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To explore the use of a thermoreversible copolymer gel coating to prevent donor tissue scrolling in Descemet's membrane endothelial keratoplasty (DMEK).PLGA-PEG-PLGA triblock copolymer was synthesised via ring opening polymerisation. Two formulations were fabricated and gelation properties characterised using rheological analyses. Endothelial cytotoxicity of the copolymer was assessed using a Trypan Blue exclusion assay. Thickness of the copolymer gel coating on the endothelial surface was analysed using anterior segment optical coherence tomography (OCT) (RTVue-100, Optovue Inc.). Gold nanoparticles were added to the copolymer to aid visualisation using OCT. Prevention of Descemet membrane donor scrolling was represented via a novel, in vitro, immersion of copolymer coated donor graft material.Two different formulations of PLGA-PEG-PLGA copolymer were successfully fabricated and the desired peak gelling temperature of 24°C was achieved by polymer blending. Application of 20%, 30% and 40% (wt/vol) polymer concentrations resulted in a statistically significant increase in polymer thickness on the endothelium (p 0.001). There was no detectable endothelial cytotoxicity. The polymer was easy to apply to the endothelium and prevented scrolling of the DMEK graft.This PLGA-PEG-PLGA thermoreversible copolymer gel could be exploited as a therapeutic aid for preventing DMEK graft scrolling.

Reinforcement learning is a subtype of machine learning in which a virtual agent, functioning within a set of predefined rules, aims to maximise a specified outcome or reward. This agent can consider multiple variables and many parallel actions at once to optimise its reward, thereby solving complex, sequential problems. Clinical decision making requires physicians to optimise patient outcomes within a set practice framework and, thus, presents considerable opportunity for the implementation of reinforcement learning-driven solutions. We provide an overview of reinforcement learning, and focus on potential applications within ophthalmology. We also explore the challenges associated with development and implementation of reinforcement learning solutions and discuss possible approaches to address them.

Importance Although the diagnosis of vitreoretinal lymphoma (VRL) can be challenging, early detection is critical for visual prognosis. Objective To analyze the spectrum of optical coherence tomography (OCT) findings in patients with biopsy-proven VRL and correlate these features with clinical parameters. Design Retrospective cross-sectional study Setting Multicenter chart review from 13 retina, uveitis and ocular oncology clinics worldwide over an 11-year period (2008-2019). Participants Patients with a diagnosis of biopsy-proven VRL imaged with OCT at presentation Exposure The ocular information, systemic information and multimodal retinal imaging findings were collected and studied. Main Outcome Measure Characteristics of VRL on OCT Results A total of 182 eyes of 115 patients (63 women, mean age 65 years) were included in this study. The disease was bilateral in 81 (70%) patients, and mean baseline visual acuity (VA) was 0.2±0.89 logMAR (Snellen equivalent of 20/32). At baseline, 38 patients (33%) presented with isolated ocular involvement, 54 (45%) with associated central nervous system (CNS) involvement and 11 (10%) with other systemic lymphomatous involvement and an additional 12 (10%) patients presented with both CNS and other systemic involvement. On OCT, tumor infiltration was identified in various retinal layers including lesions in the sub-retinal pigment epithelium (RPE) compartment (91% of eyes), the subretinal compartment (43% of eyes) and the intraretinal compartment (7% of eyes). Conclusions and Relevance OCT analysis of eyes with vitreoretinal lymphoma identified 3 main regions of retinal infiltration. Sub-RPE location, with or without subretinal infiltration, was the most common pattern of involvement whereas isolated intraretinal infiltration was the least common.

Uveitis consists of a group of syndromes characterised by intraocular inflammation, accounting for up to 15% of visual loss in the western world and 10% worldwide. Assessment of intraocular inflammation has been limited to clinician-dependent, subjective grading. Developments in imaging technology, such as optical coherence tomography (OCT), have enabled the development of objective, quantitative measures of inflammatory activity. Important quantitative metrics including central macular thickness and vitreous signal intensity allow longitudinal monitoring of disease activity and can be used in conjunction with other imaging modalities enabling holistic assessment of ocular inflammation. Ongoing work into the validation of instrument-based measures alongside development of core outcome sets is crucial for standardisation of clinical trial endpoints and developing guidance for quantitative multi-modal imaging approaches. This review outlines methods of grading inflammation in the vitreous and retina, with a focus on the use of OCT as an objective measure of disease activity.

Computer vision, understood as the area of science that trains computers to interpret digital images through both artificial intelligence (AI) and classical algorithms, has significantly advanced the analysis and interpretation of optical coherence tomography (OCT) in retina research. The aim of this review is to summarise the recent advances of computer vision in imaging processing in uveitis, with a particular focus in optical coherence tomography images.Literature review.The development of computer vision to assist uveitis diagnosis and prognosis is still undergoing, but important efforts have been made in the field.The automatising of image processing in uveitis could be fundamental to establish objective and standardised outcomes for future clinical trials. In addition, it could help to better understand the disease and its progression.

Purpose To evaluate the predictive usefulness of quantitative imaging biomarkers, acquired automatically from OCT scans, of cross-sectional and future visual outcomes of patients with neovascular age-related macular degeneration (AMD) starting anti–vascular endothelial growth factor (VEGF) therapy. Design Retrospective cohort study. Participants Treatment-naive, first-treated eyes of patients with neovascular AMD between 2007 and 2017 at Moorfields Eye Hospital (a large, United Kingdom single center) undergoing anti-VEGF therapy. Methods Automatic segmentation was carried out by applying a deep learning segmentation algorithm to 137 379 OCT scans from 6467 eyes of 3261 patients with neovascular AMD. After applying selection criteria, 926 eyes of 926 patients were analyzed. Main Outcome Measures Correlation coefficients (R2 values) and mean absolute error (MAE) between quantitative OCT (qOCT) parameters and cross-sectional visual function, as well as the predictive value of these parameters for short-term visual change, that is, incremental visual acuity (VA) resulting from an individual injection, as well as VA at distant time points (up to 12 months after baseline). Results Visual acuity at distant time points could be predicted: R2 = 0.80 (MAE, 5.0 Early Treatment Diabetic Retinopathy Study [ETDRS] letters) and R2 = 0.7 (MAE, 7.2 ETDRS letters) after injection at 3 and at 12 months after baseline (P Conclusions Automatic segmentation enables rapid acquisition of quantitative and reproducible OCT biomarkers with potential to inform treatment decisions in the care of neovascular AMD. This furthers development of point-of-care decision-aid systems for personalized medicine.

ObjectivePredicting the impact of neovascular age-related macular degeneration (nAMD) service disruption on visual outcomes following national lockdown in the UK to contain SARS-CoV-2.Methods and analysisThis retrospective cohort study includes deidentified data from 2229 UK patients from the INSIGHT Health Data Research digital hub. We forecasted the number of treatment-naïve nAMD patients requiring anti-vascular endothelial growth factor (anti-VEGF) initiation during UK lockdown (16 March 2020 through 31 July 2020) at Moorfields Eye Hospital (MEH) and University Hospitals Birmingham (UHB). Best-measured visual acuity (VA) changes without anti-VEGF therapy were predicted using post hoc analysis of Minimally Classic/Occult Trial of the Anti-VEGF Antibody Ranibizumab in the Treatment of Neovascular AMD trial sham-control arm data (n=238).ResultsAt our centres, 376 patients were predicted to require anti-VEGF initiation during lockdown (MEH: 325; UHB: 51). Without treatment, mean VA was projected to decline after 12 months. The proportion of eyes in the MEH cohort predicted to maintain the key positive visual outcome of ≥70 ETDRS letters (Snellen equivalent 6/12) fell from 25.5% at baseline to 5.8% at 12 months (UHB: 9.8%–7.8%). Similarly, eyes with VA ConclusionsHere, we demonstrate how combining data from a recently founded national digital health data repository with historical industry-funded clinical trial data can enhance predictive modelling in nAMD. The demonstrated detrimental effects of prolonged treatment delay should incentivise healthcare providers to support nAMD patients accessing care in safe environments.Trial registration numberNCT00056836.

Biallelic pathogenic variants in ABCA4 are the commonest cause of monogenic retinal disease. The full-field electroretinogram (ERG) quantifies severity of retinal dysfunction. We explored application of machine learning in ERG interpretation and in genotype-phenotype correlations.International standard ERGs in 597 cases of ABCA4 retinopathy were classified into three functional phenotypes by human experts: macular dysfunction alone (group 1), or with additional generalized cone dysfunction (group 2), or both cone and rod dysfunction (group 3). Algorithms were developed for automatic selection and measurement of ERG components and for classification of ERG phenotype. Elastic-net regression was used to quantify severity of specific ABCA4 variants based on effect on retinal function.Of the cohort, 57.6%, 7.4%, and 35.0% fell into groups 1, 2, and 3 respectively. Compared with human experts, automated classification showed overall accuracy of 91.8% (SE, 0.169), and 96.7%, 39.3%, and 93.8% for groups 1, 2, and 3. When groups 2 and 3 were combined, the average holdout group accuracy was 93.6% (SE, 0.142). A regression model yielded phenotypic severity scores for the 47 commonest ABCA4 variants.This study quantifies prevalence of phenotypic groups based on retinal function in a uniquely large single-center cohort of patients with electrophysiologically characterized ABCA4 retinopathy and shows applicability of machine learning. Novel regression-based analyses of ABCA4 variant severity could identify individuals predisposed to severe disease.Machine learning can yield meaningful classifications of ERG data, and data-driven scoring of genetic variants can identify patients likely to benefit most from future therapies.

Purpose: Serum lipids are modifiable, routinely collected blood test features associated with cardiovascular health. We examined the association of commonly collected serum lipid measures (total cholesterol [TC], high-density lipoprotein cholesterol [HDL-C], low-density lipoprotein cholesterol [LDL-C], and triglycerides) with intraocular pressure (IOP). Design: Cross-sectional study in the UK Biobank and European Prospective Investigation into Cancer and Nutrition (EPIC)-Norfolk cohorts. Participants: We included 94 323 participants from the UK Biobank (mean age, 57 years) and 6230 participants from the EPIC-Norfolk (mean age, 68 years) cohorts with data on TC, HDL-C, LDL-C, and triglycerides collected between 2006 and 2009. Methods: Multivariate linear regression adjusting for demographic, lifestyle, anthropometric, medical, and ophthalmic covariables was used to examine the associations of serum lipids with corneal-compensated IOP (IOPcc). Main Outcome Measures: Corneal-compensated IOP. Results: Higher levels of TC, HDL-C, and LDL-C were associated independently with higher IOPcc in both cohorts after adjustment for key demographic, medical, and lifestyle factors. For each 1-standard deviation increase in TC, HDL-C, and LDL-C, IOPcc was higher by 0.09 mmHg (95% confidence interval [CI], 0.06–0.11 mmHg; P < 0.001), 0.11 mmHg (95% CI, 0.08–0.13 mmHg; P < 0.001), and 0.07 mmHg (95% CI, 0.05–0.09 mmHg; P < 0.001), respectively, in the UK Biobank cohort. In the EPIC-Norfolk cohort, each 1-standard deviation increase in TC, HDL-C, and LDL-C was associated with a higher IOPcc by 0.19 mmHg (95% CI, 0.07–0.31 mmHg; P = 0.001), 0.14 mmHg (95% CI, 0.03–0.25 mmHg; P = 0.016), and 0.17 mmHg (95% CI, 0.06–0.29 mmHg; P = 0.003). An inverse association between triglyceride levels and IOP in the UK Biobank (–0.05 mmHg; 95% CI, –0.08 to –0.03; P < 0.001) was not replicated in the EPIC-Norfolk cohort (P = 0.30). Conclusions: Our findings suggest that serum TC, HDL-C, and LDL-C are associated positively with IOP in 2 United Kingdom cohorts and that triglyceride levels may be associated negatively. Future research is required to assess whether these associations are causal in nature.

Introduction To assess inter-device agreement in optical coherence tomography-derived retinal thickness measurements in patients with known macular conditions between spectral-domain and swept-source optical coherence tomography (OCT). Methods Two hundred seventy-two subjects were included in the study. They consisted of 91 male (33.5%) and 181 female (66.5%) subjects, and 132 left (48.5%) and 140 right (51.5%) eyes. Each subject underwent spectral-domain OCT (SD-OCT, Spectralis, Heidelberg Engineering; RTVue XR Avanti XR HD, Optovue) and swept-source OCT (SS-OCT; DRI-OCT-1, Atlantis, Topcon) in a single imaging session performed by the same clinical trial-certified technician. The comparison of retinal thickness reproducibility between devices was performed using Bland-Altman analyses and across the entire data set using the intraclass correlation coefficient (ICC). Results The ICC of the retinal thickness measurements (95% confidence interval) made using all three OCT instruments was 0.81 (0.77-0.84). The mean difference in mean retinal thickness between Spectralis SD-OCT and Topcon SS-OCT was 59.1 μm (95% limit of agreement [LoA] -21.7 to 139.8 μm). The mean difference in mean retinal thickness between Optovue SD-OCT and Topcon SS-OCT was 21.8 μm (95% LoA -34.7 to 78.3 μm). Conclusions Retinal layer thickness measurements vary between SS-OCT and SD-OCT devices. We describe inter-device agreement in retinal thickness between SS-OCT and SD-OCT in patients with macular conditions. Clinicians should be aware of the differences in retinal thickness values when imaging patients using different OCT devices and should consider using the same OCT device model in order to monitor clinical change. Trial registration ClinicalTrials.gov Identifier (NCT02828215).

Purpose of review The purpose of this review is to describe the current status of automated deep learning in healthcare and to explore and detail the development of these models using commercially available platforms. We highlight key studies demonstrating the effectiveness of this technique and discuss current challenges and future directions of automated deep learning. Recent findings There are several commercially available automated deep learning platforms. Although specific features differ between platforms, they utilise the common approach of supervised learning. Ophthalmology is an exemplar speciality in the area, with a number of recent proof-of-concept studies exploring classification of retinal fundus photographs, optical coherence tomography images and indocyanine green angiography images. Automated deep learning has also demonstrated impressive results in other specialities such as dermatology, radiology and histopathology. Summary Automated deep learning allows users without coding expertise to develop deep learning algorithms. It is rapidly establishing itself as a valuable tool for those with limited technical experience. Despite residual challenges, it offers considerable potential in the future of patient management, clinical research and medical education. Video abstract http://links.lww.com/COOP/A44.

PURPOSE OF REVIEW: In this article, we introduce the concept of model interpretability, review its applications in deep learning models for clinical ophthalmology, and discuss its role in the integration of artificial intelligence in healthcare. RECENT FINDINGS: The advent of deep learning in medicine has introduced models with remarkable accuracy. However, the inherent complexity of these models undermines its users’ ability to understand, debug and ultimately trust them in clinical practice. Novel methods are being increasingly explored to improve models’ “interpretability” and draw clearer associations between their outputs and features in the input dataset. In the field of ophthalmology, interpretability methods have enabled users to make informed adjustments, identify clinically relevant imaging patterns, and predict outcomes in deep learning models. SUMMARY: Interpretability methods support the transparency necessary to implement, operate and modify complex deep learning models. These benefits are becoming increasingly demonstrated in models for clinical ophthalmology. As quality standards for deep learning models used in healthcare continue to evolve, interpretability methods may prove influential in their path to regulatory approval and acceptance in clinical practice.

Purpose To apply a deep learning algorithm for automated, objective, and comprehensive quantification of OCT scans to a large real-world dataset of eyes with neovascular age-related macular degeneration (AMD) and make the raw segmentation output data openly available for further research. Design Retrospective analysis of OCT images from the Moorfields Eye Hospital AMD Database. Participants A total of 2473 first-treated eyes and 493 second-treated eyes that commenced therapy for neovascular AMD between June 2012 and June 2017. Methods A deep learning algorithm was used to segment all baseline OCT scans. Volumes were calculated for segmented features such as neurosensory retina (NSR), drusen, intraretinal fluid (IRF), subretinal fluid (SRF), subretinal hyperreflective material (SHRM), retinal pigment epithelium (RPE), hyperreflective foci (HRF), fibrovascular pigment epithelium detachment (fvPED), and serous PED (sPED). Analyses included comparisons between first- and second-treated eyes by visual acuity (VA) and race/ethnicity and correlations between volumes. Main Outcome Measures Volumes of segmented features (mm3) and central subfield thickness (CST) (μm). Results In first-treated eyes, the majority had both IRF and SRF (54.7%). First-treated eyes had greater volumes for all segmented tissues, with the exception of drusen, which was greater in second-treated eyes. In first-treated eyes, older age was associated with lower volumes for RPE, SRF, NSR, and sPED; in second-treated eyes, older age was associated with lower volumes of NSR, RPE, sPED, fvPED, and SRF. Eyes from Black individuals had higher SRF, RPE, and serous PED volumes compared with other ethnic groups. Greater volumes of the majority of features were associated with worse VA. Conclusions We report the results of large-scale automated quantification of a novel range of baseline features in neovascular AMD. Major differences between first- and second-treated eyes, with increasing age, and between ethnicities are highlighted. In the coming years, enhanced, automated OCT segmentation may assist personalization of real-world care and the detection of novel structure–function correlations. These data will be made publicly available for replication and future investigation by the AMD research community.

Purpose This study aimed to describe the choroidal features of ocular amyloidosis using multimodal imaging, to correlate these findings with systemic involvement, and to propose a choroidal grading system. Methods Eleven patients with systemic amyloidosis were reviewed retrospectively. Each case was assigned a grade according to the severity of choroidal findings as determined by both enhanced depth imaging optical coherence tomography and indocyanine green angiography. The severity of systemic amyloidosis was then correlated with the choroidal involvement. Results On indocyanine green angiography, all patients exhibited hyperfluorescent spots in the late stage and were classified according to preexisting criteria. On enhanced depth imaging optical coherence tomography, hyperreflective foci were seen in the choriocapillaris and Sattler's layer in Grade 1, partial loss of Sattler's layer was additionally seen in Grade 2, and a dense hyperreflective Haller's layer was seen in Grade 3. Choroidal grading scores were significantly correlated with the systemic severity score (P = 0.0014, Pearson's correlation coefficient; ρ = 0.83). Conclusion With ocular amyloidosis, evaluation of choroidal characteristics using multimodal imaging may serve as a biomarker for systemic involvement.

OBJECTIVE: To describe the role of local anaesthetic blocks as a potential cause of paracentral acute middle maculopathy (PAMM) after uneventful ocular surgery. METHODS: Retrospective, observational, international, multicentre case series. Nine cases of PAMM with associated visual loss following uneventful ocular surgery with local anaesthetic blocks were observed in a 9-year period (2011–2020). Demographic, ocular and systemic data, anaesthetic data and surgical details were collected. Visual acuity (VA), fundus photography, fluorescein angiography, optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) images were reviewed. RESULTS: All nine cases were associated with decreased VA at 24 h postoperative check (ranging from hand movement to 20/200). A hyperreflective band within the middle retinal layers was observed in the structural OCT in the acute phase, evolving to thinning and atrophy of the inner retinal layers in sequential follow-up scans performed. Fluorescein angiography showed delayed perfusion in early arterial phase with normal perfusion in late venous phases. OCTA showed decreased perfusion in the deep capillary plexus. Visual recovery was variable between cases during follow-up (ranging from count fingers to 20/20). CONCLUSIONS: A combination of a vasoconstrictive effect of the anaesthetic agent, an intraocular pressure spike and a mechanical effect of the volume of anaesthetic injected may result in decreased retinal artery perfusion and be evidenced as PAMM in OCT scans. PAMM may present as a potential complication of local anaesthetic blocks in cases of unexpected visual loss after uneventful ocular surgery.

PurposeTo externally validate a deep learning pipeline (AutoMorph) for automated analysis of retinal vascular morphology on fundus photographs. AutoMorph has been made publicly available (https://github.com/rmaphoh/AutoMorph), facilitating widespread research in ophthalmic and systemic diseases.MethodsAutoMorph consists of four functional modules: image pre-processing, image quality grading, anatomical segmentation (including binary vessel, artery/vein, and optic disc/cup segmentation), and vascular morphology feature measurement. Image quality grading and anatomical segmentation use the most recent deep learning techniques. We employ a model ensemble strategy to achieve robust results and analyse the prediction confidence to rectify false gradable cases in image quality grading. We externally validate each module’s performance on several independent publicly available datasets.ResultsThe EfficientNet-b4 architecture used in the image grading module achieves comparable performance to the state-of-the-art for EyePACS-Q, with an F1-score of 0.86. The confidence analysis reduces the number of images incorrectly assessed as gradable by 76%. Binary vessel segmentation achieves an F1-score of 0.73 on AV-WIDE and 0.78 on DR-HAGIS. Artery/vein scores 0.66 on IOSTAR-AV, and disc segmentation achieves 0.94 in IDRID. Vascular morphology features measured from AutoMorph segmentation map and expert annotation show good to excellent agreement.ConclusionsAutoMorph modules perform well even when external validation data shows domain differences from training data, e.g., with different imaging devices. This fully automated pipeline can thus allow detailed, efficient and comprehensive analysis of retinal vascular morphology on colour fundus photographs.Translational RelevanceBy making AutoMorph publicly available and open source, we hope to facilitate ophthalmic and systemic disease research, particularly in the emerging field of ‘oculomics’.

Optical coherence tomography (OCT) is a paragon of success in the translation of biophotonics science to clinical practice. OCT systems have become ubiquitous in eye clinics but access beyond this is limited by their cost, size and the skill required to operate the devices. Remarkable progress has been made in the development of OCT technology to improve the speed of acquisition, the quality of images and into functional extensions of OCT such as OCT angiography. However, more needs to be done to radically improve the access to OCT by addressing its limitations and enable penetration outside of typical clinical settings and into underserved populations. Beyond high-income countries, there are 6.5 billion people with similar eye-care needs, which cannot be met by the current generation of bulky, expensive and complex OCT systems. In addition, advancing the portability of this technology to address opportunities in point-of-care diagnostics, telemedicine and remote monitoring may aid development of personalised medicine. In this review, we discuss the major milestones in OCT hardware development to reach those beyond the eye clinic.

PURPOSE This systematic review aims to identify instrument-based tests for quantifying vitreous inflammation in uveitis, report the test reliability and the level of correlation with clinician grading. METHODS Studies describing instrument-based tests for detecting vitreous inflammation were identified by searching bibliographic databases and trials registers. Test reliability measures and level of correlation with clinician vitreous haze grading are extracted. RESULTS Twelve studies describing ultrasound, optical coherence tomography (OCT), and retinal photography for detecting vitreous inflammation were included: Ultrasound was used for detection of disease features, whereas OCT and retinal photography provided quantifiable measurements. Correlation with clinician grading for OCT was 0.53-0.60 (three studies) and for retinal photography was 0.51 (1 study). Both instruments showed high inter- and intra-observer reliability (>0.70 intraclass correlation and Cohen's kappa), where reported in four studies. CONCLUSION Retinal photography and OCT are able to detect and measure vitreous inflammation. Both techniques are reliable, automatable, and warrant further evaluation.

BackgroundTo describe 10-year trends in visual outcomes, anatomical outcomes and treatment burden of patients receiving antivascular endothelial growth factor (anti-VEGF) therapy for neovascular age-related macular degeneration (nAMD).MethodsRetrospective cohort study of treatment-naïve, first-affected eyes with nAMD started on ranibizumab before January 1, 2009. The primary outcome was time to best-corrected visual acuity (BCVA) falling ≤35 ETDRS letters after initiating anti-VEGF therapy. Secondary outcomes included time to BCVA reaching ≥70 letters, proportion of eyes with BCVA ≥70 and ≤35 letters in 10 years, mean trend of BCVA and central retinal thickness over 10 years, and mean number of injections.ResultsFor our cohort of 103 patients, Kaplan-Meier analyses demonstrated median time to BCVA reaching ≤35 and ≥70 letters were 37.8 (95% CI 22.2 to 65.1) and 8.3 (95% CI 4.8 to 20.9) months after commencing anti-VEGF therapy, respectively. At the final follow-up, BCVA was ≤35 letters and ≥70 letters in 41.1% and 21%, respectively, in first-affected eyes, while this was the case for 5.4% and 48.2%, respectively, in a patient’s better-seeing eye. Mean injection number was 37.0±24.2 per eye and 53.6±30.1 at patient level (63.1% of patients required injections in both eyes).ConclusionsThe chronicity of nAMD disease and its management highlights the importance of long-term visual prognosis. Our analyses suggest that one in five patients will retain good vision (BCVA ≥70 ETDRS letters) in the first-affected eye at 10 years after starting anti-VEGF treatment; yet, one in two patients will have good vision in their better-seeing eye. Moreover, our data suggest that early treatment of nAMD is associated with better visual outcomes.

Ocular tuberculosis has protean clinical manifestations. Because of its varied clinical presentation, multimodal imaging is very important to characterize the disease activity, presence of inflammation, determining therapeutic response, and detection of complications.Narrative review.In this review, various imaging modalities employed in the management of ocular tuberculosis including fluorescein angiography (FA), indocyanine green angiography (ICGA), optical coherence tomography (OCT), and optical coherence tomography angiography (OCTA) have been reviewed. Not only do these imaging tools complement each other in providing a comprehensive assessment of the pathology, they also help in gaining valuable insights regarding the evolution of the disease.Fundus imaging plays a vital role in the diagnosis and management of patients with posterior uveitis due to tuberculosis. Fundus imaging may have a useful role in defining clinical endpoints for ocular tuberculosis in the future.

Purpose of review To review the current status of artificial intelligence systems in ophthalmology and highlight the steps required for clinical translation of artificial intelligence into personalized health care (PHC) in retinal disease. Recent findings Artificial intelligence systems for ophthalmological application have made rapid advances, but are yet to attain a state of technical maturity that allows their adoption into real-world settings. There remains an 'artificial intelligence chasm' in the spheres of validation, regulation, safe implementation, and demonstration of clinical impact that needs to be bridged before the full potential of artificial intelligence to deliver PHC can be realized. Summary Ophthalmology is currently in a stage between the demonstration of the potential of artificial intelligence and widespread deployment. Next stages include aggregating and curating datasets, training and validating artificial intelligence systems, establishing the regulatory framework, implementation and adoption with ongoing evaluation and model adjustment, and finally, meaningful human-artificial intelligence interaction with clinically validated tools that have demonstrated measurable impact on patient and healthcare system outcomes. Ophthalmologists should leverage the ability of artificial intelligence systems to glean insights from large volumes of multivariate data, and to interpret artificial intelligence recommendations in a clinical context. In doing so, the field will be well positioned to lead the transformation of health care in a personalized direction. VIDEO ABSTRACT: http://links.lww.com/COOP/A35.

OBJECTIVES: The objective of this paper is to evaluate visual acuity (VA) outcomes of intravitreal anti-vascular endothelial growth factor (VEGF) in diabetic macular oedema (DMO). METHODS: In this retrospective cohort study, electronic medical records for all patients undergoing intravitreal injections in a tertiary referral centre between March 2013 and October 2018 were analysed. Treatment response in terms of VA outcomes was reported for all eyes over a 4-year observation period. RESULTS: Our cohort includes 2614 DMO eyes of 1964 patients over 48 months. Cox proportional-hazards modelling identified injection number (hazard ratio (HR) = 1.18), male gender (HR = 1.13) and baseline VA (HR = 1.09) as independent predictors to reach a favourable visual outcome of more than 70 Early Treatment Diabetic Retinopathy Study letters. Half of our cohort reached 70 letters 1.9 months after starting anti-VEGF therapy. Of those that reached 70 letters, 50% fell below 70 letters by 14.7 months. CONCLUSION: To date, this is the largest single centre cohort study and over the longest observation period reporting on real-life outcomes of anti-VEGF in DMO. We have made an anonymised version of our data set available on an open-source data repository as a resource for clinical researchers globally.

Purpose To determine the test-retest reliability and diagnostic accuracy of a binocular optical coherence tomography (OCT) prototype (Envision Diagnostics, El Segundo, California, USA) for pupillometry. Design Assessment of diagnostic reliability and accuracy. Methods Fifty participants with relative afferent pupillary defects (RAPDs) confirmed using the swinging flashlight method (mean age 49.6 years) and 50 healthy control subjects (mean age 31.3 years) were examined. Participants twice underwent an automated pupillometry examination using a binocular OCT system that presents a stimulus and simultaneously captures OCT images of the iris-pupil plane of both eyes. Participants underwent a single examination on the RAPDx (Konan Medical, Irvine, California, USA), an automated infrared pupillometer. Pupil parameters including maximum and minimum diameter, and anisocoria were measured. The magnitude of RAPD was calculated using the log of the ratio of the constriction amplitude between the eyes. A pathological RAPD was above ±0.5 log units on both devices. Results The intraclass correlation coefficient was >0.90 for OCT-derived maximum pupil diameter, minimum pupil diameter, and anisocoria. The RAPDx had a sensitivity of 82% and a specificity of 94% for detection of RAPD whereas the binocular OCT had a sensitivity of 74% and specificity of 86%. The diagnostic accuracy of the RAPDx and binocular OCT was 88% (95% confidence interval 80%-94%) and 80% (95% confidence interval 71%-87%) respectively. Conclusions Binocular OCT-derived pupil parameters had excellent test-retest reliability. The diagnostic accuracy of RAPD was inferior to the RAPDx and is likely related to factors such as eye movement during OCT capture. As OCT becomes ubiquitous, OCT-derived measurements may provide an efficient method of objectively quantifying the pupil responses.

Progression to exudative 'wet' age-related macular degeneration (exAMD) is a major cause of visual deterioration. In patients diagnosed with exAMD in one eye, we introduce an artificial intelligence (AI) system to predict progression to exAMD in the second eye. By combining models based on three-dimensional (3D) optical coherence tomography images and corresponding automatic tissue maps, our system predicts conversion to exAMD within a clinically actionable 6-month time window, achieving a per-volumetric-scan sensitivity of 80% at 55% specificity, and 34% sensitivity at 90% specificity. This level of performance corresponds to true positives in 78% and 41% of individual eyes, and false positives in 56% and 17% of individual eyes at the high sensitivity and high specificity points, respectively. Moreover, we show that automatic tissue segmentation can identify anatomical changes before conversion and high-risk subgroups. This AI system overcomes substantial interobserver variability in expert predictions, performing better than five out of six experts, and demonstrates the potential of using AI to predict disease progression.

PurposeTo evaluate the utility of widefield optical coherence tomography angiography (WF-OCTA) compared with clinical examination in grading diabetic retinopathy in patients diagnosed clinically with proliferative diabetic retinopathy (PDR) or severe non-proliferative diabetic retinopathy (NPDR).DesignThis retrospective observational case series included patients diagnosed clinically with PDR or severe NPDR. Patients underwent standard clinical examination and WF-OCTA imaging (PLEX Elite 9000, Carl Zeiss Meditec AG) using 12×12 montage scans between August 2018 and January 2019. Two trained graders identified neovascularisation at the disc (NVD) and neovascularisation elsewhere (NVE) on WF-OCTA which were compared with the clinical examination, and to ultra-widefield fluorescein angiography (UWFA) when available.ResultsSeventy-nine eyes of 46 patients were evaluated. Of those, 57 eyes were diagnosed clinically with PDR, and 22 with severe NPDR. NVD was detected on OCTA-B scan as preretinal hyperreflective material (PRHM) in 39 eyes (100%) with evident flow signals in 79.5% compared with 51.3% detected clinically. We further classified NVD on OCTA into four subtypes and found that subtypes 1 and 2 could not be seen on clinical examination alone. WF-OCTA detected NVE in 81% of the cases compared with 55.7% detected clinically. Using WF-OCTA resulted in a higher percentage of PDR grading (88.6%) than on clinical examination (72.2%). When available, UWFA confirmed the WF-OCTA diagnosis in the majority of cases.ConclusionThis study demonstrates that WF-OCTA has a higher detection rate of PDR than clinical examination. This suggests that this modality could be used non-invasively for the purpose of early detection and characterisation of neovascularisation.

Purpose: Diabetic retinopathy is an increasingly common complication of diabetes mellitus that benefits from early diagnosis and frequent monitoring, especially in pregnancy where there is a greater risk of progression. Fundus fluorescein angiography is currently the gold standard method of investigation for neovascularization. However, this has risks of unpleasant and potentially dangerous side effects, with an added theoretical risk to the fetus in pregnancy. Wide-field optical coherence tomography angiography (PLEX Elite 9000, Carl Zeiss Meditec, Inc. USA and DRI Swept Source OCT Triton, Topcon Medical Systems, Inc. USA) potentially offers a safer, faster and equally effective alternative method for diagnosis and monitoring of diabetic retinopathy in pregnant patients. Observations: In the 3 cases reviewed from our clinic, we found that wide-field optical coherence tomography angiography was successfully used as an alternative to fundus fluorescein angiography in early diagnosis and ongoing monitoring of diabetic retinopathy in pregnant patients. Conclusions and importance: Wide-field optical coherence tomography angiography can be considered as a useful alternative to fundus fluorescein angiography when reviewing pregnant diabetic retinopathy patients. This allows avoidance of any unpleasant and potentially dangerous side effects associated with fluorescein injection, and appears to be just as effective in diagnosis and detecting disease progression.

Background/aimsEvaluation of telemedicine care models has highlighted its potential for exacerbating healthcare inequalities. This study seeks to identify and characterise factors associated with non-attendance across face-to-face and telemedicine outpatient appointments.MethodsA retrospective cohort study at a tertiary-level ophthalmic institution in the UK, between 1 January 2019 and 31 October 2021. Logistic regression modelled non-attendance against sociodemographic, clinical and operational exposure variables for all new patient registrations across five delivery modes: asynchronous, synchronous telephone, synchronous audiovisual and face to face prior to the pandemic and face to face during the pandemic.ResultsA total of 85 924 patients (median age 55 years, 54.4% female) were newly registered. Non-attendance differed significantly by delivery mode: (9.0% face to face prepandemic, 10.5% face to face during the pandemic, 11.7% asynchronous and 7.8%, synchronous during pandemic). Male sex, greater levels of deprivation, a previously cancelled appointment and not self-reporting ethnicity were strongly associated with non-attendance across all delivery modes. Individuals identifying as black ethnicity had worse attendance in synchronous audiovisual clinics (adjusted OR 4.24, 95% CI 1.59 to 11.28) but not asynchronous. Those not self-reporting their ethnicity were from more deprived backgrounds, had worse broadband access and had significantly higher non-attendance across all modes (all pConclusionPersistent non-attendance among underserved populations attending telemedicine appointments highlights the challenge digital transformation faces for reducing healthcare inequalities. Implementation of new programmes should be accompanied by investigation into the differential health outcomes of vulnerable populations.

ObjectiveTo evaluate the role of automated optical coherence tomography (OCT) segmentation, using a validated deep-learning model, for assessing the effect of C3 inhibition on the area of geographic atrophy (GA); the constituent features of GA on OCT (photoreceptor degeneration (PRD), retinal pigment epithelium (RPE) loss and hypertransmission); and the area of unaffected healthy macula.To identify OCT predictive biomarkers for GA growth.MethodsPost hoc analysis of the FILLY trial using a deep-learning model for spectral domain OCT (SD-OCT) autosegmentation. 246 patients were randomised 1:1:1 into pegcetacoplan monthly (PM), pegcetacoplan every other month (PEOM) and sham treatment (pooled) for 12 months of treatment and 6 months of therapy-free monitoring. Only participants with Heidelberg SD-OCT were included (n=197, single eye per participant).The primary efficacy endpoint was the square root transformed change in area of GA as complete RPE and outer retinal atrophy (cRORA) in each treatment arm at 12 months, with secondary endpoints including RPE loss, hypertransmission, PRD and intact macular area.ResultsEyes treated PM showed significantly slower mean change of cRORA progression at 12 and 18 months (0.151 and 0.277 mm, p=0.0039; 0.251 and 0.396 mm, p=0.039, respectively) and RPE loss (0.147 and 0.287 mm, p=0.0008; 0.242 and 0.410 mm, p=0.00809). PEOM showed significantly slower mean change of RPE loss compared with sham at 12 months (p=0.0313). Intact macular areas were preserved in PM compared with sham at 12 and 18 months (p=0.0095 and p=0.044). PRD in isolation and intact macula areas was predictive of reduced cRORA growth at 12 months (coefficient 0.0195, p=0.01 and 0.00752, p=0.02, respectively)ConclusionThe OCT evidence suggests that pegcetacoplan slows progression of cRORA overall and RPE loss specifically while protecting the remaining photoreceptors and slowing the progression of healthy retina to iRORA.

ObjectiveTo train and validate a code-free deep learning system (CFDLS) on classifying high-resolution digital retroillumination images of posterior capsule opacification (PCO) and to discriminate between clinically significant and non-significant PCOs.Methods and analysisFor this retrospective registry study, three expert observers graded two independent datasets of 279 images three separate times with no PCO to severe PCO, providing binary labels for clinical significance. The CFDLS was trained and internally validated using 179 images of a training dataset and externally validated with 100 images. Model development was through Google Cloud AutoML Vision. Intraobserver and interobserver variabilities were assessed using Fleiss kappa (κ) coefficients and model performance through sensitivity, specificity and area under the curve (AUC).ResultsIntraobserver variability κ values for observers 1, 2 and 3 were 0.90 (95% CI 0.86 to 0.95), 0.94 (95% CI 0.90 to 0.97) and 0.88 (95% CI 0.82 to 0.93). Interobserver agreement was high, ranging from 0.85 (95% CI 0.79 to 0.90) between observers 1 and 2 to 0.90 (95% CI 0.85 to 0.94) for observers 1 and 3. On internal validation, the AUC of the CFDLS was 0.99 (95% CI 0.92 to 1.0); sensitivity was 0.89 at a specificity of 1. On external validation, the AUC was 0.97 (95% CI 0.93 to 0.99); sensitivity was 0.84 and specificity was 0.92.ConclusionThis CFDLS provides highly accurate discrimination between clinically significant and non-significant PCO equivalent to human expert graders. The clinical value as a potential decision support tool in different models of care warrants further research.

IntroductionRecent years have witnessed an upsurge of demand in eye care services in the UK. With a large proportion of patients referred to Hospital Eye Services (HES) for diagnostics and disease management, the referral process results in unnecessary referrals from erroneous diagnoses and delays in access to appropriate treatment. A potential solution is a teleophthalmology digital referral pathway linking community optometry and HES.Methods and analysisThe HERMES study (Teleophthalmology-enabled and artificial intelligence-ready referral pathway for community optometry referrals of retinal disease: a cluster randomised superiority trial with a linked diagnostic accuracy study) is a cluster randomised clinical trial for evaluating the effectiveness of a teleophthalmology referral pathway between community optometry and HES for retinal diseases. Nested within HERMES is a diagnostic accuracy study, which assesses the accuracy of an artificial intelligence (AI) decision support system (DSS) for automated diagnosis and referral recommendation. A postimplementation, observational substudy, a within-trial economic evaluation and discrete choice experiment will assess the feasibility of implementation of both digital technologies within a real-life setting. Patients with a suspicion of retinal disease, undergoing eye examination and optical coherence tomography (OCT) scans, will be recruited across 24 optometry practices in the UK. Optometry practices will be randomised to standard care or teleophthalmology. The primary outcome is the proportion of false-positive referrals (unnecessary HES visits) in the current referral pathway compared with the teleophthalmology referral pathway. OCT scans will be interpreted by the AI DSS, which provides a diagnosis and referral decision and the primary outcome for the AI diagnostic study is diagnostic accuracy of the referral decision made by the Moorfields-DeepMind AI system. Secondary outcomes relate to inappropriate referral rate, cost-effectiveness analyses and human–computer interaction (HCI) analyses.Ethics and disseminationEthical approval was obtained from the London—Bromley Research Ethics Committee (REC 20/LO/1299). Findings will be reported through academic journals in ophthalmology, health services research and HCI.Trial registration numberISRCTN18106677 (protocol V.1.1).

Natural language processing (NLP) is a subfield of machine intelligence focused on the interaction of human language with computer systems. NLP has recently been discussed in the mainstream media and the literature with the advent of Generative Pre-trained Transformer 3 (GPT-3), a language model capable of producing human-like text. The release of GPT-3 has also sparked renewed interest on the applicability of NLP to contemporary healthcare problems. This article provides an overview of NLP models, with a focus on GPT-3, as well as discussion of applications specific to ophthalmology. We also outline the limitations of GPT-3 and the challenges with its integration into routine ophthalmic care.

Purpose: Vitreous haze (VH) is a key marker of inflammation in uveitis but limited by its subjectivity. Optical coherence tomography (OCT) has potential as an objective, noninvasive method for quantifying VH. We test the hypotheses that OCT can reliably quantify VH and the measurement is associated with slit-lamp based grading of VH. \ud \ud Methods: In this prospective study, participants underwent three repeated OCT macular scans to evaluate the within-eye reliability of the OCT vitreous intensity (VI). Association between OCT VI and clinical findings (including VH grade, phakic status, visual acuity [VA], anterior chamber cells, and macular thickness) were assessed. \ud \ud Results: One hundred nineteen participants were included (41 healthy participants, 32 patients with uveitis without VH, and 46 patients with uveitis with VH). Within-eye test reliability of OCT VI was high in healthy eyes and in all grades of VH (intraclass correlation coefficient [ICC] > 0.79). Average OCT VI was significantly different between healthy eyes and uveitic eyes without and uveitic eyes with VH, and was associated with increasing clinical VH grade (P < 0.05). OCT VI was significantly associated with VA, whereas clinical VH grading was not. Cataract was also associated with higher OCT VI (P = 0.03). \ud \ud Conclusions: OCT VI is a fast, noninvasive, objective, and automated method for measuring vitreous inflammation. It is associated with clinician grading of vitreous inflammation and VA, however, it can be affected by media opacities. \ud \ud Translational Relevance: OCT imaging for quantifying vitreous inflammation shows high within-eye repeatability and is associated with clinical grading of vitreous haze. OCT measurements are also associated with visual acuity but may be affected by structures anterior to the acquisition window, such as lens opacity and other anterior segment changes.

BackgroundMany artificial intelligence (AI) studies have focused on development of AI models, novel techniques, and reporting guidelines. However, little is understood about clinicians' perspectives of AI applications in medical fields including ophthalmology, particularly in light of recent regulatory guidelines. The aim for this study was to evaluate the perspectives of ophthalmologists regarding AI in 4 major eye conditions: diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD) and cataract.MethodsThis was a multi-national survey of ophthalmologists between March 1st, 2020 to February 29th, 2021 disseminated via the major global ophthalmology societies. The survey was designed based on microsystem, mesosystem and macrosystem questions, and the software as a medical device (SaMD) regulatory framework chaired by the Food and Drug Administration (FDA). Factors associated with AI adoption for ophthalmology analyzed with multivariable logistic regression random forest machine learning.ResultsOne thousand one hundred seventy-six ophthalmologists from 70 countries participated with a response rate ranging from 78.8 to 85.8% per question. Ophthalmologists were more willing to use AI as clinical assistive tools (88.1%, n = 890/1,010) especially those with over 20 years' experience (OR 3.70, 95% CI: 1.10–12.5, p = 0.035), as compared to clinical decision support tools (78.8%, n = 796/1,010) or diagnostic tools (64.5%, n = 651). A majority of Ophthalmologists felt that AI is most relevant to DR (78.2%), followed by glaucoma (70.7%), AMD (66.8%), and cataract (51.4%) detection. Many participants were confident their roles will not be replaced (68.2%, n = 632/927), and felt COVID-19 catalyzed willingness to adopt AI (80.9%, n = 750/927). Common barriers to implementation include medical liability from errors (72.5%, n = 672/927) whereas enablers include improving access (94.5%, n = 876/927). Machine learning modeling predicted acceptance from participant demographics with moderate to high accuracy, and area under the receiver operating curves of 0.63–0.83.ConclusionOphthalmologists are receptive to adopting AI as assistive tools for DR, glaucoma, and AMD. Furthermore, ML is a useful method that can be applied to evaluate predictive factors on clinical qualitative questionnaires. This study outlines actionable insights for future research and facilitation interventions to drive adoption and operationalization of AI tools for Ophthalmology.

Key Points Question What are the enablers and barriers of patient engagement for app-based home vision monitoring at scale? Findings In this cohort and survey study including 417 adults, 258 patients were active users (61.9%) of whom 166 patients (64.3%) were compliant users. Engagement was positively associated with higher comfort with technology, White British ethnicity, visual acuity, neovascular age-related macular degeneration diagnosis, and the number of intravitreal injections and was negatively associated with increased age. Meaning These findings suggest effective smartphone app-based home vision monitoring should address the risk factors for low engagement and digital exclusion during clinical practice setting deployment., This cohort study examines the associations between patient characteristics and clinical measures with vision monitoring app uptake and engagement., Importance Telemedicine is accelerating the remote detection and monitoring of medical conditions, such as vision-threatening diseases. Meaningful deployment of smartphone apps for home vision monitoring should consider the barriers to patient uptake and engagement and address issues around digital exclusion in vulnerable patient populations. Objective To quantify the associations between patient characteristics and clinical measures with vision monitoring app uptake and engagement. Design, Setting, and Participants In this cohort and survey study, consecutive adult patients attending Moorfields Eye Hospital receiving intravitreal injections for retinal disease between May 2020 and February 2021 were included. Exposures Patients were offered the Home Vision Monitor (HVM) smartphone app to self-test their vision. A patient survey was conducted to capture their experience. App data, demographic characteristics, survey results, and clinical data from the electronic health record were analyzed via regression and machine learning. Main Outcomes and Measures Associations of patient uptake, compliance, and use rate measured in odds ratios (ORs). Results Of 417 included patients, 236 (56.6%) were female, and the mean (SD) age was 72.8 (12.8) years. A total of 258 patients (61.9%) were active users. Uptake was negatively associated with age (OR, 0.98; 95% CI, 0.97-0.998; P = .02) and positively associated with both visual acuity in the better-seeing eye (OR, 1.02; 95% CI, 1.00-1.03; P = .01) and baseline number of intravitreal injections (OR, 1.01; 95% CI, 1.00-1.02; P = .02). Of 258 active patients, 166 (64.3%) fulfilled the definition of compliance. Compliance was associated with patients diagnosed with neovascular age-related macular degeneration (OR, 1.94; 95% CI, 1.07-3.53; P = .002), White British ethnicity (OR, 1.69; 95% CI, 0.96-3.01; P = .02), and visual acuity in the better-seeing eye at baseline (OR, 1.02; 95% CI, 1.01-1.04; P = .04). Use rate was higher with increasing levels of comfort with use of modern technologies (β = 0.031; 95% CI, 0.007-0.055; P = .02). A total of 119 patients (98.4%) found the app either easy or very easy to use, while 96 (82.1%) experienced increased reassurance from using the app. Conclusions and Relevance This evaluation of home vision monitoring for patients with common vision-threatening disease within a clinical practice setting revealed demographic, clinical, and patient-related factors associated with patient uptake and engagement. These insights inform targeted interventions to address risks of digital exclusion with smartphone-based medical devices.

Background/aimsDeep learning systems (DLSs) for diabetic retinopathy (DR) detection show promising results but can underperform in racial and ethnic minority groups, therefore external validation within these populations is critical for health equity. This study evaluates the performance of a DLS for DR detection among Indigenous Australians, an understudied ethnic group who suffer disproportionately from DR-related blindness.MethodsWe performed a retrospective external validation study comparing the performance of a DLS against a retinal specialist for the detection of more-than-mild DR (mtmDR), vision-threatening DR (vtDR) and all-cause referable DR. The validation set consisted of 1682 consecutive, single-field, macula-centred retinal photographs from 864 patients with diabetes (mean age 54.9 years, 52.4% women) at an Indigenous primary care service in Perth, Australia. Three-person adjudication by a panel of specialists served as the reference standard.ResultsFor mtmDR detection, sensitivity of the DLS was superior to the retina specialist (98.0% (95% CI, 96.5 to 99.4) vs 87.1% (95% CI, 83.6 to 90.6), McNemar’s test pConclusionThe DLS showed improved sensitivity and similar specificity compared with a retina specialist for DR detection. This demonstrates its potential to support DR screening among Indigenous Australians, an underserved population with a high burden of diabetic eye disease.

Purpose of review The development of deep learning (DL) systems requires a large amount of data, which may be limited by costs, protection of patient information and low prevalence of some conditions. Recent developments in artificial intelligence techniques have provided an innovative alternative to this challenge via the synthesis of biomedical images within a DL framework known as generative adversarial networks (GANs). This paper aims to introduce how GANs can be deployed for image synthesis in ophthalmology and to discuss the potential applications of GANs-produced images. Recent findings Image synthesis is the most relevant function of GANs to the medical field, and it has been widely used for generating 'new' medical images of various modalities. In ophthalmology, GANs have mainly been utilized for augmenting classification and predictive tasks, by synthesizing fundus images and Optical coherence tomography images with and without pathologies such as age-related macular degeneration and diabetic retinopathy. Despite their ability to generate high-resolution images, the development of GANs remains data intensive, and there is a lack of consensus on how best to evaluate the outputs produced by GANs. Summary Although the problem of artificial biomedical data generation is of great interest, image synthesis by GANs represents an innovation with yet unclear relevance for ophthalmology.