Quantitative Analysis of OCT for Neovascular Age-Related Macular Degeneration Using Deep Learning.

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.

Relationship between Psychophysical Measures of Retinal Ganglion Cell Density and In Vivo Measures of Cone Density in Glaucoma.

PURPOSE: Considerable between-individual variation in retinal ganglion cell (RGC) density exists in healthy individuals, making identification of change from normal to glaucoma difficult. In ascertaining local cone-to-RGC density ratios in healthy individuals, we wished to investigate the usefulness of objective cone density estimates as a surrogate of baseline RGC density in glaucoma patients, and thus a more efficient way of identifying early changes. DESIGN: Exploratory cohort study. PARTICIPANTS: Twenty glaucoma patients (60% women) with a median age of 54 years and mean deviation (MD) in the visual field of -5 dB and 20 healthy controls (70% women) with a median age of 57 years and a mean MD of 0 dB were included. METHODS: Glaucoma patients and healthy participants underwent in vivo cone imaging at 4 locations of 8.8° eccentricity with a modified Heidelberg Retina Angiograph HRA2 (scan angle, 3°). Cones were counted using an automated program. Retinal ganglion cell density was estimated at the same test locations from peripheral grating resolution acuity thresholds. MAIN OUTCOME MEASURES: Retinal cone density, estimated RGC density, and cone-to-RGC ratios in glaucoma patients and healthy controls. RESULTS: Median cone-to-RGC density was 3.51:1 (interquartile range [IQR], 2.59:1-6.81:1) in glaucoma patients compared with 2.35:1 (IQR, 1.83:1-2.82:1) in healthy participants. Retinal ganglion cell density was 33% lower in glaucoma patients than in healthy participants; however, cone density was very similar in glaucoma patients (7248 cells/mm2) and healthy controls (7242 cells/mm2). The area under the receiver operator characteristic curve was 0.79 (95% confidence interval [CI], 0.71-0.86) for both RGC density and cone-to-RGC ratio and 0.49 (95% CI, 0.39-0.58) for cone density. CONCLUSIONS: Local measurements of cone density do not differ significantly from normal in glaucoma patients despite large differences in RGC density. There was no statistically significant association between RGC density and cone density in the normal participants, and the range of cone-to-RGC density ratios was relatively large in healthy controls. These findings suggest that estimates of baseline RGC density from cone density are unlikely to be precise and offer little advantage over determination of RGC alone in the identification of early glaucomatous change.

Vision-Language Models for Feature Detection of Macular Diseases on Optical Coherence Tomography.

Importance: Vision-language models (VLMs) are a novel artificial intelligence technology capable of processing image and text inputs. While demonstrating strong generalist capabilities, their performance in ophthalmology has not been extensively studied. Objective: To assess the performance of the Gemini Pro VLM in expert-level tasks for macular diseases from optical coherence tomography (OCT) scans. Design, Setting, and Participants: This was a cross-sectional diagnostic accuracy study evaluating a generalist VLM on ophthalmology-specific tasks using the open-source Optical Coherence Tomography Image Database. The dataset included OCT B-scans from 50 unique patients: healthy individuals and those with macular hole, diabetic macular edema, central serous chorioretinopathy, and age-related macular degeneration. Each OCT scan was labeled for 10 key pathological features, referral recommendations, and treatments. The images were captured using a Cirrus high definition OCT machine (Carl Zeiss Meditec) at Sankara Nethralaya Eye Hospital, Chennai, India, and the dataset was published in December 2018. Image acquisition dates were not specified. Exposures: Gemini Pro, using a standard prompt to extract structured responses on December 15, 2023. Main Outcomes and Measures: The primary outcome was model responses compared against expert labels, calculating F1 scores for each pathological feature. Secondary outcomes included accuracy in diagnosis, referral urgency, and treatment recommendation. The model's internal concordance was evaluated by measuring the alignment between referral and treatment recommendations, independent of diagnostic accuracy. Results: The mean F1 score was 10.7% (95% CI, 2.4-19.2). Measurable F1 scores were obtained for macular hole (36.4%; 95% CI, 0-71.4), pigment epithelial detachment (26.1%; 95% CI, 0-46.2), subretinal hyperreflective material (24.0%; 95% CI, 0-45.2), and subretinal fluid (20.0%; 95% CI, 0-45.5). A correct diagnosis was achieved in 17 of 50 cases (34%; 95% CI, 22-48). Referral recommendations varied: 28 of 50 were correct (56%; 95% CI, 42-70), 10 of 50 were overcautious (20%; 95% CI, 10-32), and 12 of 50 were undercautious (24%; 95% CI, 12-36). Referral and treatment concordance were very high, with 48 of 50 (96%; 95 % CI, 90-100) and 48 of 49 (98%; 95% CI, 94-100) correct answers, respectively. Conclusions and Relevance: In this study, a generalist VLM demonstrated limited vision capabilities for feature detection and management of macular disease. However, it showed low self-contradiction, suggesting strong language capabilities. As VLMs continue to improve, validating their performance on large benchmarking datasets will help ascertain their potential in ophthalmology.

Artificial Intelligence to Facilitate Clinical Trial Recruitment in Age-Related Macular Degeneration.

Objective: Recent developments in artificial intelligence (AI) have positioned it to transform several stages of the clinical trial process. In this study, we explore the role of AI in clinical trial recruitment of individuals with geographic atrophy (GA), an advanced stage of age-related macular degeneration, amidst numerous ongoing clinical trials for this condition. Design: Cross-sectional study. Subjects: Retrospective dataset from the INSIGHT Health Data Research Hub at Moorfields Eye Hospital in London, United Kingdom, including 306 651 patients (602 826 eyes) with suspected retinal disease who underwent OCT imaging between January 1, 2008 and April 10, 2023. Methods: A deep learning model was trained on OCT scans to identify patients potentially eligible for GA trials, using AI-generated segmentations of retinal tissue. This method's efficacy was compared against a traditional keyword-based electronic health record (EHR) search. A clinical validation with fundus autofluorescence (FAF) images was performed to calculate the positive predictive value of this approach, by comparing AI predictions with expert assessments. Main Outcome Measures: The primary outcomes included the positive predictive value of AI in identifying trial-eligible patients, and the secondary outcome was the intraclass correlation between GA areas segmented on FAF by experts and AI-segmented OCT scans. Results: The AI system shortlisted a larger number of eligible patients with greater precision (1139, positive predictive value: 63%; 95% confidence interval [CI]: 54%-71%) compared with the EHR search (693, positive predictive value: 40%; 95% CI: 39%-42%). A combined AI-EHR approach identified 604 eligible patients with a positive predictive value of 86% (95% CI: 79%-92%). Intraclass correlation of GA area segmented on FAF versus AI-segmented area on OCT was 0.77 (95% CI: 0.68-0.84) for cases meeting trial criteria. The AI also adjusts to the distinct imaging criteria from several clinical trials, generating tailored shortlists ranging from 438 to 1817 patients. Conclusions: This study demonstrates the potential for AI in facilitating automated prescreening for clinical trials in GA, enabling site feasibility assessments, data-driven protocol design, and cost reduction. Once treatments are available, similar AI systems could also be used to identify individuals who may benefit from treatment. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Quantifying Changes on OCT in Eyes Receiving Treatment for Neovascular Age-Related Macular Degeneration.

Purpose: Application of artificial intelligence (AI) to macular OCT scans to segment and quantify volumetric change in anatomical and pathological features during intravitreal treatment for neovascular age-related macular degeneration (AMD). Design: Retrospective analysis of OCT images from the Moorfields Eye Hospital AMD Database. Participants: A total of 2115 eyes from 1801 patients starting anti-VEGF treatment between June 1, 2012, and June 30, 2017. Methods: The Moorfields Eye Hospital neovascular AMD database was queried for first and second eyes receiving anti-VEGF treatment and had an OCT scan at baseline and 12 months. Follow-up scans were input into the AI system and volumes of OCT variables were studied at different time points and compared with baseline volume groups. Cross-sectional comparisons between time points were conducted using Mann-Whitney U test. Main Outcome Measures: Volume outputs of the following variables were studied: intraretinal fluid, subretinal fluid, pigment epithelial detachment (PED), subretinal hyperreflective material (SHRM), hyperreflective foci, neurosensory retina, and retinal pigment epithelium. Results: Mean volumes of analyzed features decreased significantly from baseline to both 4 and 12 months, in both first-treated and second-treated eyes. Pathological features that reflect exudation, including pure fluid components (intraretinal fluid and subretinal fluid) and those with fluid and fibrovascular tissue (PED and SHRM), displayed similar responses to treatment over 12 months. Mean PED and SHRM volumes showed less pronounced but also substantial decreases over the first 2 months, reaching a plateau postloading phase, and minimal change to 12 months. Both neurosensory retina and retinal pigment epithelium volumes showed gradual reductions over time, and were not as substantial as exudative features. Conclusions: We report the results of a quantitative analysis of change in retinal segmented features over time, enabled by an AI segmentation system. Cross-sectional analysis at multiple time points demonstrated significant associations between baseline OCT-derived segmented features and the volume of biomarkers at follow-up. Demonstrating how certain OCT biomarkers progress with treatment and the impact of pretreatment retinal morphology on different structural volumes may provide novel insights into disease mechanisms and aid the personalization of care. Data will be made public for future studies. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Ageing changes in retinal outer nuclear layer thickness and cone photoreceptor density using adaptive optics-free imaging.

PURPOSE: To investigate age-related changes of the outer nuclear layer (ONL) thickness and cone density, and their associations in healthy participants using a modified, narrow scan-angle Heidelberg Retina Angiograph (HRA2). METHODS: Retinal cones were imaged outside the fovea at 8.8° eccentricity and cone density was compared to ONL thickness measurements obtained by Spectral-Domain Optical Coherence Tomography (SD-OCT) at the same locations. Fifty-six eyes of 56 healthy participants with a median age (interquartile range, IQR) of 37 years (29-55) were included. RESULTS: Median (IQR) cone count was 7,472 (7,188, 7,746) cones/mm2 and median (IQR) ONL thickness was 56 (52, 60) µm for healthy participants. Both cone density and ONL thickness were negatively associated with age: cone density, R2 = 0.16 (F(1,54) = 10.41, P = 0.002); ONL thickness, R2 = 0.12 (F(1,54) = 7.41, P = 0.009). No significant association was seen between cone density and ONL thickness (R2 = 0.03; F(1,54) = 1.66, P = 0.20). CONCLUSION: Cone density was lower, and ONL thinner, in older compared to younger participants, therefore, image-based structural measures should be compared to age-related data. However, cone density and ONL thickness were not strongly associated, indicating that determinants of ONL thickness measurements other than cone density measurements, and including measurement error, have a major influence.

Risk Stratification for Diabetic Retinopathy Screening Order Using Deep Learning: A Multicenter Prospective Study.

Purpose: Real-world evaluation of a deep learning model that prioritizes patients based on risk of progression to moderate or worse (MOD+) diabetic retinopathy (DR). Methods: This nonrandomized, single-arm, prospective, interventional study included patients attending DR screening at four centers across Thailand from September 2019 to January 2020, with mild or no DR. Fundus photographs were input into the model, and patients were scheduled for their subsequent screening from September 2020 to January 2021 in order of predicted risk. Evaluation focused on model sensitivity, defined as correctly ranking patients that developed MOD+ within the first 50% of subsequent screens. Results: We analyzed 1,757 patients, of which 52 (3.0%) developed MOD+. Using the model-proposed order, the model's sensitivity was 90.4%. Both the model-proposed order and mild/no DR plus HbA1c had significantly higher sensitivity than the random order (P < 0.001). Excluding one major (rural) site that had practical implementation challenges, the remaining sites included 567 patients and 15 (2.6%) developed MOD+. Here, the model-proposed order achieved 86.7% versus 73.3% for the ranking that used DR grade and hemoglobin A1c. Conclusions: The model can help prioritize follow-up visits for the largest subgroups of DR patients (those with no or mild DR). Further research is needed to evaluate the impact on clinical management and outcomes. Translational Relevance: Deep learning demonstrated potential for risk stratification in DR screening. However, real-world practicalities must be resolved to fully realize the benefit.

Intravitreal injections: past trends and future projections within a UK tertiary hospital.

AIMS: To describe past trends and future projections for the number of intravitreal injections being administered at a large tertiary hospital in London, United Kingdom. METHODS: Retrospective data from Moorfields Eye Hospital were collected using the electronic medical record system. Descriptive statistics were used to visualise overall trends. Time series forecasting was used to predict the number of injections that will be administered up to and including the year 2029. RESULTS: The number of injections has increased nearly 11-fold from 2009 to 2019, with a total of 44,924 injections delivered in 2019. The majority of injections were given for the treatment of neovascular age-related macular degeneration. Aflibercept formed 87% of injections administered in 2019. The number of injections is predicted to continue to increase every year, with nearly 83,000 injections forecasted in the year 2029. CONCLUSION: The demand for intravitreal injections has increased substantially over the last decade and is predicted to further increase. Healthcare systems will need to adapt to accommodate the high demand. Other solutions may include longer-acting therapies to reduce the treatment burden.

Deep Learning to Detect OCT-derived Diabetic Macular Edema from Color Retinal Photographs: A Multicenter Validation Study.

PURPOSE: To validate the generalizability of a deep learning system (DLS) that detects diabetic macular edema (DME) from 2-dimensional color fundus photographs (CFP), for which the reference standard for retinal thickness and fluid presence is derived from 3-dimensional OCT. DESIGN: Retrospective validation of a DLS across international datasets. PARTICIPANTS: Paired CFP and OCT of patients from diabetic retinopathy (DR) screening programs or retina clinics. The DLS was developed using data sets from Thailand, the United Kingdom, and the United States and validated using 3060 unique eyes from 1582 patients across screening populations in Australia, India, and Thailand. The DLS was separately validated in 698 eyes from 537 screened patients in the United Kingdom with mild DR and suspicion of DME based on CFP. METHODS: The DLS was trained using DME labels from OCT. The presence of DME was based on retinal thickening or intraretinal fluid. The DLS's performance was compared with expert grades of maculopathy and to a previous proof-of-concept version of the DLS. We further simulated the integration of the current DLS into an algorithm trained to detect DR from CFP. MAIN OUTCOME MEASURES: The superiority of specificity and noninferiority of sensitivity of the DLS for the detection of center-involving DME, using device-specific thresholds, compared with experts. RESULTS: The primary analysis in a combined data set spanning Australia, India, and Thailand showed the DLS had 80% specificity and 81% sensitivity, compared with expert graders, who had 59% specificity and 70% sensitivity. Relative to human experts, the DLS had significantly higher specificity (P = 0.008) and noninferior sensitivity (P < 0.001). In the data set from the United Kingdom, the DLS had a specificity of 80% (P < 0.001 for specificity of >50%) and a sensitivity of 100% (P = 0.02 for sensitivity of > 90%). CONCLUSIONS: The DLS can generalize to multiple international populations with an accuracy exceeding that of experts. The clinical value of this DLS to reduce false-positive referrals, thus decreasing the burden on specialist eye care, warrants a prospective evaluation.

Single-unit muscle sympathetic nervous activity and its relation to cardiac noradrenaline spillover.

Recent work using single-unit sympathetic nerve recording techniques has demonstrated aberrations in the firing pattern of sympathetic nerves in a variety of patient groups. We sought to examine whether nerve firing pattern is associated with increased noradrenaline release. Using single-unit muscle sympathetic nerve recording techniques coupled with direct cardiac catheterisation and noradrenaline isotope dilution methodology we examined the relationship between single-unit firing patterns and cardiac and whole body noradrenaline spillover to plasma. Participants comprised patients with hypertension (n=6), depression (n=7) and panic disorder (n =9) who were drawn from our ongoing studies. The patient groups examined did not differ in their single-unit muscle sympathetic nerve firing characteristics nor in the rate of spillover of noradrenaline to plasma from the heart. The median incidence of multiple spikes per beat was 9%. Patients were stratified according to the firing pattern: low level of incidence (less than 9% incidence of multiple spikes per beat) and high level of incidence (greater than 9% incidence of multiple spikes per beat). High incidence of multiple spikes within a cardiac cycle was associated with higher firing rates (P <0.0001) and increased probability of firing (P <0.0001). Whole body noradrenaline spillover to plasma and (multi-unit) muscle sympathetic nerve activity in subjects with low incidence of multiple spikes was not different to that of those with high incidence of multiple spikes. In those with high incidence of multiple spikes there occurred a parallel activation of the sympathetic outflow to the heart, with cardiac noradrenaline spillover to plasma being two times that of subjects with low nerve firing rates (11.0 ± 1.5 vs. 22.0 ± 4.5 ng min⁻¹, P <0.05). This study indicates that multiple within-burst firing and increased single-unit firing rates of the sympathetic outflow to the skeletal muscle vasculature is associated with high cardiac noradrenaline spillover.

Optical coherence tomography in the 2020s-outside the eye clinic.

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.

Predicting Incremental and Future Visual Change in Neovascular Age-Related Macular Degeneration Using Deep Learning.

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 < 0.001 for both), respectively. Best performing models included both baseline qOCT parameters and treatment response. Furthermore, we present proof-of-principle evidence that the incremental change in VA from an injection can be predicted: R2 = 0.14 (MAE, 5.6 ETDRS letters) for injection 2 and R2 = 0.11 (MAE, 5.0 ETDRS letters) for injection 3 (P < 0.001 for both). 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.

Ten-year survival trends of neovascular age-related macular degeneration at first presentation.

BACKGROUND: To 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). METHODS: Retrospective 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. RESULTS: For 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). CONCLUSIONS: The 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.

Perfluorocarbons Therapeutics in Modern Cancer Nanotechnology for Hypoxiainduced Anti-tumor Therapy.

With an estimated failure rate of about 90%, immunotherapies that are intended for the treatment of solid tumors have caused an anomalous rise in the mortality rate over the past decades. It is apparent that resistance towards such therapies primarily occurs due to elevated levels of HIF-1 (Hypoxia-induced factor) in tumor cells, which are caused by disrupted microcirculation and diffusion mechanisms. With the advent of nanotechnology, several innovative advances were brought to the fore; and, one such promising direction is the use of perfluorocarbon nanoparticles in the management of solid tumors. Perfluorocarbon nanoparticles enhance the response of hypoxia-based agents (HBAs) within the tumor cells and have been found to augment the entry of HBAs into the tumor micro-environment. The heightened penetration of HBAs causes chronic hypoxia, thus aiding in the process of cell quiescence. In addition, this technology has also been applied in photodynamic therapy, where oxygen self-enriched photosensitizers loaded perfluorocarbon nanoparticles are employed. The resulting processes initiate a cascade, depleting tumour oxygen and turning it into a reactive oxygen species eventually to destroy the tumour cell. This review elaborates on the multiple applications of nanotechnology based perfluorocarbon formulations that are being currently employed in the treatment of tumour hypoxia.

Validation and Clinical Applicability of Whole-Volume Automated Segmentation of Optical Coherence Tomography in Retinal Disease Using Deep Learning.

IMPORTANCE: Quantitative volumetric measures of retinal disease in optical coherence tomography (OCT) scans are infeasible to perform owing to the time required for manual grading. Expert-level deep learning systems for automatic OCT segmentation have recently been developed. However, the potential clinical applicability of these systems is largely unknown. OBJECTIVE: To evaluate a deep learning model for whole-volume segmentation of 4 clinically important pathological features and assess clinical applicability. DESIGN, SETTING, PARTICIPANTS: This diagnostic study used OCT data from 173 patients with a total of 15 558 B-scans, treated at Moorfields Eye Hospital. The data set included 2 common OCT devices and 2 macular conditions: wet age-related macular degeneration (107 scans) and diabetic macular edema (66 scans), covering the full range of severity, and from 3 points during treatment. Two expert graders performed pixel-level segmentations of intraretinal fluid, subretinal fluid, subretinal hyperreflective material, and pigment epithelial detachment, including all B-scans in each OCT volume, taking as long as 50 hours per scan. Quantitative evaluation of whole-volume model segmentations was performed. Qualitative evaluation of clinical applicability by 3 retinal experts was also conducted. Data were collected from June 1, 2012, to January 31, 2017, for set 1 and from January 1 to December 31, 2017, for set 2; graded between November 2018 and January 2020; and analyzed from February 2020 to November 2020. MAIN OUTCOMES AND MEASURES: Rating and stack ranking for clinical applicability by retinal specialists, model-grader agreement for voxelwise segmentations, and total volume evaluated using Dice similarity coefficients, Bland-Altman plots, and intraclass correlation coefficients. RESULTS: Among the 173 patients included in the analysis (92 [53%] women), qualitative assessment found that automated whole-volume segmentation ranked better than or comparable to at least 1 expert grader in 127 scans (73%; 95% CI, 66%-79%). A neutral or positive rating was given to 135 model segmentations (78%; 95% CI, 71%-84%) and 309 expert gradings (2 per scan) (89%; 95% CI, 86%-92%). The model was rated neutrally or positively in 86% to 92% of diabetic macular edema scans and 53% to 87% of age-related macular degeneration scans. Intraclass correlations ranged from 0.33 (95% CI, 0.08-0.96) to 0.96 (95% CI, 0.90-0.99). Dice similarity coefficients ranged from 0.43 (95% CI, 0.29-0.66) to 0.78 (95% CI, 0.57-0.85). CONCLUSIONS AND RELEVANCE: This deep learning-based segmentation tool provided clinically useful measures of retinal disease that would otherwise be infeasible to obtain. Qualitative evaluation was additionally important to reveal clinical applicability for both care management and research.

In Vivo Confocal Microscopy Morphologic Features and Cyst Density in Acanthamoeba Keratitis.

PURPOSE: To correlate in vivo confocal microscopy morphologic features (IVCM-MF) and Acanthamoeba cyst density (ACD) with final best-corrected visual acuity (BCVA) in Acanthamoeba keratitis (AK). DESIGN: Retrospective cohort study. METHODS: Patient demographics, treatment outcome, and corresponding IVCM-MF performed at the acute stage of infection were analyzed. Inclusion criteria were microbiological positive AK cases seen at Moorfields Eye Hospital between February 2013 and October 2017. Statistical significance was assessed by multinomial regression and multiple linear regression analysis. Main outcome measure was final BCVA. RESULTS: A total of 157 eyes (157 patients) had AK. Absence of single-file round/ovoid objects was associated with a BCVA of 6/36 to 6/9 (odds ratio [OR] 8.13; 95% confidence interval [CI], 1.55-42.56, P = .013) and ≥6/6 (OR 10.50; 95% CI, 2.12-51.92, P = .004) when compared to no perception of light to 6/60. Absence of rod/spindle objects was associated with a BCVA of ≥6/6 (OR 4.55; 95% CI, 1.01-20.45, P = .048). Deep stromal/ring infiltrate was associated with single-file round/ovoid objects (OR 7.78; 95% CI, 2.69-22.35, P < .001), rod/spindle objects (OR 7.05; 95% CI, 2.11-23.59, P = .002), and binary round/ovoid objects (OR 3.45; 95% CI, 1.17-10.14, P = .024). There was a positive association between ACD and treatment duration (ß = 0.14, P = .049), number of IVCM-MF (ß = 0.34, P = .021), and clusters of round/ovoid objects (ß = 0.29, P = .002). CONCLUSIONS: Specific IVCM-MF correlate with ACD and clinical staging of disease, and are prognostic indicators for a poorer visual outcome.

Automated Pupillometry Using a Prototype Binocular Optical Coherence Tomography System.

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.

Moorfields AMD database report 2: fellow eye involvement with neovascular age-related macular degeneration.

BACKGROUND/AIMS: Neovascular age-related macular degeneration (nAMD) is frequently bilateral, and previous reports on 'fellow eyes' have assumed sequential treatment after a period of treatment of the first eye only. The aim of our study was to analyse baseline characteristics and visual acuity (VA) outcomes of fellow eye involvement with nAMD, specifically differentiating between sequential and non-sequential (due to macular scarring in the first eye) antivascular endothelial growth factor treatment and timelines for fellow eye involvement. METHODS: Retrospective, electronic medical record database study of the Moorfields AMD database of 6265 patients/120 286 single entries with data extracted between 21 October 2008 and 9 August 2018. The data set for analysis consisted of 1180 sequential, 807 non-sequential and 3410 unilateral eyes. RESULTS: Mean VA (ETDRS letters±SD) of sequentially treated fellow eyes at baseline was significantly higher (63±13), VA gain over 2 years lower (0.37±14) and proportion of eyes with good VA (≥70 letters) higher (46%) than the respective first eyes (baseline VA 54±16, VA gain at 2 years 5.6±15, percentage of eyes with good VA 39%). Non-sequential fellow eyes showed baseline characteristics and VA outcomes similar to first eyes. Fellow eye involvement rate was 32% at 2 years, and median time interval to fellow eye involvement was 71 (IQR: 27-147) weeks. CONCLUSION: This report shows that sequentially treated nAMD fellow eyes have better baseline and final VA than non-sequentially treated eyes after 2 years of treatment. Sequentially treated eyes also had a greater proportion with good VA after 2 years.

The Utility of Corneal Nerve Fractal Dimension Analysis in Peripheral Neuropathies of Different Etiology.

Purpose: Quantification of corneal confocal microscopy (CCM) images has shown a significant reduction in corneal nerve fiber length (CNFL) in a range of peripheral neuropathies. We assessed whether corneal nerve fractal dimension (CNFrD) analysis, a novel metric to quantify the topological complexity of corneal subbasal nerves, can differentiate peripheral neuropathies of different etiology. Methods: Ninety patients with peripheral neuropathy, including 29 with diabetic peripheral neuropathy (DPN), 34 with chronic inflammatory demyelinating polyneuropathy (CIDP), 13 with chemotherapy-induced peripheral neuropathy (CIPN), 14 with human immunodeficiency virus-associated sensory neuropathy (HIV-SN), and 20 healthy controls (HCs), underwent CCM for estimation of corneal nerve fiber density (CNFD), CNFL, corneal nerve branch density (CNBD), CNFrD, and CNFrD adjusted for CNFL (ACNFrD). Results: In patients with DPN, CIDP, CIPN, or HIV-SN compared to HCs, CNFD (P = 0.004-0.0001) and CNFL (P = 0.05-0.0001) were significantly lower, with a further significant reduction among subgroups. CNFrD was significantly lower in patients with CIDP compared to HCs and patients with HIV-SN (P = 0.02-0.0009) and in patients with DPN compared to HCs and patients with HIV-SN, CIPN, or CIDP (P = 0.001-0.0001). ACNFrD was lower in patients with CIPN, CIDP, or DPN compared to HCs (P = 0.03-0.0001) and in patients with DPN compared to those with HIV-SN, CIPN, or CIDP (P = 0.01-0.005). Conclusions: CNFrD can detect a distinct pattern of corneal nerve loss in patients with DPN or CIDP compared to those with CIPN or HIV-SN and controls. Translational Relevance: Various peripheral neuropathies are characterized by a comparable degree of corneal nerve loss. Assessment of corneal nerve topology by CNFrD could be useful in differentiating neuropathies based on the pattern of loss.

Predicting conversion to wet age-related macular degeneration using deep learning.

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.