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In this manuscript we develop a deep learning algorithm to improve estimation of rates of progression and prediction of future patterns of visual field loss in glaucoma. A generalized variational auto-encoder (VAE) was trained to learn a low-dimensional representation of standard automated perimetry (SAP) visual fields using 29,161 fields from 3,832 patients. The VAE was trained on a 90% sample of the data, with randomization at the patient level. Using the remaining 10%, rates of progression and predictions were generated, with comparisons to SAP mean deviation (MD) rates and point-wise (PW) regression predictions, respectively. The longitudinal rate of change through the VAE latent space (e.g., with eight dimensions) detected a significantly higher proportion of progression than MD at two (25% vs. 9%) and four (35% vs 15%) years from baseline. Early on, VAE improved prediction over PW, with significantly smaller mean absolute error in predicting the 4 th , 6 th and 8 th visits from the first three (e.g., visit eight: VAE8: 5.14 dB vs. PW: 8.07 dB; P < 0.001). A deep VAE can be used for assessing both rates and trajectories of progression in glaucoma, with the additional benefit of being a generative technique capable of predicting future patterns of visual field damage.

BackgroundMacular Integrity Assessment (MAIA) microperimetry is increasingly used in clinical and research settings to assess point retinal sensitivity and fixation stability. Testing occurs under mesopic conditions, commonly after a period of dark adaptation. Our aim was to identify the minimum length of adaptation required to optimise microperimetry performance.MethodsMAIA microperimetry using the 10-2 grid was performed on 40 right eyes of 40 healthy participants aged 18–73 with no ocular pathology and vision of at least 0.1 logMAR after ambient light exposure, with 0, 5, 10, 15, 20 and 30 min of adaptation in mesopic settings. Ten right eyes of 10 participants with choroideremia were also tested following 0 and 20 min of adaptation. We further tested 10 right eyes of 10 healthy participants after bright light exposure, with 0, 10 and 20 min of adaptation. We compared changes in threshold sensitivity and fixation stability across time points.ResultsMicroperimetry performance did not improve with increasing adaptation time in healthy participants or patients with choroideremia after ambient light exposure. After bright light exposure, we found microperimetry thresholds improved after 10 min of adaptation, but did not improve further at 20 min.ConclusionMesopic adaptation is not required before MAIA microperimetry after exposure to ambient light. Ten minutes of adaptation is sufficient after exposure to a bright light stimulus, such as ophthalmoscopy or retinal imaging. The brief time of dark adaptation required corresponds to cone adaptation curves and provides further evidence for cone-mediated central retinal function under mesopic conditions.

This study compared between TEMPO, a new binocular perimeter, with the Humphrey Field Analyzer (HFA). Patients were tested with both TEMPO 24–2 Ambient Interactive Zippy Estimated by Sequential Testing (AIZE)-Rapid and HFA 24–2 Swedish Interactive Threshold Algorithm (SITA)-Fast in a randomized sequence on the same day. Using a mixed-effects model, visual field (VF) parameters and reliability indices were compared. Retinal nerve fiber layer (RNFL) thickness was measured using Cirrus optical coherence tomography (OCT), and coefficient of determinations for VF and OCT parameters were calculated and compared using Akaike information criteria. 740 eyes (including 68 healthy, 262 glaucoma suspects, and 410 glaucoma) of 370 participants were evaluated. No significant differences were seen in mean deviation and visual field index between the two perimeters (P > 0.05). A stronger association between VF mean sensitivity (dB or 1/L) and circumpapillary RNFL was found for TEMPO (adjusted R 2  = 0.25; Akaike information criteria [AIC] = 5235.5 for dB, and adjusted R 2  = 0.29; AIC = 5200.8 for 1/L, respectively) compared to HFA (adjusted R 2  = 0.22; AIC = 5263.9 for dB, and adjusted R 2  = 0.22; AIC = 5262.7 for 1/L, respectively). Measurement time was faster for TEMPO compared to HFA (261 s vs. 429 s, P < 0.001). Further investigations are needed to assess the long-term monitoring potential of this binocular VF test.

Glaucoma is a neurodegenerative disease, our study aimed to evaluate the potential effects of Palmitoylethanolamide (PEA) supplementation on RGCs function by PERG examination, and to record effects on intraocular pressure, visual field and quality of life. It was a single centre, randomized, prospective, single blind, two treatment, two period crossover study on stable glaucoma patients on topical monotherapy comparing current topical therapy alone or additioned with PEA 600 mg one tablet a day. At baseline, at 4 and at 8 months, all patients underwent to complete ophthalmic examination, pattern electroretinogram, visual field, and quality of life evaluation. 40 patients completed the study: mean age 66.6 ± 7.6 years; 21 (52.5%) male; 35 POAG (87.5%). At baseline, most patients had an early visual field defect, the IOP was well controlled. At the end of the PEA 600 mg supplementation, a significantly higher (mean 0.56 μV, 95% CI 0.30–0.73, p < 0.001) in the P50-wave amplitude was observed; in the PEA period a significantly lower IOP (− 1.6 mmHg, 95% CI − 2 to 1.2, p < 0.001) and higher quality of life scores (+ 6.7, 95% CI 4–9.9, p < 0.001) were observed. Our study is the first to show promising effects of PEA on PERG and on quality of life in glaucoma patients.

The aims of this randomized observational case control study were to quantify fixation behavior during standard automated perimetry (SAP) with different fixation targets and to evaluate the relationship between fixation behavior and threshold variability at each test point in healthy young participants experienced with perimetry. SAP was performed on the right eyes of 29 participants using the Octopus 900 perimeter, program 32, dynamic strategy. The fixation targets of Point, Cross, and Ring were used for SAP. Fixation behavior was recorded using a wearable eye-tracking glass. All participants underwent SAP twice with each fixation target in a random fashion. Fixation behavior was quantified by calculating the bivariate contour ellipse area (BCEA) and the frequency of deviation from the fixation target. The BCEAs (deg2) of Point, Cross, and Ring targets were 1.11, 1.46, and 2.02, respectively. In all cases, BCEA increased significantly with increasing fixation target size (p < 0.05). The logarithmic value of BCEA demonstrated the same tendency (p < 0.05). A positive correlation was identified between fixation behavior and threshold variability for the Point and Cross targets (ρ = 0.413-0.534, p < 0.05). Fixation behavior increased with increasing fixation target size. Moreover, a larger fixation behavior tended to be associated with a higher threshold variability. A small fixation target is recommended during the visual field test.

Purpose: To compare the effect of audiovisual and verbal instructions on patient performance while performing automated Humphrey visual field testing. Methods: This was a prospective study. A total 120 patients divided into groups of 40 each were recruited from the glaucoma outpatient department (OPD). All patients were aged 35-75 years with no previous experience of performing HFA. Patients with hearing impairment, any other cognitive impairment, and best-corrected visual acuity (BCVA) ≤6/36 on Snellen's visual acuity were excluded. The first two groups were given strict (conservative) and lenient (liberal) verbal instructions. The instructions were adapted from those listed in the manufacturer's instruction. and the third group was shown a standard video depicting in detail how perimetry was to be performed. A questionnaire was given to each patient before and after the test to assess the patient's performance. Results: Patients diagnosed with glaucoma during testing in each group were 29 (72.50%), 30 (75.0%), and 33 (82.5%) in the video instructed, strictly verbal, and leniently verbal groups, respectively. The overall mean deviation (MD) in the right eye (RE) was of − 3.38 (−4.9 to 1.9) and in the left eye (LE) was − 3.96 (−6.4 to − 1.9). Reliable field was slightly higher for the video instructed group (47.5%) and lowest for the strictly verbal group (22.5%) (P = 0.033). A higher number of patients were very motivated in the video instructed group (27%) (P = 0.041). Post-test questionnaires showed that 40% of patients felt they have performed the test with 100% accuracy in video group with less guessing. A higher number of patients in the video instructed group (85%) felt instruction was helpful in performing the test (P = 0.001). Conclusion: The video groups were more motivated and had better confidence to perform the test with less anxiety and stress and with probably better understanding of the procedure due to visual effects enhancing their understanding.

To explore the feasibility of using Saccadic Vector Optokinetic Perimetry (SVOP) to differentiate glaucomatous and healthy eyes. A prospective case–control study was performed using a convenience sample recruited from a single university glaucoma clinic and a group of healthy controls. SVOP and standard automated perimetry (SAP) was performed with testing order randomised. The reference standard was a diagnosis of glaucoma based a comprehensive ophthalmic examination and abnormality on standard automated perimetry (SAP). The index test was SVOP. 31 patients with glaucoma and 24 healthy subjects were included. Mean SAP mean deviation (MD) in those with glaucoma was − 8.7 ± 7.4 dB, with mean SAP and SVOP sensitivities of 23.3 ± 0.9 dB and 22.1 ± 4.3 dB respectively. Participants with glaucoma were significantly older. On average, SAP sensitivity was 1.2 ± 1.4 dB higher than SVOP (95% limits of agreement = − 1.6 to 4.0 dB). SVOP sensitivity had good ability to differentiate healthy and glaucomatous eyes with a 95% CI for area under the curve (AUC) of 0.84 to 0.96, similar to the performance of SAP sensitivity (95% CI 0.86 to 0.97, P = 0.60). For 80% specificity, SVOP had a 95% CI sensitivity of 75.7% to 94.8% compared to 77.8% to 96.0% for SAP. SVOP took considerably longer to perform (514 ± 54 s compared to 267 ± 76 s for SAP). Eye tracking perimetry may be useful for detection of glaucoma but further studies are needed to evaluate SVOP within its intended sphere of use, using an appropriate design and independent reference standard.

Background: Use of the antiepileptic drug vigabatrin is associated with an elevated risk of visual field loss. Objective: To determine the frequency of, and risk factors for, vigabatrin-attributed visual field loss (VAVFL) in the setting of a large-scale, multinational, prospective, observational study. Study design: A comparative, open-label, parallel-group, multicentre study. Setting: Hospital outpatient clinics at 46 centres in five countries. Patients: 734 patients with refractory partial epilepsy, divided into three groups and stratified by age (8–12 years; >12 years) and exposure to vigabatrin. Group I comprised patients treated with vigabatrin for ≥6 months. Group II comprised patients previously treated with vigabatrin for ≥6 months who had withdrawn from the drug for ≥6 months. Group III comprised patients never treated with vigabatrin. Patients underwent perimetry at either 4- or 6-month intervals, for up to 36 months. Visual field outcome was evaluated masked to drug exposure. Intervention: Perimetry. Main outcome measure: The visual field outcome at each of four analysis points: (i) at enrolment (i.e. baseline, all patients); (ii) for patients exhibiting a conclusive outcome at the initial visual field examination; (iii) for patients exhibiting at least one conclusive outcome to the visual field examinations; and (iv) at the last conclusive outcome to the visual field examinations. Results: Of the 734 patients, 524 yielded one or more conclusive visual field examinations. For Group I, the frequency of VAVFL at the last conclusive examination was 10/38 (26.3%) for those aged 8–12 years and 65/150 (43.3%) for those aged >12 years. For Group II, the respective frequencies were 7/47 (14.9%) and 37/151 (24.5%). One case resembling VAVFL was present amongst the 186 patients in Group III at the last conclusive examination. The frequency of VAVFL in Groups I and II combined was 20.0% for those aged 8–12 years and 33.9% for those aged >12 years. VAVFL was associated with duration of vigabatrin therapy (odds ratio [OR] up to 15.2; 95% CI 4.4, 51.7), mean daily dose of vigabatrin (OR up to 26.4; 95% CI 2.4, 291.7) and male gender (OR 2.51; 95% CI 1.5, 4.1). VAVFL was more frequently detected with static than with kinetic perimetry (OR up to 0.43; 95% CI 0.24, 0.75). Conclusions: Since the probability of VAVFL is positively associated with treatment duration, careful assessment of the risk-benefit ratio of continuing treatment with vigabatrin is recommended in patients currently receiving this drug. All patients continuing to receive vigabatrin should undergo visual field examination at least every 6 months for the duration of treatment. We recommend two-level (three-zone), gradient-adapted, suprathreshold static perimetry of the peripheral field together with threshold perimetry of the central field out to 30° from fixation. The frequency of ophthalmological and perimetric examinations should be increased in the presence of VAVFL.

AimTo examine the level of agreement among nine clinicians in assessing progressive deterioration in visual field (VF) overview using three different methods of analysis.MethodsEach visual field was assessed by Humphrey Field Analyzer (HFA), program 24-2 SITA Standard. Nine expert clinicians assessed the progression status of each series by using HFA ‘overview printouts’ (HFA OP), the Guided Progression Analysis (GPA) and the Guided Progression Analysis (GPA2). VF series were presented in random order, but each patient's VF remained in chronological order within a given field series. Each clinician adopted his personal methods based on his knowledge to evaluate VF progression. The level of agreement between the clinicians was evaluated by using weighted κ statistics.ResultsA total of 303 tests, comprising 38 visual field series of 7.9±3.4 tests (mean±SD), were assessed by the nine glaucoma specialists. When the intra-observer agreement was evaluated between HFA OP and GPA, the mean κ statistic was 0.58±0.13, between HFA OP and GPA2, κ was 0.55±0.06 and between GPA and GPA2 it was 0.56±0.17. When the inter-observer agreement was analysed κ statistic was 0.65 for HFA OP, 0.54 for GPA and 0.70 for GPA2.ConclusionsUsing any procedure for evaluating the progression of a series of VF, agreement between expert clinicians is moderate. Clinicians had higher agreement when GPA2 was used, followed by HFA OP and GPA printouts, but these differences were not significant.

To determine if deep learning networks could be trained to forecast future 24-2 Humphrey Visual Fields (HVFs). All data points from consecutive 24-2 HVFs from 1998 to 2018 were extracted from a university database. Ten-fold cross validation with a held out test set was used to develop the three main phases of model development: model architecture selection, dataset combination selection, and time-interval model training with transfer learning, to train a deep learning artificial neural network capable of generating a point-wise visual field prediction. The point-wise mean absolute error (PMAE) and difference in Mean Deviation (MD) between predicted and actual future HVF were calculated. More than 1.7 million perimetry points were extracted to the hundredth decibel from 32,443 24-2 HVFs. The best performing model with 20 million trainable parameters, CascadeNet-5, was selected. The overall point-wise PMAE for the test set was 2.47 dB (95% CI: 2.45 dB to 2.48 dB), and deep learning showed a statistically significant improvement over linear models. The 100 fully trained models successfully predicted future HVFs in glaucomatous eyes up to 5.5 years in the future with a correlation of 0.92 between the MD of predicted and actual future HVF and an average difference of 0.41 dB. Using unfiltered real-world datasets, deep learning networks show the ability to not only learn spatio-temporal HVF changes but also to generate predictions for future HVFs up to 5.5 years, given only a single HVF.