Clinical Correlates of Computationally Derived Visual Field Defect Archetypes in Patients from a Glaucoma Clinic

Diagnosing glaucoma in primary eye care and the role of Artificial Intelligence applications for reducing the prevalence of undetected glaucoma in Australia

Glaucoma is the commonest cause of irreversible blindness worldwide, with over 70% of people affected remaining undiagnosed. Early detection is crucial for halting progressive visual impairment in glaucoma patients, as there is no cure available. This narrative review aims to: identify reasons for the significant under-diagnosis of glaucoma globally, particularly in Australia, elucidate the role of primary healthcare in glaucoma diagnosis using Australian healthcare as an example, and discuss how recent advances in artificial intelligence (AI) can be implemented to improve diagnostic outcomes. Glaucoma is a prevalent disease in ageing populations and can have improved visual outcomes through appropriate treatment, making it essential for general medical practice. In countries such as Australia, New Zealand, Canada, USA, and the UK, optometrists serve as the gatekeepers for primary eye care, and glaucoma detection often falls on their shoulders. However, there is significant variation in the capacity for glaucoma diagnosis among eye professionals. Automation with Artificial Intelligence (AI) analysis of optic nerve photos can help optometrists identify high-risk changes and mitigate the challenges of image interpretation rapidly and consistently. Despite its potential, there are significant barriers and challenges to address before AI can be deployed in primary healthcare settings, including external validation, high quality real-world implementation, protection of privacy and cybersecurity, and medico-legal implications. Overall, the incorporation of AI technology in primary healthcare has the potential to reduce the global prevalence of undiagnosed glaucoma cases by improving diagnostic accuracy and efficiency.

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.

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.

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.

Archetypal analysis of visual fields in optic neuritis reveals functional biomarkers associated with outcome and treatment response

BACKGROUND AND OBJECTIVES

Archetypal analysis (AA), a form of unsupervised machine learning, can identify quantifiable visual field (VF) patterns seen in optic neuritis (ON), known as archetypes (ATs). We hypothesized that AT weight changes over time would reflect the course of recovery and the effects of therapy in ON. We explored whether baseline AT weights would be associated with VF status at the clinical trial outcome and if ATs would indicate residual VF defects in eyes with mean deviation (MD) ≥ -2.00 at six months.

METHODS

We used a published 16-AT model derived from 3892 Optic Neuritis Treatment Trial VFs (456 eyes) for all analyses. We measured AT weight changes over the six-month study period and used asymptotic regression to analyze the rate of change. We compared AT weights at six months between treatment groups. We evaluated associations between baseline AT weight thresholds and VF outcome or treatment effect. We calculated residual AT weights in eyes with MD ≥ -2.00 dB at six months.

RESULTS

Over six months, AT1 (a normal VF pattern) demonstrated the greatest median weight change, increasing from 0.00% (IQR 0.00-0.00%) at baseline to 60.0% (IQR 38.3-70.8%) at six months (p < 0.001). At outcome, the intravenous methylprednisolone (IVMP) group had the highest median AT1 weight (IVMP: 63.3%, IQR 51.3-72.8%; placebo: 56.2%, IQR 35.1-71.6%; prednisone 58.3%, IQR 35.1-71.6%; p = 0.019). Eyes with AT1 weight ≥ 19% at baseline had superior median MD values (-0.91 vs. -2.07 dB, p < 0.001) and AT1 weights (70.8% vs. 57.8% p < 0.001) at six months. Only eyes with AT1 weight < 19% at baseline showed a treatment benefit for IVMP, with a higher six-month median AT1 weight compared to placebo (p = 0.015) and prednisone (p = 0.016), and a higher median MD compared to placebo (p = 0.027). At six months, 182 (80.2%) VFs with MD ≥ -2.00 had at least one abnormal AT.

DISCUSSION

Changes in quantifiable, archetypal patterns of VF loss reflect recovery in ON. Machine learning analysis of the VFs in optic neuritis reveals associations with response to therapy and VF outcome, and uncovers residual deficits, not readily seen with standard evaluations.

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.

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.

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.

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.

The Prevalence of Autoimmune Diseases in Patients with Primary Open-Angle Glaucoma Undergoing Ophthalmic Surgeries

PURPOSE

To assess the prevalence of autoimmune disease (AiD) in patients with primary open-angle glaucoma (POAG) undergoing ophthalmic surgery.

DESIGN

Retrospective, cross-sectional study.

PARTICIPANTS

Patients with POAG undergoing any ophthalmic surgery and control subjects undergoing cataract surgery at the Massachusetts Eye and Ear from March 2019 to April 2020.

METHODS

All available medical records with patient demographics, ocular, and medical conditions were reviewed. Differences in AiD prevalence were assessed and adjusted for covariates using multiple logistic regression. Additionally, a subgroup analysis comparing the POAG patients with and without AiD was performed.

MAIN OUTCOME MEASURES

To assess the prevalence of AiD based on the American Autoimmune Related Diseases Association list.

RESULTS

A total of 172 patients with POAG and 179 controls were included. The overall prevalence of AiD was 17.4% in the POAG group and 10.1% in the controls (P = 0.044); 6.4% of POAG patients and 3.4% of controls had more than 1 AiD (P = 0.18). The most prevalent AiDs in POAG group were rheumatoid arthritis (4.6%) and psoriasis (4.1%), which were also the most common in controls (2.8% each). In a fully adjusted multiple logistic regression analysis accounting for steroid use, having an AiD was associated with 2.62-fold increased odds of POAG relative to controls (95% confidence interval, 1.27-5.36, P = 0.009); other risk factors for POAG derived from the analysis included age (odds ratio [OR], 1.04, P = 0.006), diabetes mellitus (OR, 2.31, P = 0.008), and non-White ethnicity (OR, 4.75, P < 0.001). In a case-only analysis involving the eye with worse glaucoma, there was no statistical difference in visual field mean deviation or retinal nerve fiber layer (RNFL) thickness in POAG patients with AiD (n = 30) and without AiD (n = 142, P > 0.13, for both).

CONCLUSIONS

A higher prevalence of AiD was found in POAG patients compared with control patients undergoing ophthalmic surgery. The presence of AiD was associated with increased risk for POAG after adjusting for covariates. Additional factors may have prevented a difference in RNFL thickness in POAG patients with and without AiD. Autoimmunity should be explored further in the pathogenesis of POAG.

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.

Inter-Eye Association of Visual Field Defects in Glaucoma and Its Clinical Utility

Purpose

To investigate intereye associations of visual field (VF) defects.

Methods

We selected 24-2 VF pairs of both eyes from 63,604 patients tested on the same date with mean deviation (MD) ≥ −12 dB. VFs were decomposed into one normal and 15 defect patterns previously identified using archetypal analysis. VF pattern weighting coefficients were correlated between the worse and better eyes, as defined by MD. VF defect patterns (weighting coefficients > 10%) in the better eye were predicted from weighting coefficients of the worse eye by logistic regression models, which were evaluated by area under the receiver operating characteristic curve (AUC).

Results

Intereye correlations of archetypal VF patterns were strongest for the same defect pattern between fellow eyes. The AUCs for predicting the presence of 15 defect patterns in the better eye based on the worse eye ranged from 0.69 (superior nasal step) to 0.92 (near total loss). The AUC for predicting superior paracentral loss was 0.89. Superior paracentral loss in the better eye was positively correlated with coefficients of superior paracentral loss, central scotoma, superior altitudinal defect, nasal hemianopia, and inferior paracentral loss in the worse eye, and negatively correlated with coefficients of the normal VF, superior peripheral defect, concentric peripheral defect, and temporal wedge. The parameters are presented in the descending order of statistical significance.

Conclusions

VF patterns of the worse eye are predictive of VF defects in the better eye.

Translational Relevance

Our models can potentially assist clinicians to better interpret VF loss under measurement uncertainty.

Ultrasound cyclo plasty for the management of refractory glaucoma in chinese patients: a before-after study

BACKGROUND

Cyclocryotherapy and transscleral cyclophotocoagulation are commonly used procedures for the treatment of refractory glaucoma, but still with many complications. Ultrasound cyclo plasty (UCP) is a novel technique that determines a selective and more precise coagulation necrosis of the ciliary body to reduce intraocular pressure (IOP), but its efficacy and safety have been less investigated in Chinese population. This study aimed to evaluate the clinical efficacy and safety of UCP in Chinese patients with refractory glaucoma.

METHODS

We designed a prospective, before-after study involving 36 eyes of 36 patients with refractory glaucoma that underwent uneventful UCP. Mean IOP, mean IOP reduction and subjective pain scale scores before and after UCP were compared to evaluate the efficacy of UCP in these patients. Procedural success defined as no abnormality of the treatment sites, and complications were recorded, along with confirmation of the safety of UCP.

RESULTS

The 36 UCP patients had a mean IOP of 53.61 ± 12.4 mmHg and a mean VAS score of 5.69 ± 3.02 preoperatively. Successful operation was achieved in 28 patients, with a success rate of 77.8%. In the follow-up observation at day 1, day 7, and month 1, 2, 3 and 6 postoperatively, both mean IOP and mean VAS score were significantly lower than those before operation (both P < 0.0001). The median baseline IOP reduction ranged from 22 to 36%. The mean reduction was 42.5% in 36 patients when taking IOP at the last follow-up visit as the last. No significant changes in visual acuity were achieved in 4 patients after UCP, and no adverse outcomes were present in other patients after timely treatment of complications such as conjunctival hyperemia, subconjunctival hemorrhage, or hyphema.

CONCLUSIONS

UCP is a novel yet reliable option for Chinese patients with refractory glaucoma since its high efficacy and safety.

Artificial Intelligence Algorithms to Diagnose Glaucoma and Detect Glaucoma Progression: Translation to Clinical Practice

Purpose

This concise review aims to explore the potential for the clinical implementation of artificial intelligence (AI) strategies for detecting glaucoma and monitoring glaucoma progression.

Methods

Nonsystematic literature review using the search combinations “Artificial Intelligence,” “Deep Learning,” “Machine Learning,” “Neural Networks,” “Bayesian Networks,” “Glaucoma Diagnosis,” and “Glaucoma Progression.” Information on sensitivity and specificity regarding glaucoma diagnosis and progression analysis as well as methodological details were extracted.

Results

Numerous AI strategies provide promising levels of specificity and sensitivity for structural (e.g. optical coherence tomography [OCT] imaging, fundus photography) and functional (visual field [VF] testing) test modalities used for the detection of glaucoma. Area under receiver operating curve (AROC) values of > 0.90 were achieved with every modality. Combining structural and functional inputs has been shown to even more improve the diagnostic ability. Regarding glaucoma progression, AI strategies can detect progression earlier than conventional methods or potentially from one single VF test.

Conclusions

AI algorithms applied to fundus photographs for screening purposes may provide good results using a simple and widely accessible test. However, for patients who are likely to have glaucoma more sophisticated methods should be used including data from OCT and perimetry. Outputs may serve as an adjunct to assist clinical decision making, whereas also enhancing the efficiency, productivity, and quality of the delivery of glaucoma care. Patients with diagnosed glaucoma may benefit from future algorithms to evaluate their risk of progression. Challenges are yet to be overcome, including the external validity of AI strategies, a move from a “black box” toward “explainable AI,” and likely regulatory hurdles. However, it is clear that AI can enhance the role of specialist clinicians and will inevitably shape the future of the delivery of glaucoma care to the next generation.

Translational Relevance

The promising levels of diagnostic accuracy reported by AI strategies across the modalities used in clinical practice for glaucoma detection can pave the way for the development of reliable models appropriate for their translation into clinical practice. Future incorporation of AI into healthcare models may help address the current limitations of access and timely management of patients with glaucoma across the world.

Artificial Intelligence Classification of Central Visual Field Patterns in Glaucoma

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.

Characterization of Central Visual Field Loss in End-stage Glaucoma by Unsupervised Artificial Intelligence

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.

The impact of artificial intelligence in the diagnosis and management of glaucoma

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.

Clinical Evaluation of Swedish Interactive Thresholding Algorithm-Faster Compared With Swedish Interactive Thresholding Algorithm-Standard in Normal Subjects, Glaucoma Suspects, and Patients With Glaucoma

PURPOSE

To compare the visual fields results obtained using the Swedish interactive thresholding algorithm-Standard (SS) and the Swedish interactive thresholding algorithm-Faster (SFR) in normal subjects, glaucoma suspects, and patients with glaucoma and to quantify potential time-saving benefits of the SFR algorithm.

DESIGN

Prospective, cross-sectional study.

METHODS

One randomly selected eye from 364 patients (77 normal subjects, 178 glaucoma suspects, and 109 patients with glaucoma) seen in a single institution underwent testing using both SS and SFR on the Humphrey Field Analyzer. Cumulative test time using each algorithm was compared after accounting for different rates of test reliability. Pointwise and cluster analysis was performed to determine whether there were systematic differences between algorithms.

RESULTS

Using SFR had a greater rate of unreliable results (29.3%) compared with SS (7.7%, P < .0001). This was mainly because of high false positive rates and seeding point errors. However, modeled test times showed that using SFR could obtain a greater number of reliable results within a shorter period of time. SFR resulted in higher sensitivity values (on average 0.5 dB for patients with glaucoma) that was greater under conditions of field loss (<19 dB). Cluster analysis showed no systematic patterns of sensitivity differences between algorithms.

CONCLUSIONS

After accounting for different rates of test reliability, SFR can result in significant time savings compared with SS. Clinicians should be cognizant of false positive rates and seeding point errors as common sources of error for SFR. Results between algorithms are not directly interchangeable, especially if there is a visual field deficit <19 dB.

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

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.

Machine Learning in the Detection of the Glaucomatous Disc and Visual Field

Glaucoma is the leading cause of irreversible blindness worldwide. Early detection is of utmost importance as there is abundant evidence that early treatment prevents disease progression, preserves vision, and improves patients' long-term quality of life. The structure and function thresholds that alert to the diagnosis of glaucoma can be obtained entirely via digital means, and as such, screening is well suited to benefit from artificial intelligence and specifically machine learning. This paper reviews the concepts and current literature on the use of machine learning for detection of the glaucomatous disc and visual field.

Application of Pattern Recognition Analysis to Optimize Hemifield Asymmetry Patterns for Early Detection of Glaucoma

Purpose

To assess the diagnostic utility of a new hemifield asymmetry analysis derived using pattern recognition contrast sensitivity isocontours (CSIs) within the Humphrey Field Analyzer (HFA) 24-2 visual field (VF) test grid. The performance of an optimal CSI-derived map was compared against a commercially available clustering method (Glaucoma Hemifield Test, GHT).

Methods

Five hundred VF results of 116 healthy subjects were used to determine normative distribution limits for comparisons. Pattern recognition analysis was applied to HFA 24-2 sensitivity data to determine CSI theme maps delineating clusters for hemifield comparisons. Then, 1019 VF results from 228 glaucoma patients were assessed using different clustering methods to determine the true-positive rate. We also assessed additional 354 VF results of 145 healthy subjects to determine the false-positive rate.

Results

The optimum clustering method was the CSI-derived seven-theme class map, which identified more glaucomatous VFs compared with the GHT map. The seven-class theme map also identified more cases compared with the five-, six-, and eight-class maps, suggesting no effect of number of clusters. Integrating information regarding the location of glaucomatous defects to the CSI clusters did not improve detection rate.

Conclusions

A clustering map derived using CSIs improved detection of glaucomatous VFs compared with the currently available GHT. An optimized CSI-derived map may serve as an additional means to aid earlier detection of glaucoma.

Translational Relevance

Pattern recognition–derived theme maps provide a means for guiding test point selection for asymmetry analysis in glaucoma assessment.

Cerebrospinal Fluid Pressure: Revisiting Factors Influencing Optic Nerve Head Biomechanics

Purpose

To model the sensitivity of the optic nerve head (ONH) biomechanical environment to acute variations in IOP, cerebrospinal fluid pressure (CSFP), and central retinal artery blood pressure (BP).

Methods

We extended a previously published numerical model of the ONH to include 24 factors representing tissue anatomy and mechanical properties, all three pressures, and constraints on the optic nerve (CON). A total of 8340 models were studied to predict factor influences on 98 responses in a two-step process: a fractional factorial screening analysis to identify the 16 most influential factors, followed by a response surface methodology to predict factor effects in detail.

Results

The six most influential factors were, in order: IOP, CON, moduli of the sclera, lamina cribrosa (LC) and dura, and CSFP. IOP and CSFP affected different aspects of ONH biomechanics. The strongest influence of CSFP, more than twice that of IOP, was on the rotation of the peripapillary sclera. CSFP had similar influence on LC stretch and compression to moduli of sclera and LC. On some ONHs, CSFP caused large retrolamina deformations and subarachnoid expansion. CON had a strong influence on LC displacement. BP overall influence was 633 times smaller than that of IOP.

Conclusions

Models predict that IOP and CSFP are the top and sixth most influential factors on ONH biomechanics. Different IOP and CSFP effects suggest that translaminar pressure difference may not be a good parameter to predict biomechanics-related glaucomatous neuropathy. CON may drastically affect the responses relating to gross ONH geometry and should be determined experimentally.

Reversal of Glaucoma Hemifield Test Results and Visual Field Features in Glaucoma

PURPOSE

To develop a visual field (VF) feature model to predict the reversal of glaucoma hemifield test (GHT) results to within normal limits (WNL) after 2 consecutive outside normal limits (ONL) results.

DESIGN

Retrospective cohort study.

PARTICIPANTS

Visual fields of 44 503 eyes from 26 130 participants.

METHODS

Eyes with 3 or more consecutive reliable VFs measured with the Humphrey Field Analyzer (Swedish interactive threshold algorithm standard 24-2) were included. Eyes with ONL GHT results for the 2 baseline VFs were selected. We extracted 3 categories of VF features from the baseline tests: (1) VF global indices (mean deviation [MD] and pattern standard deviation), (2) mismatch between baseline VFs, and (3) VF loss patterns (archetypes). Logistic regression was applied to predict the GHT results reversal. Cross-validation was applied to evaluate the model on testing data by the area under the receiver operating characteristic curve (AUC). We ascertained clinical glaucoma status on a patient subset (n = 97) to determine the usefulness of our model.

MAIN OUTCOME MEASURES

Predictive models for GHT results reversal using VF features.

RESULTS

For the 16 604 eyes with 2 initial ONL results, the prevalence of a subsequent WNL result increased from 0.1% for MD < -12 dB to 13.8% for MD ≥-3 dB. Compared with models with VF global indices, the AUC of predictive models increased from 0.669 (MD ≥-3 dB) and 0.697 (-6 dB ≤ MD < -3 dB) to 0.770 and 0.820, respectively, by adding VF mismatch features and computationally derived VF archetypes (P < 0.001 for both). The GHT results reversal was associated with a large mismatch between baseline VFs. Moreover, the GHT results reversal was associated more with VF archetypes of nonglaucomatous loss, severe widespread loss, and lens rim artifacts. For a subset of 97 eyes, using our model to predict absence of glaucoma based on clinical evidence after 2 ONL results yielded significantly better prediction accuracy (87.7%; P < 0.001) than predicting GHT results reversal (68.8%) with a prescribed specificity 67.7%.

CONCLUSIONS

Using VF features may predict the GHT results reversal to WNL after 2 consecutive ONL results.

Association of SIX1/SIX6 locus polymorphisms with regional circumpapillary retinal nerve fibre layer thickness: The Nagahama study

SIX1 and SIX6 are glaucoma susceptibility genes. Previous reports indicate that the single nucleotide polymorphism (SNP) rs33912345 in SIX6 is associated with inferior circumpapillary retinal nerve fibre layer (cpRNFL) thickness (cpRNFLT). Although the region of visual field defect in glaucoma patients is directly related to cpRNFL thinning, a detailed sector analysis has not been performed in genetic association studies. In the present study, we evaluated 26 tagging SNPs in the SIX1/SIX6 locus ±50 kb region in a population of 2,306 Japanese subjects with 4- and 32-sector cpRNFLT analysis. While no SNPs showed a significant association with cpRNFLT in the 4-sectored analysis, the finer 32-sector assessment clearly showed a significant association between rs33912345 in the SIX1/SIX6 locus with inferior cpRNFL thinning at 292.5-303.8° (β = -4.55, P = 3.0 × 10^-5). Furthermore, the fine-sectored cpRNFLT analysis indicated that SIX1/SIX6 polymorphisms would affect cpRNFL thinning at 281.3-303.8°, which corresponds to parafoveal scotoma in a visual field test of glaucoma patients.