Learning from data: Recognizing glaucomatous defect patterns and detecting progression from visual field measurements

A hierarchical approach to learn from visual field data was adopted to identify glaucomatous visual field defect patterns and to detect glaucomatous progression. The analysis pipeline included three stages, namely, clustering, glaucoma boundary limit detection, and glaucoma progression detection testing. First, cross-sectional visual field tests collected from each subject were clustered using a mixture of Gaussians and model parameters were estimated using expectation maximization. The visual field clusters were further estimated to recognize glaucomatous visual field defect patterns by decomposing each cluster into several axes. The glaucoma visual field defect patterns along each axis then were identified. To derive a definition of progression, the longitudinal visual fields of stable glaucoma eyes on the abnormal cluster axes were projected and the slope was approximated using linear regression (LR) to determine the confidence limit of each axis. For glaucoma progression detection, the longitudinal visual fields of each eye on the abnormal cluster axes were projected and the slope was approximated by LR. Progression was assigned if the progression rate was greater than the boundary limit of the stable eyes; otherwise, stability was assumed. The proposed method was compared to a recently developed progression detection method and to clinically available glaucoma progression detection software. The clinical accuracy of the proposed pipeline was as good as or better than the currently available methods. © 1964-2012 IEEE