Traditionally, abnormalities of the retinal vasculature and perfusion in retinal vascular disorders, such as diabetic retinopathy and retinal vascular occlusions, have been visualized with dye-based fluorescein angiography (FA). Optical coherence tomography angiography (OCTA) is a newer, alternative modality for imaging the retinal vasculature, which has some advantages over FA, such as its dye-free, non-invasive nature, and depth resolution. The depth resolution of OCTA allows for characterization of the retinal microvasculature in distinct anatomic layers, and commercial OCTA platforms also provide automated quantitative vascular and perfusion metrics. Quantitative and qualitative OCTA analysis in various retinal vascular disorders has facilitated the detection of pre-clinical vascular changes, greater understanding of known clinical signs, and the development of imaging biomarkers to prognosticate and guide treatment. With further technological improvements, such as a greater field of view and better image quality processing algorithms, it is likely that OCTA will play an integral role in the study and management of retinal vascular disorders. Artificial intelligence methods—in particular, deep learning—show promise in refining the insights to be gained from the use of OCTA in retinal vascular disorders. This review aims to summarize the current literature on this imaging modality in relation to common retinal vascular disorders.

Color fundus photography (CFP) and Optical coherence tomography (OCT) images are two of the most widely used modalities in the clinical diagnosis and management of retinal diseases. Despite the widespread use of multimodal imaging in clinical practice, few methods for automated diagnosis of eye diseases utilize correlated and complementary information from multiple modalities effectively. This paper explores how to leverage the information from CFP and OCT images to improve the automated diagnosis of retinal diseases. We propose a novel multimodal learning method, named geometric correspondence-based multimodal learning network (GeCoM-Net), to achieve the fusion of CFP and OCT images. Specifically, inspired by clinical observations, we consider the geometric correspondence between the OCT slice and the CFP region to learn the correlated features of the two modalities for robust fusion. Furthermore, we design a new feature selection strategy to extract discriminative OCT representations by automatically selecting the important feature maps from OCT slices. Unlike the existing multimodal learning methods, GeCoM-Net is the first method that formulates the geometric relationships between the OCT slice and the corresponding region of the CFP image explicitly for CFP and OCT fusion. Experiments have been conducted on a large-scale private dataset and a publicly available dataset to evaluate the effectiveness of GeCoM-Net for diagnosing diabetic macular edema (DME), impaired visual acuity (VA) and glaucoma. The empirical results show that our method outperforms the current state-of-the-art multimodal learning methods by improving the AUROC score 0.4%, 1.9% and 2.9% for DME, VA and glaucoma detection, respectively.

Background/aims: To compare the performance of generative versus retrieval-based chatbots in answering patient inquiries regarding age-related macular degeneration (AMD) and diabetic retinopathy (DR).Methods: We evaluated four chatbots: generative models (ChatGPT-4, ChatGPT-3.5 and Google Bard) and a retrieval-based model (OcularBERT) in a cross-sectional study. Their response accuracy to 45 questions (15 AMD, 15 DR and 15 others) was evaluated and compared. Three masked retinal specialists graded the responses using a three-point Likert scale: either 2 (good, error-free), 1 (borderline) or 0 (poor with significant inaccuracies). The scores were aggregated, ranging from 0 to 6. Based on majority consensus among the graders, the responses were also classified as ‘Good’, ‘Borderline’ or ‘Poor’ quality.Results: Overall, ChatGPT-4 and ChatGPT-3.5 outperformed the other chatbots, both achieving median scores (IQR) of 6 (1), compared with 4.5 (2) in Google Bard, and 2 (1) in OcularBERT (all p ≤8.4×10⁻³). Based on the consensus approach, 83.3% of ChatGPT-4’s responses and 86.7% of ChatGPT-3.5’s were rated as ‘Good’, surpassing Google Bard (50%) and OcularBERT (10%) (all p ≤1.4×10⁻²). ChatGPT-4 and ChatGPT-3.5 had no ‘Poor’ rated responses. Google Bard produced 6.7% Poor responses, and OcularBERT produced 20%. Across question types, ChatGPT-4 outperformed Google Bard only for AMD, and ChatGPT-3.5 outperformed Google Bard for DR and others.Conclusion: ChatGPT-4 and ChatGPT-3.5 demonstrated superior performance, followed by Google Bard and OcularBERT. Generative chatbots are potentially capable of answering domain-specific questions outside their original training. Further validation studies are still required prior to real-world implementation.

Background: To evaluate the ability of handheld chromatic pupillometry to reveal and localise retinal neural dysfunction in diabetic patients with and without diabetic retinopathy (DR).Methods: This cross‐sectional study included 82 diabetics (DM) and 93 controls (60.4 ± 8.4 years, 44.1% males). DM patients included those without (n = 25, 64.7 ± 6.3 years, 44.0% males) and with DR (n = 57, 60.3 ± 8.5 years, 64.9% males). Changes in horizontal pupil radius in response to blue (469 nm) and red (640 nm) light stimuli were assessed monocularly, in clinics, using a custom‐built handheld pupillometer. Pupillometric parameters (phasic constriction amplitudes [predominantly from the outer retina], maximal constriction amplitudes [from the inner and outer retina] and post‐illumination pupillary responses [PIPRs; predominantly from the inner retina]) were extracted from baseline‐adjusted pupillary light response traces and compared between controls, DM without DR, and DR. Net PIPR was defined as the difference between blue and red PIPRs.Results: Phasic constriction amplitudes to blue and red lights were decreased in DR compared to controls (p < 0.001; p < 0.001). Maximal constriction amplitudes to blue and red lights were decreased in DR compared to DM without DR (p < 0.001; p = 0.02), and in DM without DR compared to controls (p < 0.001; p = 0.005). Net PIPR was decreased in both DR and DM without DR compared to controls (p = 0.02; p = 0.03), suggesting a wavelength‐dependent (and hence retinal) pupillometric dysfunction in diabetic patients with or without DR.Conclusions: Handheld chromatic pupillometry can reveal retinal neural dysfunction in diabetes, even without DR. Patients with DM but no DR displayed primarily inner retinal dysfunction, while patients with DR showed both inner and outer retinal dysfunction.

Femtosecond laser-assisted conjunctival autografts (CAG) preparation was recently proposed. This study reports the outcomes of the first clinical trial on the use of laser to prepare CAG in pterygium surgery, and to compare the outcomes with those of manual technique. Forty eyes undergoing primary pterygium excision with laser-assisted CAG transplantation were prospectively included (L group). Two historical matched cohorts whose CAGs were prepared manually were compared (n = 78 eyes by the same experienced surgeon, M group; n = 78 eyes by trainees; TM group). We found the laser-created CAGs had only 11 μm deviation from the targeted thickness. The best-corrected visual acuity improved, and the astigmatism significantly decreased after surgery, with comparable efficacy across 3 groups. The 1-year recurrence rate was 2.5%, 3.8% and 7.7% in the L, M and TM groups, respectively (P = 0.12). There was no significant difference between the L and M groups in the complication rate (5.0% and 1.3%, respectively), surgical time (19.4 ± 5.1 and 19.1 ± 6.2 minutes, respectively), and postoperative discomfort scores (0.1 ± 0.3 and 0.2 ± 0.3, respectively), but these outcomes were significantly less favorable in the TM group. The results of this first comparative clinical trial suggest that femtosecond laser-assisted CAG preparation can be considered as an alternative technique for CAGs preparation.