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Clinical imaging provided by optical coherence tomography (OCT) and its variant, OCT-angiography (OCT-A), has revolutionised eyecare practice. The imaging techniques allow for the identification and quantification of ocular structures, supporting the diagnosis and prognosis of eye disease. In this review, an overview of the usefulness of OCT-A imaging in the diagnosis and management of a range of ocular conditions is provided when used in isolation or in combination with other imaging modalities and measures of visual function (visual field results). OCT-A imaging has the capacity to identify and quantify ocular vasculature non-invasively, thereby assisting the clinician in the diagnosis or to determine the efficacy of intervention in ocular conditions impacting retinal vasculature. Thus, additional clinically useful information can be obtained in eye diseases involving conditions such as those impacting retinal vessel occlusion, in diabetic retinopathy, inherited retinal dystrophy, age-related macular degeneration, choroidal neovascularisation and optic nerve disorders. Through a clinical case series, various ocular conditions are reviewed, and the impact of OCT-A imaging is discussed. Although OCT-A imaging has great promise and is already used in clinical management, there is a lack of set standards to characterise altered vascular features in disease and consequently for prognostication, primarily due to a lack of large-scale clinical trials and variability in OCT-A algorithms when generating quantitative parameters. [ABSTRACT FROM AUTHOR]

A method for determining 10-2 deployment in glaucoma with the goal of detecting additional visual field sensitivity for the purpose of functional monitoring is proposed. To provide a pilot method for determining when to deploy the 10-2 visual field (VF) test grid in glaucoma by characterising the 'functional vulnerability zone'. The cross-sectional 24-2 (central 12 locations) and 10-2 VF results from 133 eyes of 133 glaucoma subjects were used to describe the central Hill of Vision using VF sensitivity. The 'volume' (defined using arbitrary units, A.U.) under the Hill was calculated. A greater A.U. on the 10-2 indicated a functional vulnerability zone (FVZ), signifying additional clinical dynamic range for potential future monitoring. The main outcome measures were calculated A.U. and 24-2 factors which were significantly related to A.U. differences between 24-2 and 10-2. Over 55% of patients had an FVZ (A.U. greater using 10-2). Several 24-2 features (worse mean deviation, worse central 24-2 mean defect, and a higher proportion of defective locations) were significant in the FVZ cohort compared to non-FVZ. 24-2 mean deviation levels at which 10-2 may be favoured were low at −3.16 to −3.62 dB. Specifically, 5 or more defective central 24-2 test locations were associated with an FVZ. Subjects exhibiting a less severe defect on the 10-2 were more likely to have an FVZ, indicating its potential for future VF monitoring. The authors propose several clinical markers, focussing on the 24-2, which can guide clinicians on when the 10-2 may have utility in glaucoma assessment. The authors provide a pilot reference spreadsheet for clinicians to visualise the likelihood of 10-2 utility in the context of an FVZ. [ABSTRACT FROM AUTHOR]

Artificial Intelligence is a rapidly expanding field within computer science that encompasses the emulation of human intelligence by machines. Machine learning and deep learning – two primary data-driven pattern analysis approaches under the umbrella of artificial intelligence – has created considerable interest in the last few decades. The evolution of technology has resulted in a substantial amount of artificial intelligence research on ophthalmic and neurodegenerative disease diagnosis using retinal images. Various artificial intelligence-based techniques have been used for diagnostic purposes, including traditional machine learning, deep learning, and their combinations. Presented here is a review of the literature covering the last 10 years on this topic, discussing the use of artificial intelligence in analysing data from different modalities and their combinations for the diagnosis of glaucoma and neurodegenerative diseases. The performance of published artificial intelligence methods varies due to several factors, yet the results suggest that such methods can potentially facilitate clinical diagnosis. Generally, the accuracy of artificial intelligence-assisted diagnosis ranges from 67–98%, and the area under the sensitivity-specificity curve (AUC) ranges from 0.71–0.98, which outperforms typical human performance of 71.5% accuracy and 0.86 area under the curve. This indicates that artificial intelligence-based tools can provide clinicians with useful information that would assist in providing improved diagnosis. The review suggests that there is room for improvement of existing artificial intelligence-based models using retinal imaging modalities before they are incorporated into clinical practice. [ABSTRACT FROM AUTHOR]

Purpose: To describe variations in ganglion cell–inner plexiform layer (GCIPL) thickness in a healthy cohort from widefield optical coherence tomography (OCT) scans. Methods: Widefield OCT scans spanning 55° × 45° were acquired from 470 healthy eyes. The GCIPL was automatically segmented using deep learning methods. Thickness measurements were extracted after correction for warpage and retinal tilt. Multiple linear regression analysis was applied to discern trends between global GCIPL thickness and age, axial length and sex. To further characterise age‐related change, hierarchical and two‐step cluster algorithms were applied to identify locations sharing similar ageing properties, and rates of change were quantified using regression analyses with data pooled by cluster analysis outcomes. Results: Declines in widefield GCIPL thickness with age, increasing axial length and female sex were observed (parameter estimates −0.053, −0.436 and −0.464, p‐values <0.001, <0.001 and 0.02, respectively). Cluster analyses revealed concentric, slightly nasally displaced, horseshoe patterns of age‐related change in the GCIPL, with up to four statistically distinct clusters outside the macula. Linear regression analyses revealed significant ageing decline in GCIPL thickness across all clusters, with faster rates of change observed at central locations when expressed as absolute (slope = −0.19 centrally vs. −0.04 to −0.12 peripherally) and percentage rates of change (slope = −0.001 centrally vs. −0.0005 peripherally). Conclusions: Normative variations in GCIPL thickness from widefield OCT with age, axial length and sex were noted, highlighting factors worth considering in further developments. Widefield OCT has promising potential to facilitate quantitative detection of abnormal GCIPL outside standard fields of view. [ABSTRACT FROM AUTHOR]

Purpose: To compare clinical visual field outputs in glaucoma and healthy patients returned by the Humphrey Field Analyzer (HFA) and virtual reality (Virtual Field, VF) perimetry. Methods: One eye of 54 glaucoma patients and 41 healthy subjects was prospectively tested (three times each in random order) using the HFA and VF perimeters (24‐2 test grids). We extracted and compared global indices (mean deviation [MD] and pattern standard deviation [PSD]), pointwise sensitivity (and calculated 'equivalent' sensitivity after accounting for differences in background luminance) and pointwise defects. Bland–Altman (mean difference [Mdiff] and 95% limits of agreement [LoA]) and intraclass correlation analyses were performed. Results: The VF test was shorter (by 76 s) and had lower fixation losses (by 0.08) and false‐positive rate (by 0.01) compared to the HFA (all p < 0.0001). Intraclass correlations were 0.86, 0.82 and 0.47 for MD, PSD and pointwise sensitivity between devices, respectively. Test–retest variability was higher for VF (Mdiff 0.3 dB, LoA −7.6 to 8.2 dB) compared to the HFA (Mdiff −0.3 dB, LoA −6.4 to 5.9 dB), indicating greater test–retest variability. When using each device's underlying normative database, the HFA detected, on average, 7 more defects (at the p < 0.05 level) out of the 52 test locations compared to this iteration of VF in the glaucoma cohort. Conclusions: Virtual Field returns global results that are correlated with the HFA, but pointwise sensitivities were more variable. Differences in test–retest variability and defect detection by its current normative database raise questions about the widespread adoption of VF in lieu of the HFA. [ABSTRACT FROM AUTHOR]

This article discusses the challenges and opportunities in the diagnosis and management of glaucoma, a sight-threatening eye condition. It highlights the high rates of undiagnosed glaucoma in Australia, even among those who have seen an optometrist or ophthalmologist. The article emphasizes the importance of early detection and suitable management to minimize the disability-adjusted life years (DALYs) associated with vision loss. It also explores the role of new diagnostic techniques, collaborative care models, and emerging technologies like artificial intelligence (AI) in improving glaucoma diagnosis and management. The article concludes by emphasizing the need for intra- and inter-professional collaboration and a deeper understanding of disease mechanisms to improve patient outcomes and develop novel therapies. [Extracted from the article]

Purpose: To determine whether there are quantifiable structural or functional differences that can distinguish between high‐tension glaucoma (HTG; intraocular pressure [IOP] > 21 mm Hg) and low‐tension glaucoma (LTG; IOP ≤ 21 mm Hg) at diagnosis. Method: This was a retrospective, cross‐sectional study. Clinical results of one eye from 90 newly diagnosed HTG and 319 newly diagnosed LTG patients (117 with very‐low‐tension glaucoma [vLTG; ≤15 mm Hg] and 202 with middling LTG [mLTG; >15 mm Hg, ≤21 mm Hg]) were extracted, which included relevant demographic covariates of glaucoma, quantitative optical coherence tomography (including the optic nerve head, retinal nerve fibre layer and ganglion cell‐inner plexiform layer) measurements and standard automated perimetry global metrics. We used binary logistic regression analysis to identify statistically significant clinical parameters distinguishing between phenotypic groups for inclusion in principal component (PC) (factor) analysis (PCA). The separability between each centroid for each cohort was calculated using the Euclidean distance (d(x,y)). Results: The binary logistic regression comparing HTG and all LTG identified eight statistically significant clinical parameters. Subsequent PCA results included three PCs with an eigenvalue >1. PCs 1 and 2 accounted for 21.2% and 20.2% of the model, respectively, with a d(x,y) = 0.468, indicating low separability between HTG and LTG. The analysis comparing vLTG, mLTG and HTG identified 15 significant clinical parameters, which were subsequently grouped into five PCs. PCs 1 and 2 accounted for 24.1% and 17.8%, respectively. The largest separation was observed between vLTG and HTG (d(x,y) = 0.581), followed by vLTG and mLTG (d(x,y) = 0.435) and lastly mLTG and HTG (d(x,y) = 0.210). Conclusion: Conventional quantitative structural or functional parameters could not distinguish between pressure‐defined glaucoma phenotypes at the point of diagnosis and are therefore not contributory to separating cohorts. The overlap in findings highlights the heterogeneity of the primary open‐angle glaucoma clinical presentations among pressure‐defined groups at the cohort level. [ABSTRACT FROM AUTHOR]