Publications
Publications by year in reversed chronological order.
2025
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Chris Bradley, Alex Pham, and Jithin YohannanAJO international Jul 2025OBJECTIVE: Determine how sensitivities below the measurement floor of the Humphrey Field Analyzer change when transitioning from Swedish Interactive Thresholding Algorithm (SITA) Standard to SITA-Fast and SITA-Faster strategies. DESIGN: Retrospective descriptive study. PARTICIPANTS: A total of 21,468 24-2 SITA-Standard, 4872 SITA-Fast and 3468 SITA-Faster VFs from 7917 glaucoma and glaucoma suspect eyes with at least 5 VFs between 1997 and 2023 at the Wilmer Eye Institute. METHODS: At each test location of the 24-2 test pattern, we measured the probability that \textless0 dB at a given test location on two baseline SITA-Standard VFs was M dB or higher on the first SITA-Fast or SITA-Faster post-baseline VF for different values of M \textgreater 0. Results were compared to using the same test strategy for both baseline and post-baseline VFs. MAIN OUTCOME MEASURES: Probability of \textless0 dB at baseline being measured as M \textgreater 0 dB or higher on the first post-baseline VF. RESULTS: At M = 7 dB, which was approximately one standard deviation above the mean for post-baseline SITA-Standard sensitivities, average percent change from \textless0 dB across all test locations was 10.3 % for SITA-Standard, 15.8 % for SITA-Fast and 25.5 % for SITA-Faster. Percent change from \textless0 dB for all M tested (up to M = 20) was consistently higher near the macula compared to overall averages: on average 1.3 % higher for SITA-Standard, 1.5 % higher for SITA-Fast, and 6.3 % higher for SITA-Faster. CONCLUSIONS: Increased caution is advised when following the progression of \textless0 dB defects during a transition from SITA-Standard to SITA-Fast or SITA-Faster.
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Alex T. Pham, Chris Bradley, Kaihua Hou, and 2 more authorsScientific Reports Feb 2025Multiple glaucoma studies have attempted to generate visual field (VF) mean deviation (MD) estimates using cross-sectional optical coherence tomography (OCT) data. However, whether such models offer any value in detecting longitudinal VF progression is unclear. We address this by developing a machine learning (ML) model to convert OCT data to MD and assessing its ability to detect longitudinal worsening. In this study, we created a model dataset of 70,575 paired OCT/VFs to train an ML model to convert OCT to VF-MD. We created a separate progression dataset of 4,044 eyes with ≥ 5 paired OCT/VFs to assess the ability of OCT-derived MD to detect worsening. The progression dataset eyes had 2 additional unpaired VFs (≥ 7 total) to establish a “ground truth” rate of progression defined by MD slope. We used the ML model to generate longitudinal OCT-MD estimates for each OCT scan for progression dataset eyes. We calculated MD slopes after substituting/supplementing VF-MD with OCT-MD and measured the ability to detect progression. We labeled true progressors using a ground truth MD slope \textless 0.5 dB/year calculated from ≥ 7 VF-MD measurements. We compared the area under the curve (AUC) of MD slopes calculated using both VF-MD (with \textless 7 measurements) and OCT-MD. Because we found OCT-MD substitution had a statistically inferior AUC to VF-MD, we simulated the effect of reducing OCT-MD mean absolute error (MAE) on the ability to detect worsening. Our model’s OCT-MD estimates had an MAE of 1.62 dB (better than that of any previously published models). However, we found the AUC of MD slopes with partial OCT-MD substitution was significantly worse than the VF-MD slope. Supplementing VF-MD with OCT-MD also did not improve AUC, regardless of MAE. We found that OCT-MD estimates needed an MAE ≤ 1.00 dB before AUC was statistically similar to VF-MD alone. Overall, our ML model converting OCT data to VF-MD had error levels lower than those published in prior work and was inferior to VF-MD data for detecting trend-based VF progression. Our data suggest that future models converting OCT data to VF-MD must achieve better prediction errors (MAE ≤ 1 dB) to be clinically valuable at detecting VF worsening.
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Jeremy C. K. Tan, Jithin Yohannan, Pradeep Y. Ramulu, and 4 more authorsSurvey of Ophthalmology Feb 2025The Swedish Interactive Thresholding Algorithm (SITA) is the main measurement acquisition algorithm used on the Humphrey Field Analyser, the most commonly used instrument for visual field (VF) assessment worldwide. We compare the sensitivity outputs and reliability parameters of the three currently available SITA algorithms-SITA Standard (SS), Fast (SF), and Faster (SFR), with a focus on the newly released SFR and the 24-2C test grid. SFR displays similar sensitivity outputs to SS and SF, but may not be interchangeable with SS in eyes with more severe VF loss. The reliability metric with the greatest impact on VF reliability is the level of false positives, although the recommended 15 % false positive cut off may be inappropriate as a threshold for judging whether a test is reliable and should be included for use in SFR. Finally, the 24-2C grid may be useful in flagging the presence of a clustered central VF defect, while the 10-2 grid can be used to more comprehensively characterize central field defects. We also discuss strategies to improve testing frequency in clinical practice.
2024
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Alex T. Pham, Annabelle A. Pan, and Jithin YohannanTaiwan Journal of Ophthalmology Sep 2024Recent technological advancements and the advent of ever-growing databases in health care have fueled the emergence of “big data” analytics. Big data has the potential to revolutionize health care, particularly ophthalmology, given the data-intensive nature of the medical specialty. As one of the leading causes of irreversible blindness worldwide, glaucoma is an ocular disease that receives significant interest for developing innovations in eye care. Among the most vital sources of data in glaucoma is visual field (VF) testing, which stands as a cornerstone for diagnosing and managing the disease. The expanding accessibility of large VF databases has led to a surge in studies investigating various applications of big data analytics in glaucoma. In this study, we review the use of big data for evaluating the reliability of VF tests, gaining insights into real-world clinical practices and outcomes, understanding new disease associations and risk factors, characterizing the patterns of VF loss, defining the structure–function relationship of glaucoma, enhancing early diagnosis or earlier detection of progression, informing clinical decisions, and improving clinical trials. Equally important, we discuss current challenges in big data analytics and future directions for improvement.
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Alex T. Pham, Annabelle A. Pan, Chris Bradley, and 5 more authorsTranslational Vision Science & Technology Aug 2024PURPOSE: Compare the use of optic disc and macular optical coherence tomography measurements to predict glaucomatous visual field (VF) worsening. METHODS: Machine learning and statistical models were trained on 924 eyes (924 patients) with circumpapillary retinal nerve fiber layer (cp-RNFL) or ganglion cell inner plexiform layer (GC-IPL) thickness measurements. The probability of 24-2 VF worsening was predicted using both trend-based and event-based progression definitions of VF worsening. Additionally, the cp-RNFL and GC-IPL predictions were combined to produce a combined prediction. A held-out test set of 617 eyes was used to calculate the area under the curve (AUC) to compare cp-RNFL, GC-IPL, and combined predictions. RESULTS: The AUCs for cp-RNFL, GC-IPL, and combined predictions with the statistical and machine learning models were 0.72, 0.69, 0.73, and 0.78, 0.75, 0.81, respectively, when using trend-based analysis as ground truth. The differences in performance between the cp-RNFL, GC-IPL, and combined predictions were not statistically significant. AUCs were highest in glaucoma suspects using cp-RNFL predictions and highest in moderate/advanced glaucoma using GC-IPL predictions. The AUCs for the statistical and machine learning models were 0.63, 0.68, 0.69, and 0.72, 0.69, 0.73, respectively, when using event-based analysis. AUCs decreased with increasing disease severity for all predictions. CONCLUSIONS: cp-RNFL and GC-IPL similarly predicted VF worsening overall, but cp-RNFL performed best in early glaucoma stages and GC-IPL in later stages. Combining both did not enhance detection significantly. TRANSLATIONAL RELEVANCE: cp-RNFL best predicted trend-based 24-2 VF progression in early-stage disease, while GC-IPL best predicted progression in late-stage disease. Combining both features led to minimal improvement in predicting progression.
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Louay Almidani, Chris Bradley, Patrick Herbert, and 2 more authorsOphthalmology. Glaucoma Aug 2024PURPOSE: To determine the associations between social vulnerability index (SVI) and baseline severity, worsening, and variability of glaucoma, as assessed by visual field (VF) and OCT. DESIGN: Retrospective longitudinal cohort study. PARTICIPANTS: Adults with glaucoma or glaucoma suspect status in 1 or both eyes. Visual fields were derived from 7897 eyes from 4482 patients, while OCTs were derived from 6271 eyes from 3976 patients. All eyes had a minimum of 5 tests over follow-up using either the Humphrey Field Analyzer or the Cirrus HD-OCT. METHODS: Social vulnerability index, which measures neighborhood-level environmental factors, was linked to patients’ addresses at the census tract level. Rates of change in mean deviation (MD) and retinal nerve fiber layer (RNFL) thickness were computed using linear regression. The slope of the regression line was used to assess worsening, while the standard deviation of residuals was used as a measure of variability. Multivariable linear mixed-effects models were used to investigate the impact of SVI on baseline, worsening, and variability in both MD and RNFL. We further explored the interaction effect of mean intraocular pressure (IOP) and SVI on worsening in MD and RNFL. MAIN OUTCOME MEASURES: Glaucoma severity defined based on baseline MD and RNFL thickness. Worsening defined as MD and RNFL slope. Variability defined as the standard deviation of the residuals obtained from MD and RNFL slopes. RESULTS: Increased (worse) SVI was significantly associated with worse baseline MD (β = -1.07 dB, 95% confidence interval [CI]: [-1.54, -0.60]), thicker baseline RNFL (β = 2.46 μm, 95% CI: [0.75, 4.17]), greater rates of RNFL loss (β = -0.12 μm, 95% CI: [-0.23, -0.02]), and greater VF variability (β = 0.16 dB, 95% CI: [0.07, 0.24]). Having worse SVI was associated with worse RNFL loss with increases in IOP (βinteraction = -0.07, 95% CI: [-0.12, -0.02]). CONCLUSIONS: Increased SVI score is associated with worse functional (VF) loss at baseline, higher rates of structural (OCT) worsening over time, higher VF variability, and a greater effect of IOP on RNFL loss. Further studies are needed to enhance our understanding of these relationships and establish their cause. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Chris Bradley, Kaihua Hou, Patrick Herbert, and 5 more authorsPloS One Aug 2024Linear regression of optical coherence tomography measurements of peripapillary retinal nerve fiber layer thickness is often used to detect glaucoma progression and forecast future disease course. However, current measurement frequencies suggest that clinicians often apply linear regression to a relatively small number of measurements (e.g., less than a handful). In this study, we estimate the accuracy of linear regression in predicting the next reliable measurement of average retinal nerve fiber layer thickness using Zeiss Cirrus optical coherence tomography measurements of average retinal nerve fiber layer thickness from a sample of 6,471 eyes with glaucoma or glaucoma-suspect status. Linear regression is compared to two null models: no glaucoma worsening, and worsening due to aging. Linear regression on the first M ≥ 2 measurements was significantly worse at predicting a reliable M+1st measurement for 2 ≤ M ≤ 6. This range was reduced to 2 ≤ M ≤ 5 when retinal nerve fiber layer thickness measurements were first "corrected" for scan quality. Simulations based on measurement frequencies in our sample-on average 393 ± 190 days between consecutive measurements-show that linear regression outperforms both null models when M ≥ 5 and the goal is to forecast moderate (75th percentile) worsening, and when M ≥ 3 for rapid (90th percentile) worsening. If linear regression is used to assess disease trajectory with a small number of measurements over short time periods (e.g., 1-2 years), as is often the case in clinical practice, the number of optical coherence tomography examinations needs to be increased.
- Ruolin Wang, Chris Bradley, Patrick Herbert, and 5 more authorsScientific Reports Jan 2024
To develop and evaluate the performance of a deep learning model (DLM) that predicts eyes at high risk of surgical intervention for uncontrolled glaucoma based on multimodal data from an initial ophthalmology visit. Longitudinal, observational, retrospective study. 4898 unique eyes from 4038 adult glaucoma or glaucoma-suspect patients who underwent surgery for uncontrolled glaucoma (trabeculectomy, tube shunt, xen, or diode surgery) between 2013 and 2021, or did not undergo glaucoma surgery but had 3 or more ophthalmology visits. We constructed a DLM to predict the occurrence of glaucoma surgery within various time horizons from a baseline visit. Model inputs included spatially oriented visual field (VF) and optical coherence tomography (OCT) data as well as clinical and demographic features. Separate DLMs with the same architecture were trained to predict the occurrence of surgery within 3 months, within 3-6 months, within 6 months-1 year, within 1-2 years, within 2-3 years, within 3-4 years, and within 4-5 years from the baseline visit. Included eyes were randomly split into 60%, 20%, and 20% for training, validation, and testing. DLM performance was measured using area under the receiver operating characteristic curve (AUC) and precision-recall curve (PRC). Shapley additive explanations (SHAP) were utilized to assess the importance of different features. Model prediction of surgery for uncontrolled glaucoma within 3 months had the best AUC of 0.92 (95% CI 0.88, 0.96). DLMs achieved clinically useful AUC values (\textgreater 0.8) for all models that predicted the occurrence of surgery within 3 years. According to SHAP analysis, all 7 models placed intraocular pressure (IOP) within the five most important features in predicting the occurrence of glaucoma surgery. Mean deviation (MD) and average retinal nerve fiber layer (RNFL) thickness were listed among the top 5 most important features by 6 of the 7 models. DLMs can successfully identify eyes requiring surgery for uncontrolled glaucoma within specific time horizons. Predictive performance decreases as the time horizon for forecasting surgery increases. Implementing prediction models in a clinical setting may help identify patients that should be referred to a glaucoma specialist for surgical evaluation.
- Wang Ruolin, Chris Bradley, Patrick Herbert, and 6 more authorsOphthalmology. Glaucoma Jan 2024
Develop and evaluate the performance of a deep learning model (DLM) that forecasts eyes with low future visual field (VF) variability, and study the impact of using this DLM on sample size requirements for neuroprotective trials.
2023
- Alex T. Pham, Chris Bradley, Kaihua Hou, and 4 more authorsAmerican Journal of Ophthalmology Nov 2023
Estimate the effect of being below and above the clinician-set target intraocular pressure (IOP) on rates of glaucomatous retinal nerve fiber layer (RNFL) thinning in a treated real-world clinical population.
- Patrick Herbert, Kaihua Hou, Chris Bradley, and 5 more authorsOphthalmol. Glaucoma Mar 2023
PURPOSE: Assess whether we can forecast future rapid visual field (VF) worsening using deep learning models (DLMs) trained on early VF, OCT, and clinical data. DESIGN: Retrospective cohort study. SUBJECTS: 4,536 eyes from 2,962 patients. 263 (5.80%) of eyes underwent rapid VF worsening (MD slope <-1dB/yr across all VFs). METHODS: We included eyes that met the following criteria: 1) followed for glaucoma or suspect status 2) had at least five longitudinal reliable VFs (VF1, VF2, VF3, VF4, VF5) 3) had one reliable baseline Optical Coherence Tomography (OCT) scan (OCT1) and one set of baseline clinical measurements (Clinical1) at the time of VF1. We designed a DLM to forecast future rapid VF worsening. The input consisted of spatially oriented total deviation values from VF1 (including or not including VF2 and VF3 in some models) and retinal nerve fiber layer thickness values from the baseline OCT. We passed this VF/OCT stack into a vision transformer feature extractor, the output of which was concatenated with baseline clinical data before putting it through a linear classifier to predict that eye’s risk of rapid VF worsening across the five VFs. We compared the performance of models with differing inputs by computing area under receiver operating curve (AUC) in the test set. Specifically, we trained models with the following inputs: Model V: VF1; VC: VF1+ Clinical1; VO: VF1+ OCT1; VOC: VF1+ Clinical1+ OCT1; V2: VF1 + VF2; V2OC: VF1 + VF2 + Clinical1 + OCT1; V3: VF1 + VF2 + VF3; V3OC: VF1 + VF2 + VF3 + Clinical1 + OCT1. MAIN OUTCOME MEASURES: AUC of DLMs when forecasting rapidly worsening eyes. RESULTS: Model V3OC best forecasted rapid worsening with an AUC (95% CI) of 0.87 (0.77, 0.97). Remaining models in descending order of performance and their respective AUC [95% CI] were: Model V3 (0.84 [0.74 to 0.95]), Model V2OC (0.81 [0.70 to 0.92]), Model V2 (0.81 [0.70 to 0.82]), Model VOC (0.77 [0.65, 0.88]), Model VO [0.75 [0.64, 0.88], Model VC (0.75 [0.63, 0.87]), Model V (0.74 [0.62, 0.86]). CONCLUSION: DLMs can forecast future rapid glaucoma worsening with modest to high performance when trained using data from early in the disease course. Including baseline data from multiple modalities and subsequent visits improves performance beyond using VF data alone.
- C. Bradley, P. Herbert, K. Hou, and 3 more authorsOphthalmology Feb 2023
Compare the accuracy of detecting moderate and rapid rates of glaucoma worsening over a 2-year period with different numbers of OCT scans and VFs in a large sample of glaucoma and glaucoma-suspect eyes.
- J. Sabharwal, K. Hou, P. Herbert, and 6 more authorsSci Rep Jan 2023
Glaucoma is a leading cause of irreversible blindness, and its worsening is most often monitored with visual field (VF) testing. Deep learning models (DLM) may help identify VF worsening consistently and reproducibly. In this study, we developed and investigated the performance of a DLM on a large population of glaucoma patients. We included 5099 patients (8705 eyes) seen at one institute from June 1990 to June 2020 that had VF testing as well as clinician assessment of VF worsening. Since there is no gold standard to identify VF worsening, we used a consensus of six commonly used algorithmic methods which include global regressions as well as point-wise change in the VFs. We used the consensus decision as a reference standard to train/test the DLM and evaluate clinician performance. 80%, 10%, and 10% of patients were included in training, validation, and test sets, respectively. Of the 873 eyes in the test set, 309 [60.6%] were from females and the median age was 62.4; (IQR 54.8–68.9). The DLM achieved an AUC of 0.94 (95% CI 0.93–0.99). Even after removing the 6 most recent VFs, providing fewer data points to the model, the DLM successfully identified worsening with an AUC of 0.78 (95% CI 0.72–0.84). Clinician assessment of worsening (based on documentation from the health record at the time of the final VF in each eye) had an AUC of 0.64 (95% CI 0.63–0.66). Both the DLM and clinician performed worse when the initial disease was more severe. This data shows that a DLM trained on a consensus of methods to define worsening successfully identified VF worsening and could help guide clinicians during routine clinical care.
2022
- Chris Bradley, Kaihua Hou, Patrick Herbert, and 4 more authorsOphthalmology Jan 2022
Estimate the number of OCT scans necessary to detect moderate and rapid rates of retinal nerve fiber layer (RNFL) thickness worsening at different levels of accuracy using a large sample of glaucoma and glaucoma-suspect eyes.
- Gabriel A. Villasana, Chris Bradley, Tobias Elze, and 8 more authorsTranslational Vision Science & Technology Jan 2022
The purpose of this study was to accurately forecast future reliable visual field (VF) mean deviation (MD) values by correcting for poor reliability.
- Christopher T. Le, Jacob Fiksel, Pradeep Ramulu, and 1 more authorScientific Reports Jan 2022
Swedish Interactive Threshold Algorithm (SITA) Faster is the most recent and fastest testing algorithm for the evaluation of Humphrey visual fields (VF). However, existing evidence suggests that there are some differences in global measures of VF loss in eyes transitioning from SITA Standard to the newer SITA Faster. These differences may be relevant, especially in glaucoma, where VF changes over time influence clinical decisions around treatment. Furthermore, characterization of differences in localizable VF loss patterns between algorithms, rather than global summary measures, can be important for clinician interpretation when transitioning testing strategies. In this study, we determined the effect of transitioning from SITA Standard to SITA Faster on VF loss patterns in glaucomatous eyes undergoing longitudinal VF testing in a real-world clinical setting. Archetypal analysis was used to derive composition weights of 16 clinically relevant VF patterns (i.e., archetypes (AT)) from patient VFs. We found switching from SITA Standard to SITA Faster was associated with less preservation of VF loss (i.e., abnormal AT 2-4, 6-9, 11, 13, 14) relative to successive SITA Standard exams (P value < 0.01) and was associated with relatively greater preservation of AT 1, the normal VF (P value < 0.01). Eyes that transition from SITA Standard to SITA Faster in a real-world clinical setting have an increased likelihood of preserving patterns reflecting a normal VF and lower tendency to preserve patterns reflecting abnormal VF as compared to consecutive SITA Standard exams in the same eye.
- Gabriel A. Villasana, Chris Bradley, Pradeep Ramulu, and 2 more authorsOphthalmology Sep 2022
To estimate the effect of achieving target intraocular pressure (IOP) values on visual field (VF) worsening in a treated clinical population.
2021
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The association between intraocular pressure and visual field worsening in treated glaucoma patientsJithin Yohannan, Michael V. Boland, and Pradeep RamuluJournal of Glaucoma Sep 2021The purpose of this study was to describe the relationship between mean treated IOP and VF worsening and understand how this relationship is affected by glaucoma severity and IOP range.
- Scott R. Shuldiner, Michael V. Boland, Pradeep Y. Ramulu, and 6 more authorsPLOS ONE Sep 2021
To assess whether machine learning algorithms (MLA) can predict eyes that will undergo rapid glaucoma progression based on an initial visual field (VF) test.
- Alex T. Pham, Pradeep Y. Ramulu, Michael V. Boland, and 1 more authorOphthalmology Mar 2021
To determine the effect of transitioning from Swedish Interactive Thresholding Algorithm (SITA) Standard to SITA Faster on visual field (VF) performance in glaucomatous eyes with a broad spectrum of disease severity undergoing longitudinal VF testing in a real-world clinical setting.
2020
- Avyuk Dixit, Jithin Yohannan, and Michael V. BolandOphthalmology Dec 2020
Rule-based approaches to determining glaucoma progression from visual fields (VFs) alone are discordant and have tradeoffs. To detect better when glaucoma progression is occurring, we used a longitudinal data set of merged VF and clinical data to assess the performance of a convolutional long short-term memory (LSTM) neural network.
- Inas F. Aboobakar, Jiangxia Wang, Balwantray C. Chauhan, and 4 more authorsTranslational Vision Science & Technology Jan 2020
Identify factors predicting worse or better than expected visual field (VF) performance.
2019
- Jithin Yohannan, Michael Cheng, Joseph Da, and 6 more authorsOphthalmology Aug 2019
To assess the impact of OCT signal strength (SS) and artifact on retinal nerve fiber layer (RNFL) measurement reliability and to understand whether glaucoma severity modifies this relationship.
2017
- Jithin Yohannan, and Michael V. BolandOphthalmology Dec 2017
The relationship between functional vision loss and structural changes of the optic nerve head and retinal ganglion cells is the hallmark of glaucoma diagnosis. Understanding and measuring this relationship has been the focus of numerous studies, the goal of which have been to improve glaucoma diagnosis and detection of glaucoma worsening. In this review, a historical perspective is used to understand structure–function relationships in glaucoma and their application to improve glaucoma diagnosis and monitoring. Initially, histologic studies that link visual field sensitivity to retinal ganglion cell count are discussed. Additionally, those studies that determined the mathematical relationship between visual field sensitivity and ganglion cell number are reviewed. Next, those studies that attempt to create a map of the structure–function relationship using fundus photography and visual field sensitivity are examined. Subsequently, studies that use more recent imaging technology, such as optical coherence tomography, confocal scanning laser ophthalmoscopy, or scanning laser perimetry, to measure structure quantitatively in vivo and to correlate these measures with automated perimetry are explored. Among these studies that use advanced imaging, those that use cross-sectional data to explore structure–function relationships to improve glaucoma diagnosis first are discussed. Second, those studies that use longitudinal data to improve detection of worsening are reviewed. Finally, areas of further research and steps needed to implement structure–function relationships clinically are explored.
- Jithin Yohannan, Jiangxia Wang, Jamie Brown, and 4 more authorsOphthalmology Jul 2017
Assess the impact of false-positives (FP), false-negatives (FN), fixation losses (FL), and test duration (TD) on visual field (VF) reliability at different stages of glaucoma severity.