An Artificial Intelligence Approach to Detect Visual Field Progression in Glaucoma Based on Spatial Pattern Analysis

Fuzzy clustering of 24-2 visual field patterns can detect glaucoma progression

PURPOSE

To represent 24-2 visual field (VF) losses of individual patients using a hybrid approach of archetypal analysis (AA) and fuzzy c-means (FCM) clustering.

METHODS

In this multicenter retrospective study, we classified characteristic patterns of 24-2 VF using AA and decomposed them with FCM clustering. We predicted the change in mean deviation (MD) through supervised machine learning from decomposition coefficient change. In addition, we compared the areas under the receiver operating characteristic curves (AUCs) of the decomposition coefficient slopes to detect VF progression using three criteria: MD slope, Visual Field Index slope, and pointwise linear regression analysis.

RESULTS

We identified 16 characteristic patterns (archetypes or ATs) of 24-2 VF from 132,938 VFs of 18,033 participants using AA. The hybrid approach using FCM revealed a lower mean squared error and greater correlation coefficient than the AA single approach for predicting MD change (all P ≤ 0.001). Three of 16 AUCs of the FCM decomposition coefficient slopes outperformed the AA decomposition coefficient slopes in detecting VF progression for all three criteria (AT5, superior altitudinal defect; AT10, double arcuate defect; AT13, total loss) (all P ≤ 0.028).

CONCLUSION

A hybrid approach combining AA and FCM to analyze 24-2 VF can visualize VF tests in characteristic patterns and enhance detection of VF progression with lossless decomposition.

The AI revolution in glaucoma: Bridging challenges with opportunities

Recent advancements in artificial intelligence (AI) herald transformative potentials for reshaping glaucoma clinical management, improving screening efficacy, sharpening diagnosis precision, and refining the detection of disease progression. However, incorporating AI into healthcare usages faces significant hurdles in terms of developing algorithms and putting them into practice. When creating algorithms, issues arise due to the intensive effort required to label data, inconsistent diagnostic standards, and a lack of thorough testing, which often limits the algorithms' widespread applicability. Additionally, the "black box" nature of AI algorithms may cause doctors to be wary or skeptical. When it comes to using these tools, challenges include dealing with lower-quality images in real situations and the systems' limited ability to work well with diverse ethnic groups and different diagnostic equipment. Looking ahead, new developments aim to protect data privacy through federated learning paradigms, improving algorithm generalizability by diversifying input data modalities, and augmenting datasets with synthetic imagery. The integration of smartphones appears promising for using AI algorithms in both clinical and non-clinical settings. Furthermore, bringing in large language models (LLMs) to act as interactive tool in medicine may signify a significant change in how healthcare will be delivered in the future. By navigating through these challenges and leveraging on these as opportunities, the field of glaucoma AI will not only have improved algorithmic accuracy and optimized data integration but also a paradigmatic shift towards enhanced clinical acceptance and a transformative improvement in glaucoma care.

Transformer-Based Deep Learning Prediction of 10-Degree Humphrey Visual Field Tests From 24-Degree Data

Purpose

To predict 10-2 Humphrey visual fields (VFs) from 24-2 VFs and associated non-total deviation features using deep learning.

Methods

We included 5189 reliable 24-2 and 10-2 VF pairs from 2236 patients, and 28,409 reliable pairs of macular OCT scans and 24-2 VF from 19,527 eyes of 11,560 patients. We developed a transformer-based deep learning model using 52 total deviation values and nine VF test features to predict 68 10-2 total deviation values. The mean absolute error, root mean square error, and the R2 were evaluation metrics. We further evaluated whether the predicted 10-2 VFs can improve the structure-function relationship between macular thinning and paracentral VF loss in glaucoma.

Results

The average mean absolute error and R2 for 68 10-2 VF test points were 3.30 ± 0.52 dB and 0.70 ± 0.11, respectively. The accuracy was lower in the inferior temporal region. The model placed greater emphasis on 24-2 VF points near the central fixation point when predicting the 10-2 VFs. The inclusion of nine VF test features improved the mean absolute error and R2 up to 0.17 ± 0.06 dB and 0.01 ± 0.01, respectively. Age was the most important 24-2 VF test parameter for 10-2 VF prediction. The predicted 10-2 VFs achieved an improved structure-function relationship between macular thinning and paracentral VF loss, with the R2 at the central 4, 12, and 16 locations of 24-2 VFs increased by 0.04, 0.05 and 0.05, respectively (P < 0.001).

Conclusions

The 10-2 VFs may be predicted from 24-2 data.

Translational Relevance

The predicted 10-2 VF has the potential to improve glaucoma diagnosis.

Impact of Demographics on Regional Visual Field Loss and Deterioration in Glaucoma

Purpose

To elucidate the impact of demographics, including gender, race, ethnicity, and preferred language, on regional visual field (VF) loss and progression in glaucoma.

Methods

Multivariable linear mixed regressions were performed to determine the impact of race, ethnicity, and preferred language on regional VF loss with adjustment for age and gender. Regional VF loss was defined by pointwise total deviation values and VF loss patterns quantified by an unsupervised machine learning method termed archetypal analysis. All cross-sectional and longitudinal analyses were performed both without and with adjustment for VF mean deviation, which represented overall VF loss severity. P values were corrected for multiple comparisons.

Results

All results mentioned had corrected P values less than 0.05. Asian and Black patients showed worse pointwise VF loss than White patients with superior hemifield more affected. Patients with a preferred language other than English demonstrated worse pointwise VF loss than patients with English as their preferred language. Longitudinal analyses revealed Black patients showed worse VF loss/year compared to White patients. Patients with a preferred language other than English demonstrated worse VF loss/year compared to patients preferring English.

Conclusions

Blacks and non-English speakers have more severe VF loss, with superior hemifield being more affected and faster VF worsening.

Translational Relevance

This study furthered our understanding of racial, ethnic, and socioeconomic disparities in glaucoma outcomes. Understanding the VF loss burden in different racial, ethnic, and socioeconomic groups may guide more effective glaucoma screening and community outreach efforts. This research could help reduce vision loss and improve quality of life in disproportionately affected populations by guiding public health efforts to promote glaucoma awareness and access to care.

Contributions of Brain Microstructures and Metabolism to Visual Field Loss Patterns in Glaucoma Using Archetypal and Information Gain Analyses

Purpose

To investigate the contributions of the microstructural and metabolic brain environment to glaucoma and their association with visual field (VF) loss patterns by using advanced diffusion magnetic resonance imaging (dMRI), proton magnetic resonance spectroscopy (MRS), and clinical ophthalmic measures.

Methods

Sixty-nine glaucoma and healthy subjects underwent dMRI and/or MRS at 3 Tesla. Ophthalmic data were collected from VF perimetry and optical coherence tomography. dMRI parameters of microstructural integrity in the optic radiation and MRS-derived neurochemical levels in the visual cortex were compared among early glaucoma, advanced glaucoma, and healthy controls. Multivariate regression was used to correlate neuroimaging metrics with 16 archetypal VF loss patterns. We also ranked neuroimaging, ophthalmic, and demographic attributes in terms of their information gain to determine their importance to glaucoma.

Results

In dMRI, decreasing fractional anisotropy, radial kurtosis, and tortuosity and increasing radial diffusivity correlated with greater overall VF loss bilaterally. Regionally, decreasing intra-axonal space and extra-axonal space diffusivities correlated with greater VF loss in the superior-altitudinal area of the right eye and the inferior-altitudinal area of the left eye. In MRS, both early and advanced glaucoma patients had lower gamma-aminobutyric acid (GABA), glutamate, and choline levels than healthy controls. GABA appeared to associate more with superonasal VF loss, and glutamate and choline more with inferior VF loss. Choline ranked third for importance to early glaucoma, whereas radial kurtosis and GABA ranked fourth and fifth for advanced glaucoma.

Conclusions

Our findings highlight the importance of non-invasive neuroimaging biomarkers and analytical modeling for unveiling glaucomatous neurodegeneration and how they reflect complementary VF loss patterns.

Deep-learning-based prediction of glaucoma conversion in normotensive glaucoma suspects

BACKGROUND/AIMS

To assess the performance of deep-learning (DL) models for prediction of conversion to normal-tension glaucoma (NTG) in normotensive glaucoma suspect (GS) patients.

METHODS

Datasets of 12 458 GS eyes were reviewed. Two hundred and ten eyes (105 eyes showing NTG conversion and 105 without conversion), followed up for a minimum of 7 years during which intraocular pressure (IOP) was lower than 21 mm Hg, were included. The features of two fundus images (optic disc photography and red-free retinal nerve fibre layer (RNFL) photography) were extracted by convolutional auto encoder. The extracted features as well as 15 clinical features including age, sex, IOP, spherical equivalent, central corneal thickness, axial length, average circumpapillary RNFL thickness, systolic/diastolic blood pressure and body mass index were used to predict NTG conversion. Prediction was performed using three machine-learning classifiers (ie, XGBoost, Random Forest, Gradient Boosting) with different feature combinations.

RESULTS

All three algorithms achieved high diagnostic accuracy for NTG conversion prediction. The AUCs ranged from 0.987 (95% CI 0.978 to 1.000; Random Forest trained with both fundus images and clinical features) and 0.994 (95% CI 0.984 to 1.000; XGBoost trained with both fundus images and clinical features). XGBoost showed the best prediction performance for time to NTG conversion (mean squared error, 2.24). The top three important clinical features for time-to-conversion prediction were baseline IOP, diastolic blood pressure and average circumpapillary RNFL thickness.

CONCLUSION

DL models, trained with both fundus images and clinical data, showed the potential to predict whether and when normotensive GS patients will show conversion to NTG.

Estimating the Distribution of True Rates of Visual Field Progression in Glaucoma

Purpose

The purpose of this study was to estimate the distribution of the true rates of progression (RoP) of visual field (VF) loss.

Methods

We analyzed the progression of mean deviation over time in series of ≥ 10 tests from 3352 eyes (one per patient) from 5 glaucoma clinics, using a novel Bayesian hierarchical Linear Mixed Model (LMM); this modeled the random-effect distribution of RoPs as the sum of 2 independent processes following, respectively, a negative exponential distribution (the "true" distribution of RoPs) and a Gaussian distribution (the "noise"), resulting in a skewed exGaussian distribution. The exGaussian-LMM was compared to a standard Gaussian-LMM using the Watanabe-Akaike Information Criterion (WAIC). The random-effect distributions were compared to the empirical cumulative distribution function (eCDF) of linear regression RoPs using a Kolmogorov-Smirnov test.

Results

The WAIC indicated a better fit with the exGaussian-LMM (estimate [standard error]: 192174.4 [721.2]) than with the Gaussian-LMM (192595 [697.4], with a difference of 157.2 [22.6]). There was a significant difference between the eCDF and the Gaussian-LMM distribution (P < 0.0001), but not with the exGaussian-LMM distribution (P = 0.108). The estimated mean (95% credible intervals, CIs) "true" RoP (-0.377, 95% CI = -0.396 to -0.359 dB/year) was more negative than the observed mean RoP (-0.283, 95% CI = -0.299 to -0.268 dB/year), indicating a bias likely due to learning in standard LMMs.

Conclusions

The distribution of "true" RoPs can be estimated with an exGaussian-LMM, improving model accuracy.

Translational Relevance

We used these results to develop a fast and accurate analytical approximation for sample-size calculations in clinical trials using standard LMMs, which was integrated in a freely available web application.

Understanding and identifying visual field progression

Detecting deterioration of visual field sensitivity measurements is important for the diagnosis and management of glaucoma. This review surveys the current methods for assessing progression that are implemented in clinical devices, which have been used in clinical trials, alongside more recent advances proposed in the literature. Advice is also offered to clinicians on what they can do to improve the collection of perimetric data to help analytical progression methods more accurately predict change. This advice includes a discussion of how frequently visual field testing should be undertaken, with a view towards future developments, such as digital healthcare outside the standard clinical setting and more personalised approaches to perimetry.

Machine Learning-Derived Baseline Visual Field Patterns Predict Future Glaucoma Onset in the Ocular Hypertension Treatment Study

Purpose

The Ocular Hypertension Treatment Study (OHTS) identified risk factors for primary open-angle glaucoma (POAG) in patients with ocular hypertension, including pattern standard deviation (PSD). Archetypal analysis, an unsupervised machine learning method, may offer a more interpretable approach to risk stratification by identifying patterns in baseline visual fields (VFs).

Methods

There were 3272 eyes available in the OHTS. Archetypal analysis was applied using 24-2 baseline VFs, and model selection was performed with cross-validation. Decomposition coefficients for archetypes (ATs) were calculated. A penalized Cox proportional hazards model was implemented to select discriminative ATs. The AT model was compared to the OHTS model. Associations were identified between ATs with both POAG onset and VF progression, defined by mean deviation change per year.

Results

We selected 8494 baseline VFs. Optimal AT count was 19. The highest prevalence ATs were AT9, AT11, and AT7. The AT-based prediction model had a C-index of 0.75 for POAG onset. Multivariable models demonstrated that a one-interquartile range increase in the AT5 (hazard ratio [HR] = 1.14; 95% confidence interval [CI], 1.04-1.25), AT8 (HR = 1.22; 95% CI, 1.09-1.37), AT15 (HR = 1.26; 95% CI, 1.12-1.41), and AT17 (HR = 1.17; 95% CI, 1.03-1.31) coefficients conferred increased risk of POAG onset. AT5, AT10, and AT14 were significantly associated with rapid VF progression. In a subgroup analysis by high-risk ATs (>95th percentile or <75th percentile coefficients), PSD lost significance as a predictor of POAG in the low-risk group.

Conclusions

Baseline VFs, prior to detectable glaucomatous damage, contain occult patterns representing early changes that may increase the risk of POAG onset and VF progression in patients with ocular hypertension. The relationship between PSD and POAG is modified by the presence of high-risk patterns at baseline. An AT-based prediction model for POAG may provide more interpretable glaucoma-specific information in a clinical setting.

Advancing Glaucoma Care: Integrating Artificial Intelligence in Diagnosis, Management, and Progression Detection

Glaucoma, the leading cause of irreversible blindness worldwide, comprises a group of progressive optic neuropathies requiring early detection and lifelong treatment to preserve vision. Artificial intelligence (AI) technologies are now demonstrating transformative potential across the spectrum of clinical glaucoma care. This review summarizes current capabilities, future outlooks, and practical translation considerations. For enhanced screening, algorithms analyzing retinal photographs and machine learning models synthesizing risk factors can identify high-risk patients needing diagnostic workup and close follow-up. To augment definitive diagnosis, deep learning techniques detect characteristic glaucomatous patterns by interpreting results from optical coherence tomography, visual field testing, fundus photography, and other ocular imaging. AI-powered platforms also enable continuous monitoring, with algorithms that analyze longitudinal data alerting physicians about rapid disease progression. By integrating predictive analytics with patient-specific parameters, AI can also guide precision medicine for individualized glaucoma treatment selections. Advances in robotic surgery and computer-based guidance demonstrate AI's potential to improve surgical outcomes and surgical training. Beyond the clinic, AI chatbots and reminder systems could provide patient education and counseling to promote medication adherence. However, thoughtful approaches to clinical integration, usability, diversity, and ethical implications remain critical to successfully implementing these emerging technologies. This review highlights AI's vast capabilities to transform glaucoma care while summarizing key achievements, future prospects, and practical considerations to progress from bench to bedside.

Artificial intelligence in glaucoma: opportunities, challenges, and future directions

Artificial intelligence (AI) has shown excellent diagnostic performance in detecting various complex problems related to many areas of healthcare including ophthalmology. AI diagnostic systems developed from fundus images have become state-of-the-art tools in diagnosing retinal conditions and glaucoma as well as other ocular diseases. However, designing and implementing AI models using large imaging data is challenging. In this study, we review different machine learning (ML) and deep learning (DL) techniques applied to multiple modalities of retinal data, such as fundus images and visual fields for glaucoma detection, progression assessment, staging and so on. We summarize findings and provide several taxonomies to help the reader understand the evolution of conventional and emerging AI models in glaucoma. We discuss opportunities and challenges facing AI application in glaucoma and highlight some key themes from the existing literature that may help to explore future studies. Our goal in this systematic review is to help readers and researchers to understand critical aspects of AI related to glaucoma as well as determine the necessary steps and requirements for the successful development of AI models in glaucoma.

Artifact Correction in Retinal Nerve Fiber Layer Thickness Maps Using Deep Learning and Its Clinical Utility in Glaucoma

Purpose

Correcting retinal nerve fiber layer thickness (RNFLT) artifacts in glaucoma with deep learning and evaluate its clinical usefulness.

Methods

We included 24,257 patients with optical coherence tomography and reliable visual field (VF) measurements within 30 days and 3,233 patients with reliable VF series of at least five measurements over ≥4 years. The artifacts are defined as RNFLT less than the known floor value of 50 µm. We selected 27,319 high-quality RNFLT maps with an artifact ratio (AR) of <2% as the ground truth. We created pseudo-artifacts from 21,722 low-quality RNFLT maps with AR of >5% and superimposed them on high-quality RNFLT maps to predict the artifact-free ground truth. We evaluated the impact of artifact correction on the structure-function relationship and progression forecasting.

Results

The mean absolute error and Pearson correlation of the artifact correction were 9.89 µm and 0.90 (P < 0.001), respectively. Artifact correction improved R2 for VF prediction in RNFLT maps with AR of >10% and AR of >20% up to 0.03 and 0.04 (P < 0.001), respectively. Artifact correction improved (P < 0.05) the AUC for progression prediction in RNFLT maps with AR of ≤10%, >10%, and >20%: (1) total deviation pointwise progression: 0.68 to 0.69, 0.62 to 0.63, and 0.62 to 0.64; and (2) mean deviation fast progression: 0.67 to 0.68, 0.54 to 0.60, and 0.45 to 0.56.

Conclusions

Artifact correction for RNFLTs improves VF and progression prediction in glaucoma.

Translational Relevance

Our model improves clinical usability of RNFLT maps with artifacts.

Association between HSP-Specific T-Cell Counts and Retinal Nerve Fiber Layer Thickness in Patients with Primary Open-Angle Glaucoma

Objective

Previous laboratory reports implicate heat shock protein (HSP)-specific T-cell responses in glaucoma pathogenesis; here, we aimed to provide direct clinical evidence by correlating systemic HSP-specific T-cell levels with glaucoma severity in patients with primary open-angle glaucoma (POAG).

Design

Cross-sectional case-control study.

Subjects

Thirty-two adult patients with POAG and 38 controls underwent blood draw and optic nerve imaging.

Methods

Peripheral blood monocytes (PBMC) were stimulated in culture with HSP27, α-crystallin, a member of the small HSP family, or HSP60. Both interferon-γ (IFN-γ)+ CD4+ T helper type 1 cells (Th1) and transforming growth factor-β1 (TGF-β1)+ CD4+ regulatory T cells (Treg) were quantified by flow cytometry and presented as a percentage of total PBMC counts. Relevant cytokines were measured using enzyme-linked immunosorbent assays. Retinal nerve fiber layer thickness (RNFLT) was measured with OCT. Pearson's correlation (r) was used to assess correlations.

Main Outcome Measures

Correlations of HSP-specific T-cell counts, and serum levels of corresponding cytokine levels with RNFLT.

Results

Patients with POAG (visual field mean deviation, -4.7 ± 4.0 dB) and controls were similar in age, gender, and body mass index. Moreover, 46.9% of POAG and 60.0% of control subjects had prior cataract surgery (P = 0.48). Although no significant difference in total nonstimulated CD4+ Th1 or Treg cells was detected, patients with POAG exhibited significantly higher frequencies of Th1 cells specific for HSP27, α-crystallin, or HSP60 than controls (7.3 ± 7.9% vs. 2.6 ± 2.0%, P = 0.004; 5.8 ± 2.7% vs. 1.8 ± 1.3%, P < 0.001; 13.2 ± 13.3 vs. 4.3 ± 5.2, P = 0.01; respectively), but similar Treg specific for the same HSPs compared with controls (P ≥ 0.10 for all). Concordantly, the serum levels of IFN-γ were higher in POAG than in controls (36.2 ± 12.1 pg/ml vs. 10.0 ± 4.3 pg/ml; P < 0.001), but TGF-β1 levels did not differ. Average RNFLT of both eyes negatively correlated with HSP27- and α-crystallin-specific Th1 cell counts, and IFN-γ levels in all subjects after adjusting for age (partial correlation coefficient r = -0.31, P = 0.03; r = -0.52, p = 0.002; r = -0.72, P < 0.001, respectively).

Conclusions

Higher levels of HSP-specific Th1 cells are associated with thinner RNFLT in patients with POAG and control subjects. The significant inverse relationship between systemic HSP-specific Th1 cell count and RNFLT supports the role of these T cells in glaucomatous neurodegeneration.

Financial Disclosures

Proprietary or commercial disclosure may be found after the references.

Visual field prediction using a deep bidirectional gated recurrent unit network model

Although deep learning architecture has been used to process sequential data, only a few studies have explored the usefulness of deep learning algorithms to detect glaucoma progression. Here, we proposed a bidirectional gated recurrent unit (Bi-GRU) algorithm to predict visual field loss. In total, 5413 eyes from 3321 patients were included in the training set, whereas 1272 eyes from 1272 patients were included in the test set. Data from five consecutive visual field examinations were used as input; the sixth visual field examinations were compared with predictions by the Bi-GRU. The performance of Bi-GRU was compared with the performances of conventional linear regression (LR) and long short-term memory (LSTM) algorithms. Overall prediction error was significantly lower for Bi-GRU than for LR and LSTM algorithms. In pointwise prediction, Bi-GRU showed the lowest prediction error among the three models in most test locations. Furthermore, Bi-GRU was the least affected model in terms of worsening reliability indices and glaucoma severity. Accurate prediction of visual field loss using the Bi-GRU algorithm may facilitate decision-making regarding the treatment of patients with glaucoma.

Artificial intelligence in ophthalmology: The path to the real-world clinic

Artificial intelligence (AI) has great potential to transform healthcare by enhancing the workflow and productivity of clinicians, enabling existing staff to serve more patients, improving patient outcomes, and reducing health disparities. In the field of ophthalmology, AI systems have shown performance comparable with or even better than experienced ophthalmologists in tasks such as diabetic retinopathy detection and grading. However, despite these quite good results, very few AI systems have been deployed in real-world clinical settings, challenging the true value of these systems. This review provides an overview of the current main AI applications in ophthalmology, describes the challenges that need to be overcome prior to clinical implementation of the AI systems, and discusses the strategies that may pave the way to the clinical translation of these systems.

Data Preprocessing and Augmentation Improved Visual Field Prediction of Recurrent Neural Network with Multi-Central Datasets

INTRODUCTION

The purpose of this study was to determine whether data preprocessing and augmentation could improve visual field (VF) prediction of recurrent neural network (RNN) with multi-central datasets.

METHODS

This retrospective study collected data from five glaucoma services between June 2004 and January 2021. From an initial dataset of 331,691 VFs, we considered reliable VF tests with fixed intervals. Since the VF monitoring interval is very variable, we applied data augmentation using multiple sets of data for patients with more than eight VFs. We obtained 5,430 VFs from 463 patients and 13,747 VFs from 1,076 patients by setting the fixed test interval to 365 ± 60 days (D = 365) and 180 ± 60 days (D = 180), respectively. Five consecutive VFs were provided to the constructed RNN as input and the 6th VF was compared with the output of the RNN. The performance of the periodic RNN (D = 365) was compared to that of an aperiodic RNN. The performance of the RNN with 6 long- and short-term memory (LSTM) cells (D = 180) was compared with that of the RNN with 5-LSTM cells. To compare the prediction performance, the root mean square error (RMSE) and mean absolute error (MAE) of the total deviation value (TDV) were calculated as accuracy metrics.

RESULTS

The performance of the periodic model (D = 365) improved significantly over aperiodic model. Overall prediction error (MAE) was 2.56 ± 0.46 dB versus 3.26 ± 0.41 dB (periodic vs. aperiodic) (p < 0.001). A higher perimetric frequency was better for predicting future VF. The overall prediction error (RMSE) was 3.15 ± 2.29 dB versus 3.42 ± 2.25 dB (D = 180 vs. D = 365). Increasing the number of input VFs improved the performance of VF prediction in D = 180 periodic model (3.15 ± 2.29 dB vs. 3.18 ± 2.34 dB, p < 0.001). The 6-LSTM in the D = 180 periodic model was more robust to worsening of VF reliability and disease severity. The prediction accuracy worsened as the false-negative rate increased and the mean deviation decreased.

CONCLUSION

Data preprocessing with augmentation improved the VF prediction of the RNN model using multi-center datasets. The periodic RNN model predicted the future VF significantly better than the aperiodic RNN model.

Archetypal analysis of longitudinal visual fields for idiopathic intracranial hypertension patients presenting in a clinic setting

We previously applied archetypal analysis (AA) using visual fields (VF) from the Idiopathic Intracranial Hypertension Treatment Trial (IIHTT) to derive a model, which quantified patterns (or archetypes [ATs] of VF loss), anticipated recovery, and identified residual VF deficits. We hypothesized that AA could produce similar results using IIH VFs collected in clinical practice. We applied AA to 803 VFs from 235 eyes with IIH from an outpatient neuro-ophthalmology clinic and created a clinic-derived model of ATs, with the relative weight (RW) and average total deviation (TD) for each AT. We also created a combined-derived model from an input dataset containing the clinic VFs and 2862 VFs from the IIHTT. We used both models to decompose clinic VF into ATs of varying percent weight (PW), correlated presentation AT PW with mean deviation (MD), and evaluated final visit VFs considered "normal" by MD ≥ -2.00 dB for residual abnormal ATs. The 14-AT clinic-derived and combined-derived models revealed similar patterns of VF loss previously identified in the IIHTT model. AT1 (a normal pattern) was most prevalent in both models (RW = 51.8% for clinic-derived; 35.4% for combined-derived). Presentation AT1 PW correlated with final visit MD (r = 0.82, p < 0.001 for the clinic-derived model; r = 0.59, p < 0.001 for the combined-derived model). Both models showed ATs with similar patterns of regional VF loss. The most common patterns of VF loss in "normal" final visit VFs using each model were clinic-derived AT2 (mild global depression with enlarged blind spot; 44/125 VFs; 34%) and combined-derived AT2 (near-normal; 93/149 VFs; 62%). AA provides quantitative values for IIH-related patterns of VF loss that can be used to monitor VF changes in a clinic setting. Presentation AT1 PW is associated with the degree of VF recovery. AA identifies residual VF deficits not otherwise indicated by MD.

A deep learning model established for evaluating lid margin signs with colour anterior segment photography

OBJECTIVES

To evaluate the feasibility of applying a deep learning model to identify lid margin signs from colour anterior segment photography.

METHODS

We collected a total of 832 colour anterior segment photographs from 428 dry eye patients. Eight lid margin signs were labelled by human ophthalmologists. Eight deep learning models were constructed based on VGGNet-13 and trained to identify lid margin signs. Sensitivity, specificity, receiver operative characteristic (ROC) curves and area under the curve (AUC) were applied to evaluate the models.

RESULTS

The AUC for rounding of posterior lid margin was 0.979 and was 0.977 and 0.980 for lid margin irregularity and vascularization. For hyperkeratinization, the AUC was 0.964. The AUCs for meibomian gland orifice (MGO) retroplacement and plugging were 0.963 and 0.968. For the mucocutaneous junction (MCJ) anteroplacement and retroplacement model, the AUCs were 0.950 and 0.978. The sensitivity and specificity for rounding of posterior lid margin were 0.974 and 0.921. For irregularity, the sensitivity and specificity were 0.930 and 0.938, and those for vascularization were 0.923 and 0.961. The hyperkeratinization model achieved a sensitivity and specificity of 0.889 and 0.948. The model identifying MGO plugging and retroplacement achieved a sensitivity of 0.979 and 0.909 with a specificity of 0.867 and 0.967. The sensitivity of MCJ anteroplacement and retroplacement were 0.875/0.969, with a specificity of 0.966/0.888.

CONCLUSIONS

The deep learning model could identify lid margin signs with high sensitivity and specificity. The study provided the potentiality of applying artificial intelligence in lid margin evaluation to assist dry eye decision-making.

Combined Model of OCT Angiography and Structural OCT Parameters to Predict Paracentral Visual Field Loss in Primary Open-Angle Glaucoma

PURPOSE

To assess a model combining OCT angiography (OCTA) and OCT parameters to predict the severity of paracentral visual field (VF) loss in primary open-angle glaucoma (POAG).

DESIGN

Cross-sectional study.

PARTICIPANTS

Forty-four patients with POAG and 42 control subjects underwent OCTA and OCT imaging with a swept-source OCT device.

METHODS

The circumpapillary microvasculature was quantified for vessel density (cpVD) and flow (cpFlow) after delineation of Bruch's membrane opening and removal of large vessels. Retinal nerve fiber layer thickness (RNFLT) and Bruch's membrane opening-minimum rim width (BMO-MRW) were measured from structural OCT. Paracentral total deviation (PaTD) was defined as the average of the total deviation values within the central 10 degrees on Humphrey VF testing (24-2) for upper and lower hemifields. The OCT and OCTA parameters were measured in the affected hemisphere corresponding to the hemifield with lower PaTD for POAG patients. Models were created to predict affected PaTD based on RNFLT alone; RNFLT and BMO-MRW; OCTA alone; or RNFLT, BMO-MRW and OCTA parameters. The models were compared using coefficient of determination (r2) and Bayesian information criterion (BIC) score. Bayesian information criterion decrease of ≥6 indicates strong evidence for model improvement.

MAIN OUTCOME MEASURES

Performance of models containing OCT and OCTA parameters in predicting PaTD.

RESULTS

Patients with POAG and controls were similar in age and sex (65.9 ± 9.5 years and 38.4% male overall, P ≥ 0.56 for both). Average RNFLT, minimum RNFLT, average BMO-MRW, minimum BMO-MRW, cpVD, and cpFlow were all significantly lower (all P < 0.001) in the affected hemisphere in patients with POAG than in controls. In patients with POAG, the average mean deviation was -4.33 ± 3.25 dB; the PaTD of the affected hemifield averaged -4.55 ± 5.26 dB and correlated significantly with both OCTA and structural OCT parameters (r ≥ 0.43, P ≤ 0.004 for all). The model containing RNFLT, BMO-MRW, and OCTA parameters was superior in predicting affected PaTD (r2 = 0.47, BIC = 290.7), with higher r2 and lower BIC compared with all 3 other models.

CONCLUSIONS

A combined model of OCTA and structural OCT parameters can predict the severity of paracentral VF loss of the affected hemifield, supporting clinical utility of OCTA in patients with POAG with paracentral VF loss.

FINANCIAL DISCLOSURE(S)

Proprietary or commercial disclosure may be found after the references.

Use of artificial intelligence in forecasting glaucoma progression

Artificial intelligence (AI) has been widely used in ophthalmology for disease detection and monitoring progression. For glaucoma research, AI has been used to understand progression patterns and forecast disease trajectory based on analysis of clinical and imaging data. Techniques such as machine learning, natural language processing, and deep learning have been employed for this purpose. The results from studies using AI for forecasting glaucoma progression however vary considerably due to dataset constraints, lack of a standard progression definition and differences in methodology and approach. While glaucoma detection and screening have been the focus of most research that has been published in the last few years, in this narrative review we focus on studies that specifically address glaucoma progression. We also summarize the current evidence, highlight studies that have translational potential, and provide suggestions on how future research that addresses glaucoma progression can be improved.

Visual fields in glaucoma: Where are we now?

Visual fields are an integral part of glaucoma diagnosis and management. COVID has heightened the awareness of the potential for viral spread with the practice of visual fields modified. Mask artefacts can occur due to fogging of the inferior rim of the trail lens. Fortunately, the risk of airborne transmission when field testing is low. The 24-2c may be useful to detect early disease and the 10-2 more sensitive to detect advanced loss. The SITA faster test algorithm is able to reduce testing time thereby improving clinic efficiency, however, may show milder results for moderate or severe glaucoma. The technician has an important role of supervising the visual field performance to achieve reliable output. Home monitoring can provide earlier detection of progression and thus improve monitoring of glaucoma as well as reduce the burden of in-clinic assessments. Artificial Intelligence has been found to have high sensitivity and specificity compared to expert observers in detecting field abnormalities and progression as well as integrating structure with function. Although these advances will improve efficiency and guide accuracy, there will remain a need for clinicians to interpret the results and instigate management.

Bidirectional Neural Network Model for Glaucoma Progression Prediction

Deep learning models are usually utilized to learn from spatial data, only a few studies are proposed to predict glaucoma time progression utilizing deep learning models. In this article, we present a bidirectional recurrent deep learning model (Bi-RM) to detect prospective progressive visual field diagnoses. A dataset of 5413 different eyes from 3321 samples is utilized as the learning phase dataset and 1272 eyes are used for testing. Five consecutive diagnoses are recorded from the dataset as input and the sixth progressive visual field diagnosis is matched with the prediction of the Bi-RM. The precision metrics of the Bi-RM are validated in association with the linear regression algorithm (LR) and term memory (TM) technique. The total prediction error of the Bi-RM is significantly less than those of LR and TM. In the class prediction, Bi-RM depicts the least prediction error in all three methods in most of the testing cases. In addition, Bi-RM is not impacted by the reliability keys and the glaucoma degree.

Precision Medicine in Glaucoma: Artificial Intelligence, Biomarkers, Genetics and Redox State

Glaucoma is a multifactorial neurodegenerative illness requiring early diagnosis and strict monitoring of the disease progression. Current exams for diagnosis and prognosis are based on clinical examination, intraocular pressure (IOP) measurements, visual field tests, and optical coherence tomography (OCT). In this scenario, there is a critical unmet demand for glaucoma-related biomarkers to enhance clinical testing for early diagnosis and tracking of the disease's development. The introduction of validated biomarkers would allow for prompt intervention in the clinic to help with prognosis prediction and treatment response monitoring. This review aims to report the latest acquisitions on biomarkers in glaucoma, from imaging analysis to genetics and metabolic markers.

A deep learning model incorporating spatial and temporal information successfully detects visual field worsening using a consensus based approach

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.

Developing a Deep Learning Model to Evaluate Bulbar Conjunctival Injection with Color Anterior Segment Photographs

The present research aims to evaluate the feasibility of a deep-learning model in identifying bulbar conjunctival injection grading. Methods: We collected 1401 color anterior segment photographs demonstrating the cornea and bulbar conjunctival. The ground truth was bulbar conjunctival injection scores labeled by human ophthalmologists. Two convolutional neural network-based models were constructed and trained. Accuracy, precision, recall, F1-score, Kappa, and the area under the curve (AUC) were calculated to evaluate the efficiency of the deep learning models. The micro-average and macro-average AUC values for model grading bulbar conjunctival injection were 0.98 and 0.98, respectively. The deep learning model achieved a high accuracy of 87.12%, a precision of 87.13%, a recall of 87.12%, an F1-score of 87.07%, and Cohen's Kappa of 0.8153. The deep learning model demonstrated excellent performance in evaluating the severity of bulbar conjunctival injection, and it has the potential to help evaluate ocular surface diseases and determine disease progression and recovery.

Glaucoma progression. Clinical practice guide

OBJECTIVE

To provide general recommendations that serve as a guide for the evaluation and management of glaucomatous progression in daily clinical practice based on the existing quality of clinical evidence.

METHODS

After defining the objectives and scope of the guide, the working group was formed and structured clinical questions were formulated following the PICO (Patient, Intervention, Comparison, Outcomes) format. Once all the existing clinical evidence had been independently evaluated with the AMSTAR 2 (Assessment of Multiple Systematic Reviews) and Cochrane "Risk of bias" tools by at least two reviewers, recommendations were formulated following the Scottish Intercollegiate Guideline network (SIGN) methodology.

RESULTS

Recommendations with their corresponding levels of evidence that may be useful in the interpretation and decision-making related to the different methods for the detection of glaucomatous progression are presented.

CONCLUSIONS

Despite the fact that for many of the questions the level of scientific evidence available is not very high, this clinical practice guideline offers an updated review of the different existing aspects related to the evaluation and management of glaucomatous progression.

Applications of Artificial Intelligence and Deep Learning in Glaucoma

Diagnosis and detection of progression of glaucoma remains challenging. Artificial intelligence-based tools have the potential to improve and standardize the assessment of glaucoma but development of these algorithms is difficult given the multimodal and variable nature of the diagnosis. Currently, most algorithms are focused on a single imaging modality, specifically screening and diagnosis based on fundus photos or optical coherence tomography images. Use of anterior segment optical coherence tomography and goniophotographs is limited. The majority of algorithms designed for disease progression prediction are based on visual fields. No studies in our literature search assessed the use of artificial intelligence for treatment response prediction and no studies conducted prospective testing of their algorithms. Additional challenges to the development of artificial intelligence-based tools include scarcity of data and a lack of consensus in diagnostic criteria. Although research in the use of artificial intelligence for glaucoma is promising, additional work is needed to develop clinically usable tools.

Clinical Applications of Artificial Intelligence in Glaucoma

Ophthalmology is one of the major imaging-intensive fields of medicine and thus has potential for extensive applications of artificial intelligence (AI) to advance diagnosis, drug efficacy, and other treatment-related aspects of ocular disease. AI has made impressive progress in ophthalmology within the past few years and two autonomous AI-enabled systems have received US regulatory approvals for autonomously screening for mid-level or advanced diabetic retinopathy and macular edema. While no autonomous AI-enabled system for glaucoma screening has yet received US regulatory approval, numerous assistive AI-enabled software tools are already employed in commercialized instruments for quantifying retinal images and visual fields to augment glaucoma research and clinical practice. In this literature review (non-systematic), we provide an overview of AI applications in glaucoma, and highlight some limitations and considerations for AI integration and adoption into clinical practice.

Artificial intelligence-assisted diagnosis of ocular surface diseases

With the rapid development of computer technology, the application of artificial intelligence (AI) in ophthalmology research has gained prominence in modern medicine. Artificial intelligence-related research in ophthalmology previously focused on the screening and diagnosis of fundus diseases, particularly diabetic retinopathy, age-related macular degeneration, and glaucoma. Since fundus images are relatively fixed, their standards are easy to unify. Artificial intelligence research related to ocular surface diseases has also increased. The main issue with research on ocular surface diseases is that the images involved are complex, with many modalities. Therefore, this review aims to summarize current artificial intelligence research and technologies used to diagnose ocular surface diseases such as pterygium, keratoconus, infectious keratitis, and dry eye to identify mature artificial intelligence models that are suitable for research of ocular surface diseases and potential algorithms that may be used in the future.

Longitudinal visual field archetypal analysis of optic neuritis treated in a clinical setting

Background/aims

We previously used archetypal analysis (AA) to create a model that quantified patterns (archetypes (ATs)) of visual field (VF) loss that can predict recovery and reveal residual VF deficits from eyes in the Optic Neuritis Treatment Trial (ONTT). We hypothesised that AA could produce similar results for ON VFs collected in clinical practice.

Methods

We applied AA to a retrospective data set of 486 VFs collected in 1 neuro-ophthalmology service from 141 eyes with acute ON and typical VF defects, to create a clinic-derived AT model. We also used the ONTT-derived AT model to analyse this new dataset. We compared the findings of both models by decomposing VFs into component ATs of varying per cent weight (PW), correlating presentation AT PW with mean deviation (MD) at final visits for each eye and identifying residual deficits in VFs considered normal.

Results

Both models, each with 16 ATs, decomposed each presentation VF into 0–6 abnormal ATs representative of known patterns of ON-related VF loss. AT1, the normal pattern in both models, correlated strongly with MD for VFs collected at presentation (r=0.82; p<0.001) and the final visit (r=0.81, p<0.001). The presentation AT1 PW was associated with improvement in MD over time. 67% of VFs considered ‘normal’ at final visit had 1.2±0.4 abnormal ATs, and both models revealed similar patterns of regional VF loss.

Conclusions

AA is a quantitative method to measure change and outcome of ON VFs. Presentation AT features are associated with MD at final visit. AA identifies residual VF deficits not otherwise indicated by MD.

Everything real about unreal artificial intelligence in diabetic retinopathy and in ocular pathologies

Artificial Intelligence is a multidisciplinary field with the aim of building platforms that can make machines act, perceive, reason intelligently and whose goal is to automate activities that presently require human intelligence. From the cornea to the retina, artificial intelligence (AI) is expected to help ophthalmologists diagnose and treat ocular diseases. In ophthalmology, computerized analytics are being viewed as efficient and more objective ways to interpret the series of images and come to a conclusion. AI can be used to diagnose and grade diabetic retinopathy, glaucoma, age-related macular degeneration, cataracts, IOL power calculation, retinopathy of prematurity and keratoconus. This review article intends to discuss various aspects of artificial intelligence in ophthalmology.

An Objective and Easy-to-Use Glaucoma Functional Severity Staging System Based on Artificial Intelligence

OBJECTIVE

The objective of this study was to develop an objective and easy-to-use glaucoma staging system based on visual fields (VFs).

SUBJECTS AND PARTICIPANTS

A total of 13,231 VFs from 8077 subjects were used to develop models and 8024 VFs from 4445 subjects were used to validate models.

METHODS

We developed an unsupervised machine learning model to identify clusters with similar VF values. We annotated the clusters based on their respective mean deviation (MD). We computed optimal MD thresholds that discriminate clusters with the highest accuracy based on Bayes minimum error principle. We evaluated the accuracy of the staging system and validated findings based on an independent validation dataset.

RESULTS

The unsupervised k -means algorithm discovered 4 clusters with 6784, 4034, 1541, and 872 VFs and average MDs of 0.0 dB (±1.4: SD), -4.8 dB (±1.9), -12.2 dB (±2.9), and -23.0 dB (±3.8), respectively. The supervised Bayes minimum error classifier identified optimal MD thresholds of -2.2, -8.0, and -17.3 dB for discriminating normal eyes and eyes at the early, moderate, and advanced stages of glaucoma. The accuracy of the glaucoma staging system was 94%, based on identified MD thresholds with respect to the initial k -means clusters.

CONCLUSIONS

We discovered that 4 severity levels based on MD thresholds of -2.2, -8.0, and -17.3 dB, provides the optimal number of severity stages based on unsupervised and supervised machine learning. This glaucoma staging system is unbiased, objective, easy-to-use, and consistent, which makes it highly suitable for use in glaucoma research and for day-to-day clinical practice.

Unsupervised Machine Learning Shows Change in Visual Field Loss in the Idiopathic Intracranial Hypertension Treatment Trial

PURPOSE

We previously reported that archetypal analysis (AA), a type of unsupervised machine learning, identified and quantified patterns of visual field (VF) loss in idiopathic intracranial hypertension (IIH), referred to as archetypes (ATs). We assessed whether AT weight changes over time are consistent with changes in conventional global indices, whether visual outcome or treatment effects are associated with select AT, and whether AA reveals residual VF defects in eyes deemed normal after treatment.

DESIGN

Analysis of data collected from a randomized controlled trial.

PARTICIPANTS

Two thousand eight hundred sixty-two VFs obtained from 165 participants during the Idiopathic Intracranial Hypertension Treatment Trial (IIHTT).

METHODS

We applied a 14-AT model derived from IIHTT VFs. We examined changes in individual AT weights over time for all study eyes and evaluated differences between treatment groups. We created an AT change score to assess overall VF change from baseline. We tested threshold baseline AT weights for association with VF outcome and treatment effect at 6 months. We determined the abnormal ATs with meaningful weight at outcome for VFs with a mean deviation (MD) of -2.00 dB or more.

MAIN OUTCOME MEASURES

Individual AT weighting coefficients and MD.

RESULTS

Archetype 1 (a normal VF pattern) showed the greatest weight change for all study eyes, increasing from 11.9% (interquartile range [IQR], 0.44%-24.1%) at baseline to 31.2% (IQR, 16.0%-45.5%) at outcome (P < 0.001). Archetype 1 weight change (r = 0.795; P < 0.001) and a global score of AT change (r = 0.988; P < 0.001) correlated strongly with MD change. Study eyes with baseline AT2 (a mild diffuse VF loss pattern) weight of 44% or more (≥ 1 standard deviation more than the mean) showed higher AT2 weights at outcome than those with AT2 weight of < 44% at baseline (P < 0.001). Only the latter group showed a significant acetazolamide treatment effect. Archetypal analysis revealed residual VF loss patterns, most frequently representing mild diffuse loss and an enlarged blind spot in 64 of 66 study eyes with MD of -2.00 dB or more at outcome.

CONCLUSIONS

Archetypal analysis provides a quantitative approach to monitoring VF changes in IIH. Baseline AT features may be associated with treatment response and VF outcome. Archetypal analysis uncovers residual VF defects not otherwise revealed by MD.

Cohort Study of Race/Ethnicity and Incident Primary Open-Angle Glaucoma Characterized by Autonomously Determined Visual Field Loss Patterns

Purpose

We evaluated racial/ethnic differences in primary open-angle glaucoma (POAG) defined by machine-learning–derived regional visual field (VF) loss patterns.

Methods

Participants (N = 209,036) from the Nurses’ Health Study (NHS; 1980–2018), Nurses’ Health Study II (NHS2; 1989–2019), and Health Professionals Follow-Up Study (HPFS; 1986–2018) who were ≥40 years of age and free of glaucoma were followed biennially. Incident POAG cases (n = 1946) with reproducible VF loss were confirmed with medical records. Total deviation information from the earliest reliable glaucomatous VF for each POAG eye (n = 2564) was extracted, and machine learning analyses were used to identify optimal solutions (“archetypes”) for regional VF loss patterns. Each POAG eye was assigned a VF archetype based on the highest weighting coefficient. Multivariable-adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using per-eye Cox proportional hazards models.

Results

We identified 14 archetypes: four representing advanced loss patterns, nine of early loss, and one of no VF loss. Compared to non-Hispanic whites, black participants had higher risk of early VF loss archetypes (HR = 1.98; 95% CI, 1.48–2.66) and even higher risk for advanced loss archetypes (HR = 6.17; 95% CI, 3.69–10.32; P-contrast = 0.0002); no differences were observed for Asians or Hispanic whites. Hispanic white participants had significantly higher risks of POAG with paracentral defects and advanced superior loss; black participants had significantly higher risks of all advanced loss archetypes and three early loss patterns, including paracentral defects.

Conclusions

Blacks, compared to non-Hispanic whites, had higher risks of POAG with early central and advanced VF loss.

Translational Relevance

In POAG, risks of VF loss regional patterns derived from machine learning algorithms showed racial differences.

Patterns of Visual Field Loss in Early, Moderate, and Severe Stages of Open Angle Glaucoma

PRCIS

Even in the early stages of glaucomatous visual field defects (VFDs), 49% of the defects occurred in both hemifields and 28% involved the central 5 degrees of the visual field (VF), which may have prognostic values.

PURPOSE

The aim was to determine the patterns of glaucomatous VFDs in early, moderate and severe stages of primary open angle glaucoma, using the Glaucoma Staging Application.

METHODS

According to the Modified University of Sao Paulo Glaucoma Visual Field Staging System Classification, patients with early, moderate and advanced VFDs were selected by the Glaucoma Staging Application using all databases of the Humphrey Visual Field Analyser of a glaucoma referral practice. We analyzed one VF of the 100 patients included in each group. The analysis consisted of classifying all exams regarding the location of the defects, the hemifields involved, and the connection to the blind spot.

RESULTS

We analyzed 300 VF. In the Early group, 27% of the VFDs are connected to the physiological blind spot, 64% in the Moderate group, and 95% in the Severe group ( P <0.01). In the Early group, 28% of the defects involved the central 5 degrees of the fixation, 59% in the Moderate and 88% in the Severe group. In the Early group, 49% of the defects involved both hemifields, 80% in the Moderate and 80% in the Severe group.

CONCLUSION

With increasing glaucoma severity, VFD showed a more central pattern, connected to the blind spot, and involved both hemifields. In early disease, both hemifields were commonly affected and more than a quarter of VFD involved the central 5 degrees close to fixation. Careful monitoring of the central VF in glaucoma is suggested.

Differences in visual field loss pattern when transitioning from SITA standard to SITA faster

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.

Accuracy of Machine Learning Assisted Detection of Keratoconus: A Systematic Review and Meta-Analysis

(1) Background: The objective of this review was to synthesize available data on the use of machine learning to evaluate its accuracy (as determined by pooled sensitivity and specificity) in detecting keratoconus (KC), and measure reporting completeness of machine learning models in KC based on TRIPOD (the transparent reporting of multivariable prediction models for individual prognosis or diagnosis) statement. (2) Methods: Two independent reviewers searched the electronic databases for all potential articles on machine learning and KC published prior to 2021. The TRIPOD 29-item checklist was used to evaluate the adherence to reporting guidelines of the studies, and the adherence rate to each item was computed. We conducted a meta-analysis to determine the pooled sensitivity and specificity of machine learning models for detecting KC. (3) Results: Thirty-five studies were included in this review. Thirty studies evaluated machine learning models for detecting KC eyes from controls and 14 studies evaluated machine learning models for detecting early KC eyes from controls. The pooled sensitivity for detecting KC was 0.970 (95% CI 0.949–0.982), with a pooled specificity of 0.985 (95% CI 0.971–0.993), whereas the pooled sensitivity of detecting early KC was 0.882 (95% CI 0.822–0.923), with a pooled specificity of 0.947 (95% CI 0.914–0.967). Between 3% and 48% of TRIPOD items were adhered to in studies, and the average (median) adherence rate for a single TRIPOD item was 23% across all studies. (4) Conclusions: Application of machine learning model has the potential to make the diagnosis and monitoring of KC more efficient, resulting in reduced vision loss to the patients. This review provides current information on the machine learning models that have been developed for detecting KC and early KC. Presently, the machine learning models performed poorly in identifying early KC from control eyes and many of these research studies did not follow established reporting standards, thus resulting in the failure of these clinical translation of these machine learning models. We present possible approaches for future studies for improvement in studies related to both KC and early KC models to more efficiently and widely utilize machine learning models for diagnostic process.

Archetypal Analysis Reveals Quantifiable Patterns of Visual Field Loss in Optic Neuritis

Purpose

Identifying and monitoring visual field (VF) defects due to optic neuritis (ON) relies on qualitative clinician interpretation. Archetypal analysis (AA), a form of unsupervised machine learning, is used to quantify VF defects in glaucoma. We hypothesized that AA can identify quantifiable, ON-specific patterns (as archetypes [ATs]) of VF loss that resemble known ON VF defects.

Methods

We applied AA to a dataset of 3892 VFs prospectively collected from 456 eyes in the Optic Neuritis Treatment Trial (ONTT), and decomposed each VF into component ATs (total weight = 100%). AA of 568 VFs from 61 control eyes was used to define a minimum meaningful (≤7%) AT weight and weight change. We correlated baseline ON AT weights with global VF indices, visual acuity, and contrast sensitivity. For eyes with a dominant AT (weight ≥50%), we compared the ONTT VF classification with the AT pattern.

Results

AA generated a set of 16 ATs containing patterns seen in the ONTT. These were distinct from control ATs. Baseline study eye VFs were decomposed into 2.9 ± 1.5 ATs. AT2, a global dysfunction pattern, had the highest mean weight at baseline (36%; 95% confidence interval, 33%–40%), and showed the strongest correlation with MD (r = −0.91; P < 0.001), visual acuity (r = 0.70; P < 0.001), and contrast sensitivity (r = −0.77; P < 0.001). Of 191 baseline VFs with a dominant AT, 81% matched the descriptive classifications.

Conclusions

AA identifies and quantifies archetypal, ON-specific patterns of VF loss.

Translational Relevance

AA is a quantitative, objective method for demonstrating and monitoring change in regional VF deficits in ON.

[Artificial intelligence and glaucoma: A literature review]

In recent years, research in artificial intelligence (AI) has experienced an unprecedented surge in the field of ophthalmology, in particular glaucoma. The diagnosis and follow-up of glaucoma is complex and relies on a body of clinical evidence and ancillary tests. This large amount of information from structural and functional testing of the optic nerve and macula makes glaucoma a particularly appropriate field for the application of AI. In this paper, we will review work using AI in the field of glaucoma, whether for screening, diagnosis or detection of progression. Many AI strategies have shown promising results for glaucoma detection using fundus photography, optical coherence tomography, or automated perimetry. The combination of these imaging modalities increases the performance of AI algorithms, with results comparable to those of humans. We will discuss potential applications as well as obstacles and limitations to the deployment and validation of such models. While there is no doubt that AI has the potential to revolutionize glaucoma management and screening, research in the coming years will need to address unavoidable questions regarding the clinical significance of such results and the explicability of the predictions.

UWHVF: A Real-World, Open Source Dataset of Perimetry Tests From the Humphrey Field Analyzer at the University of Washington

Purpose

This article describes the Humphrey field analyzer (HFA) dataset from the Department of Ophthalmology at the University of Washington.

Methods

Pointwise sensitivities were extracted from HFA 24-2, stimulus III visual fields (VF). Total deviation (TD), mean TD (MTD), pattern deviation, and pattern standard deviation (PSD) were calculated. Progression analysis was performed with simple linear regression on global, regional, and pointwise values for VF series with greater than four tests spanning at least four months. VF data were extracted independently of clinical information except for patient age, gender, and laterality

Results

This dataset includes 28,943 VFs from 7248 eyes of 3871 patients. Progression was calculated for 2985 eyes from 1579 patients. Median [interquartile range] age was 64 years [54, 73], and follow-up was 2.49 years [1.11, 5.03]. Baseline MTD was −4.51 dB [−8.01, −2.65], and baseline PSD was 2.41 dB [1.7, 5.34].

Conclusion

MTD was found to decrease by −0.10 dB/yr [−0.40, 0.11] in eyes for which progression analysis was able to be performed. VFs with deep localized defects, PSD > 12 dB and MTD −15 dB to −25 dB, were plotted, visually inspected, and found to be consistent with neurologic or glaucomatous VFs from patients. For a small number of tests, extracted sensitivity values were compared to corresponding printouts and confirmed to match.

Translational Relevance

This open access pointwise VF dataset serves as a source of raw data for investigation such as VF behavior, clinical comparisons to trials, and development of new machine learning algorithms.

Artificial intelligence applied to ophthalmology and optometry: A citation network analysis

PURPOSE

The objective of this study is to analyse co-authorship and co-citation networks of publications in the field of artificial intelligence in ophthalmology and optometry. As well as, identify the different areas of research and the most cited publication.

METHOD

A search of publications was performed in the Web of Science database for the period from 1977 to December 2021, using the term "Artificial Intelligence AND (Ophthalmol* OR optometry)". The analysis of the publication was carried out using the Citation Network Explorer, VOSviewer and CiteSpace software.

RESULTS

1086 publications and 2348 citation networks were found. 2020 was the year with the highest number of publications, a total of 351 publications and 115 citation networks. The most cited publication was "Clinically applicable deep learning for diagnosis and referral in retinal disease" published by De Fauw et al. in 2018, with a citation index of 723. Through the clustering function, three groups were found that cover the main research areas in this field: retinal pathology, anterior segment and glaucoma.

CONCLUSIONS

The citation network analysis offers an in-depth analysis of scientific publications and the adoption of new topics and fields of research. The results of an exhaustive analysis of citation networks in artificial intelligence in the field of ophthalmology and optometry are presented since the publication of the first article in 1977.

Can Artificial Intelligence Predict Glaucomatous Visual Field Progression? A Spatial-Ordinal Convolutional Neural Network Model

PURPOSE

To develop an artificial neural network model incorporating both spatial and ordinal approaches to predict glaucomatous visual field (VF) progression.

DESIGN

Cohort study. Methods From a cohort of primary open-angle glaucoma patients, 9212 eyes of 6047 patients who underwent regular reliable VF examinations for >4 years were included. We constructed all possible spatial-ordinal tensors by stacking 3 consecutive VF tests (VF-blocks) with at least 3 years of follow-up. Trend-based, event-based, and combined criteria were defined to determine the progression. VF-blocks were considered "progressed" if progression occurred within 3 years; the progression was further confirmed after 3 years. We constructed 6 convolutional neural network (NN) models and 2 linear models: regression on global indices and pointwise linear regression (PLR). We compared area under the receiver operating characteristic curve (AUROC) of each model for the prediction of glaucomatous VF progression.

RESULTS

Among 43,260 VF-blocks, 4406 (10.2%), 4376 (10.1%), and 2394 (5.5%) VF-blocks were classified as progression-based on trend-based and event-based and combined criteria. For all 3 criteria, the progression group was significantly older and had worse initial MD and VF index (VFI) than the nonprogression group (P < .001 for all). The best-performing NN model had an AUROC of 0.864 with a sensitivity of 0.42 at a specificity of 0.95. In contrast, an AUROC of 0.611 was estimated from a sensitivity of 0.28 at a specificity of 0.84 for the PLR.

CONCLUSIONS

The NN models incorporating spatial-ordinal characteristics demonstrated significantly better performance than the linear models in the prediction of glaucomatous VF progression.

A Case for The Use of Artificial Intelligence in Glaucoma Assessment

We hypothesize that artificial intelligence applied to relevant clinical testing in glaucoma has the potential to enhance the ability to detect glaucoma. This premise was discussed at the recent Collaborative Community for Ophthalmic Imaging meeting, "The Future of Artificial Intelligence-Enabled Ophthalmic Image Interpretation: Accelerating Innovation and Implementation Pathways," held virtually September 3-4, 2020. The Collaborative Community in Ophthalmic Imaging (CCOI) is an independent self-governing consortium of stakeholders with broad international representation from academic institutions, government agencies, and the private sector whose mission is to act as a forum for the purpose of helping speed innovation in healthcare technology. It was one of the first two such organizations officially designated by the FDA in September 2019 in response to their announcement of the collaborative community program as a strategic priority for 2018-2020. Further information on the CCOI can be found online at their website (https://www.cc-oi.org/about). Artificial intelligence for glaucoma diagnosis would have high utility globally, as access to care is limited in many parts of the world and half of all people with glaucoma are unaware of their illness. The application of artificial intelligence technology to glaucoma diagnosis has the potential to broadly increase access to care worldwide, in essence flattening the Earth by providing expert level evaluation to individuals even in the most remote regions of the planet.

Variability and Power to Detect Progression of Different Visual Field Patterns

PURPOSE

To compare the variability and ability to detect visual field (VF) progression of 24-2, central 12 locations of the 24-2 and 10-2 VF tests in eyes with abnormal VFs.

DESIGN

Retrospective, multisite cohort.

PARTICIPANTS

A total of 52 806 24-2 and 11 966 10-2 VF tests from 7307 eyes from the Glaucoma Research Network database were analyzed. Only eyes with ≥ 5 visits and ≥ 2 years of follow-up were included.

METHODS

Linear regression models were used to calculate the rates of mean deviation (MD) change (slopes), whereas their residuals were used to assess variability across the entire MD range. Computer simulations (n = 10 000) based on real MD residuals of our sample were performed to estimate power to detect significant progression (P < 5%) at various rates of MD change.

MAIN OUTCOME MEASURES

Time required to detect progression.

RESULTS

For all 3 patterns, the MD variability was highest within the -5 to -20 decibel (dB) range and consistently lower with the 10-2 compared with 24-2 or central 24-2. Overall, time to detect confirmed significant progression at 80% power was the lowest with 10-2 VF, with a decrease of 14.6% to 18.5% when compared with 24-2 and a decrease of 22.9% to 26.5% when compared with central 24-2.

CONCLUSIONS

Time to detect central VF progression was reduced with 10-2 MD compared with 24-2 and C24-2 MD in glaucoma eyes in this large dataset, in part because 10-2 tests had lower variability. These findings contribute to current evidence of the potential value of 10-2 testing in the clinical management of patients with glaucoma and in clinical trial design.

Measures of disease activity in glaucoma

Glaucoma is the leading cause of irreversible blindness globally which significantly affects the quality of life and has a substantial economic impact. Effective detective methods are necessary to identify glaucoma as early as possible. Regular eye examinations are important for detecting the disease early and preventing deterioration of vision and quality of life. Current methods of measuring disease activity are powerful in describing the functional and structural changes in glaucomatous eyes. However, there is still a need for a novel tool to detect glaucoma earlier and more accurately. Tear fluid biomarker analysis and new imaging technology provide novel surrogate endpoints of glaucoma. Artificial intelligence is a post-diagnostic tool that can analyse ophthalmic test results. A detail review of currently used clinical tests in glaucoma include intraocular pressure test, visual field test and optical coherence tomography are presented. The advanced technologies for glaucoma measurement which can identify specific disease characteristics, as well as the mechanism, performance and future perspectives of these devices are highlighted. Applications of AI in diagnosis and prediction in glaucoma are mentioned. With the development in imaging tools, sensor technologies and artificial intelligence, diagnostic evaluation of glaucoma must assess more variables to facilitate earlier diagnosis and management in the future.

The Impact of Artificial Intelligence and Deep Learning in Eye Diseases: A Review

Artificial intelligence (AI) is a subset of computer science dealing with the development and training of algorithms that try to replicate human intelligence. We report a clinical overview of the basic principles of AI that are fundamental to appreciating its application to ophthalmology practice. Here, we review the most common eye diseases, focusing on some of the potential challenges and limitations emerging with the development and application of this new technology into ophthalmology.

Unsupervised Machine Learning Identifies Quantifiable Patterns of Visual Field Loss in Idiopathic Intracranial Hypertension

Purpose

Archetypal analysis, a form of unsupervised machine learning, identifies archetypal patterns within a visual field (VF) dataset such that any VF is described as a weighted sum of its archetypes (ATs) and has been used to quantify VF defects in glaucoma. We applied archetypal analysis to VFs affected by nonglaucomatous optic neuropathy caused by idiopathic intracranial hypertension (IIH).

Methods

We created an AT model from 2862 VFs prospectively collected from 330 eyes in the IIH Treatment Trial (IIHTT). We compared baseline IIH AT patterns with their descriptive VF classifications from the IIHTT.

Results

The optimum IIH AT model yielded 14 ATs resembling VF patterns reported in the IIHTT. Baseline VFs contained four or fewer meaningful ATs in 147 (89%) of study eyes. AT2 (mild general VF depression pattern) demonstrated the greatest number of study eyes with meaningful AT weight at baseline (n = 114), followed by AT1 (n = 91). Other ATs captured patterns of blind spot enlargement, hemianopia, arcuate, nasal defects, and more nonspecific patterns of general VF depression. Of all ATs, AT1 (normal pattern) had the strongest correlation with mean deviation (r = 0.69, P < 0.001). For 65 of the 93 VFs with a dominant AT, this AT matched the expert classification.

Conclusions

Archetypal analysis identifies quantifiable, archetypal VF defects that resemble those commonly seen in IIH.

Translational Relevance

Archetypal analysis provides a quantitative, objective method of measuring and monitoring disease-specific regional VF defects in IIH.

Predicting Global Test-Retest Variability of Visual Fields in Glaucoma

PURPOSE

To model the global test-retest variability of visual fields (VFs) in glaucoma.

DESIGN

Retrospective cohort study.

PARTICIPANTS

Test-retest VFs from 4044 eyes of 4044 participants.

METHODS

We selected 2 reliable VFs per eye measured with the Humphrey Field Analyzer (Swedish interactive threshold algorithm 24-2) within 30 days of each other. Each VF had fixation losses (FLs) of 33% or less, false-negative results (FNRs) of 20% or less, and false-positive results (FPRs) of 20% or less. Stepwise linear regression was applied to select the model best predicting the global test-retest variability from 3 categories of features of the first VF: (1) base parameters (age, mean deviation, pattern standard deviation, glaucoma hemifield test results, FPR, FNR, and FL); (2) total deviation (TD) at each location; and (3) computationally derived archetype VF loss patterns. The global test-retest variability was defined as root mean square deviation (RMSD) of TD values at all 52 VF locations.

MAIN OUTCOME MEASURES

Archetype models to predict the global test-retest variability.

RESULTS

The mean ± standard deviation of the root mean square deviation was 4.39 ± 2.55 dB. Between the 2 VF tests, TD values were correlated more strongly in central than in peripheral VF locations (intraclass coefficient, 0.66-0.89; P < 0.001). Compared with the model using base parameters alone (adjusted R2 = 0.45), adding TD values improved prediction accuracy of the global variability (adjusted R2 = 0.53; P < 0.001; Bayesian information criterion [BIC] decrease of 527; change of >6 represents strong improvement). Lower TD sensitivity in the outermost peripheral VF locations was predictive of higher global variability. Adding archetypes to the base model improved model performance with an adjusted R2 of 0.53 (P < 0.001) and lowering of BIC by 583. Greater variability was associated with concentric peripheral defect, temporal hemianopia, inferotemporal defect, near total loss, superior peripheral defect, and central scotoma (listed in order of decreasing statistical significance), and less normal VF results and superior paracentral defect.

CONCLUSIONS

Inclusion of archetype VF loss patterns and TD values based on first VF improved the prediction of the global test-retest variability than using traditional global VF indices alone.

Development and Comparison of Machine Learning Algorithms to Determine Visual Field Progression

Purpose

To develop and test machine learning classifiers (MLCs) for determining visual field progression.

Methods

In total, 90,713 visual fields from 13,156 eyes were included. Six different progression algorithms (linear regression of mean deviation, linear regression of the visual field index, Advanced Glaucoma Intervention Study algorithm, Collaborative Initial Glaucoma Treatment Study algorithm, pointwise linear regression [PLR], and permutation of PLR) were applied to classify each eye as progressing or stable. Six MLCs were applied (logistic regression, random forest, extreme gradient boosting, support vector classifier, convolutional neural network, fully connected neural network) using a training and testing set. For MLC input, visual fields for a given eye were divided into the first and second half and each location averaged over time within each half. Each algorithm was tested for accuracy, sensitivity, positive predictive value, and class bias with a subset of visual fields labeled by a panel of three experts from 161 eyes.

Results

MLCs had similar performance metrics as some of the conventional algorithms and ranged from 87% to 91% accurate with sensitivity ranging from 0.83 to 0.88 and specificity from 0.92 to 0.96. All conventional algorithms showed significant class bias, meaning each individual algorithm was more likely to grade uncertain cases as either progressing or stable (P ≤ 0.01). Conversely, all MLCs were balanced, meaning they were equally likely to grade uncertain cases as either progressing or stable (P ≥ 0.08).

Conclusions

MLCs showed a moderate to high level of accuracy, sensitivity, and specificity and were more balanced than conventional algorithms.

Translational Relevance

MLCs may help to determine visual field progression.