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Purpose: To assess the diagnostic performance of two chatbots, ChatGPT and Glass, in uveitis diagnosis compared to renowned uveitis specialists, and evaluate clinicians' perception about utilizing artificial intelligence (AI) in ophthalmology practice., Methods: Six cases were presented to uveitis experts, ChatGPT (version 3.5 and 4.0) and Glass 1.0, and diagnostic accuracy was analyzed. Additionally, a survey about the emotions, confidence in utilizing AI-based tools, and the likelihood of incorporating such tools in clinical practice was done., Results: Uveitis experts accurately diagnosed all cases (100%), while ChatGPT achieved a diagnostic success rate of 66% and Glass 1.0 achieved 33%. Most attendees felt excited or optimistic about utilizing AI in ophthalmology practice. Older age and high level of education were positively correlated with increased inclination to adopt AI-based tools., Conclusions: ChatGPT demonstrated promising diagnostic capabilities in uveitis cases and ophthalmologist showed enthusiasm for the integration of AI into clinical practice.

Introduction: Accurate diagnosis and timely management are vital for favorable uveitis outcomes. Artificial Intelligence (AI) holds promise in medical decision-making, particularly in ophthalmology. Yet, the diagnostic precision and management advice from AI-based uveitis chatbots lack assessment., Methods: We appraised diagnostic accuracy and management suggestions of an AI-based chatbot, ChatGPT, versus five uveitis-trained ophthalmologists, using 25 standard cases aligned with new Uveitis Nomenclature guidelines. Participants predicted likely diagnoses, two differentials, and next management steps. Comparative success rates were computed., Results: Ophthalmologists excelled (60-92%) in likely diagnosis, exceeding AI (60%). Considering fully and partially accurate diagnoses, ophthalmologists achieved 76-100% success; AI attained 72%. Despite an 8% AI improvement, its overall performance lagged. Ophthalmologists and AI agreed on diagnosis in 48% cases, with 91.6% exhibiting concurrence in management plans., Conclusions: The study underscores AI chatbots' potential in uveitis diagnosis and management, indicating their value in reducing diagnostic errors. Further research is essential to enhance AI chatbot precision in diagnosis and recommendations.

Purpose: Artificial intelligence (AI) methods are changing all areas of research and have a variety of capabilities of analysis in ophthalmology, specifically in visual fields (VFs) to detect or predict vision loss progression. Whereas most of the AI algorithms are implemented in Python language, which offers numerous open-source functions and algorithms, the majority of algorithms in VF analysis are offered in the R language. This paper introduces PyVisualFields, a developed package to address this gap and make available VF analysis in the Python language., Methods: For the first version, the R libraries for VF analysis provided by vfprogression and visualFields packages are analyzed to define the overlaps and distinct functions. Then, we defined and translated this functionality into Python with the help of the wrapper library rpy2. Besides maintaining, the subsequent versions' milestones are established, and the third version will be R-independent., Results: The developed Python package is available as open-source software via the GitHub repository and is ready to be installed from PyPI. Several Jupyter notebooks are prepared to demonstrate and describe the capabilities of the PyVisualFields package in the categories of data presentation, normalization and deviation analysis, plotting, scoring, and progression analysis., Conclusions: We developed a Python package and demonstrated its functionality for VF analysis and facilitating ophthalmic research in VF statistical analysis, illustration, and progression prediction., Translational Relevance: Using this software package, researchers working on VF analysis can more quickly create algorithms for clinical applications using cutting-edge AI techniques.

Purpose: To develop an artificial intelligence (AI) dashboard for monitoring glaucomatous functional loss., Design: Retrospective, cross-sectional, longitudinal cohort study., Participants: Of 31 591 visual fields (VFs) on 8077 subjects, 13 231 VFs from the most recent visit of each patient were included to develop the AI dashboard. Longitudinal VFs from 287 eyes with glaucoma were used to validate the models., Method: We entered VF data from the most recent visit of glaucomatous and nonglaucomatous patients into a "pipeline" that included principal component analysis (PCA), manifold learning, and unsupervised clustering to identify eyes with similar global, hemifield, and local patterns of VF loss. We visualized the results on a map, which we refer to as an "AI-enabled glaucoma dashboard." We used density-based clustering and the VF decomposition method called "archetypal analysis" to annotate the dashboard. Finally, we used 2 separate benchmark datasets-one representing "likely nonprogression" and the other representing "likely progression"-to validate the dashboard and assess its ability to portray functional change over time in glaucoma., Main Outcome Measures: The severity and extent of functional loss and characteristic patterns of VF loss in patients with glaucoma., Results: After building the dashboard, we identified 32 nonoverlapping clusters. Each cluster on the dashboard corresponded to a particular global functional severity, an extent of VF loss into different hemifields, and characteristic local patterns of VF loss. By using 2 independent benchmark datasets and a definition of stability as trajectories not passing through over 2 clusters in a left or downward direction, the specificity for detecting "likely nonprogression" was 94% and the sensitivity for detecting "likely progression" was 77%., Conclusions: The AI-enabled glaucoma dashboard, developed using a large VF dataset containing a broad spectrum of visual deficit types, has the potential to provide clinicians with a user-friendly tool for determination of the severity of glaucomatous vision deficit, the spatial extent of the damage, and a means for monitoring the disease progression., (Copyright © 2020 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.)

Purpose: The purpose of this study was to classify the spatial patterns of retinal nerve fiber layer thickness (RNFLT) and assess their associations with visual field (VF) loss in glaucoma., Methods: We used paired reliable 24-2 VFs and optical coherence tomography scans of 691 eyes from 691 patients. The RNFLT maps were used to determine the RNFLT patterns (RPs) by non-negative matrix factorization (NMF). The RPs were correlated with mean deviation (MD), spherical equivalent (SE), and major blood vessel locations. The RPs were further used to predict the 52 total deviation (TD) values by linear regression compared with models using 24 15-degree sectors. Last, we associated the RPs with average TDs of the central upper two locations (C2-TD). Stepwise regression was applied to remove redundant features., Results: NMF highlighted 16 distinct RPs. Twelve RPs had arcuate-like informative zones (iZones): six with superior iZones, five with inferior iZones, and one RP with a bi-hemifield iZone, and four with non-arcuate-like temporal or nasal iZones. Twelve, nine, nine, and nine RPs were significantly ( P < 0.05) correlated to MD, SE, and superior and inferior artery locations, respectively. Using RPs significantly ( P < 0.05) improved the prediction of 52 TDs compared with using 24 15-degree sectors. Using RPs significantly ( P < 0.001) improved the C2-TD prediction related to thinning in the inferior vulnerability zone compared with using the 24 sectoral RNFLTs., Conclusions: Using RPs improved the VF prediction compared with using sectoral RNFLTs., Translational Relevance: The RPs characterizing both pathological and anatomical variations can potentially assist clinicians better assess RNFLT loss., Competing Interests: Disclosure: M. Wang, None; L.Q. Shen, None; L.R. Pasquale, None; H. Wang, None; D. Li, None; E.Y. Choi, None; S. Yousefi, None; P.J. Bex, None; T. Elze, None, (Copyright 2020 The Authors.)

Purpose: To quantify the central visual field (VF) loss patterns in glaucoma using artificial intelligence., Design: Retrospective study., Participants: VFs of 8712 patients with 13 951 Humphrey 10-2 test results from 13 951 eyes for cross-sectional analyses, and 824 patients with at least 5 reliable 10-2 test results at 6-month intervals or more from 1191 eyes for longitudinal analyses., Methods: Total deviation values were used to determine the central VF patterns using the most recent 10-2 test results. A 24-2 VF within a 3-month window of the 10-2 tests was used to stage eyes into mild, moderate, or severe functional loss using the Hodapp-Anderson-Parrish scale at baseline. Archetypal analysis was applied to determine the central VF patterns. Cross-validation was performed to determine the optimal number of patterns. Stepwise regression was applied to select the optimal feature combination of global indices, average baseline decomposition coefficients from central VFs archetypes, and other factors to predict central VF mean deviation (MD) slope based on the Bayesian information criterion (BIC)., Main Outcome Measures: The central VF patterns stratified by severity stage based on 24-2 test results and a model to predict the central VF MD change over time using baseline test results., Results: From cross-sectional analysis, 17 distinct central VF patterns were determined for the 13 951 eyes across the spectrum of disease severity. These central VF patterns could be divided into isolated superior loss, isolated inferior loss, diffuse loss, and other loss patterns. Notably, 4 of the 5 patterns of diffuse VF loss preserved the less vulnerable inferotemporal zone, whereas they lost most of the remaining more vulnerable zone described by the Hood model. Inclusion of coefficients from central VF archetypical patterns strongly improved the prediction of central VF MD slope (BIC decrease, 35; BIC decrease of >6 indicating strong prediction improvement) than using only the global indices of 2 baseline VF results. Eyes with baseline VF results with more superonasal and inferonasal loss were more likely to show worsening MD over time., Conclusions: We quantified central VF patterns in glaucoma, which were used to improve the prediction of central VF worsening compared with using only global indices., (Copyright © 2019 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.)

Importance: Although the central visual field (VF) in end-stage glaucoma may substantially vary among patients, structure-function studies and quality-of-life assessments are impeded by the lack of appropriate characterization of end-stage VF loss., Objective: To provide a quantitative characterization and classification of central VF loss in end-stage glaucoma., Design, Setting, and Participants: This retrospective cohort study collected data from 5 US glaucoma services from June 1, 1999, through October 1, 2014. A total of 2912 reliable 10-2 VFs of 1103 eyes from 1010 patients measured after end-stage 24-2 VFs with a mean deviation (MD) of -22 dB or less were included in the analysis. Data were analyzed from March 28, 2018, through May 23, 2019., Main Outcomes and Measures: Central VF patterns were determined by an artificial intelligence algorithm termed archetypal analysis. Longitudinal analyses were performed to investigate whether the development of central VF defect mostly affects specific vulnerability zones., Results: Among the 1103 patients with the most recent VFs, mean (SD) age was 70.4 (14.3) years; mean (SD) 10-2 MD, -21.5 (5.6) dB. Fourteen central VF patterns were determined, including the most common temporal sparing patterns (304 [27.5%]), followed by mostly nasal loss (280 [25.4%]), hemifield loss (169 [15.3%]), central island (120 [10.9%]), total loss (91 [8.3%]), nearly intact field (56 [5.1%]), inferonasal quadrant sparing (42 [3.8%]), and nearly total loss (41 [3.7%]). Location-specific median total deviation analyses partitioned the central VF into a more vulnerable superonasal zone and a less vulnerable inferotemporal zone. At 1-year and 2-year follow-up, new defects mostly occurred in the more vulnerable zone. Initial encroachments on an intact central VF at follow-up were more likely to be from nasal loss (11 [18.4%]; P < .001). One of the nasal loss patterns had a substantial chance at 2-year follow-up (8 [11.0%]; P = .004) to shift to total loss, whereas others did not., Conclusions and Relevance: In this study, central VF loss in end-stage glaucoma was found to exhibit characteristic patterns that might be associated with different subtypes. Initial central VF loss is likely to be nasal loss, and 1 specific type of nasal loss is likely to develop into total loss.

Deep learning (DL) is a subset of artificial intelligence (AI), which uses multilayer neural networks modelled after the mammalian visual cortex capable of synthesizing images in ways that will transform the field of glaucoma. Autonomous DL algorithms are capable of maximizing information embedded in digital fundus photographs and ocular coherence tomographs to outperform ophthalmologists in disease detection. Other unsupervised algorithms such as principal component analysis (axis learning) and archetypal analysis (corner learning) facilitate visual field interpretation and show great promise to detect functional glaucoma progression and differentiate it from non-glaucomatous changes when compared with conventional software packages. Forecasting tools such as the Kalman filter may revolutionize glaucoma management by accounting for a host of factors to set target intraocular pressure goals that preserve vision. Activation maps generated from DL algorithms that process glaucoma data have the potential to efficiently direct our attention to critical data elements embedded in high throughput data and enhance our understanding of the glaucomatous process. It is hoped that AI will realize more accurate assessment of the copious data encountered in glaucoma management, improving our understanding of the disease, preserving vision, and serving to enhance the deep bonds that patients develop with their treating physicians.

Purpose: To detect visual field (VF) progression by analyzing spatial pattern changes., Methods: We selected 12,217 eyes from 7360 patients with at least five reliable 24-2 VFs and 5 years of follow-up with an interval of at least 6 months. VFs were decomposed into 16 archetype patterns previously derived by artificial intelligence techniques. Linear regressions were applied to the 16 archetype weights of VF series over time. We defined progression as the decrease rate of the normal archetype or any increase rate of the 15 VF defect archetypes to be outside normal limits. The archetype method was compared with mean deviation (MD) slope, Advanced Glaucoma Intervention Study (AGIS) scoring, Collaborative Initial Glaucoma Treatment Study (CIGTS) scoring, and the permutation of pointwise linear regression (PoPLR), and was validated by a subset of VFs assessed by three glaucoma specialists., Results: In the method development cohort of 11,817 eyes, the archetype method agreed more with MD slope (kappa: 0.37) and PoPLR (0.33) than AGIS (0.12) and CIGTS (0.22). The most frequently progressed patterns included decreased normal pattern (63.7%), and increased nasal steps (16.4%), altitudinal loss (15.9%), superior-peripheral defect (12.1%), paracentral/central defects (10.5%), and near total loss (10.4%). In the clinical validation cohort of 397 eyes with 27.5% of confirmed progression, the agreement (kappa) and accuracy (mean of hit rate and correct rejection rate) of the archetype method (0.51 and 0.77) significantly (P < 0.001 for all) outperformed AGIS (0.06 and 0.52), CIGTS (0.24 and 0.59), MD slope (0.21 and 0.59), and PoPLR (0.26 and 0.60)., Conclusions: The archetype method can inform clinicians of VF progression patterns.