Identifying the content for an item bank and computerized adaptive testing system to measure the impact of age-related macular degeneration on health-related quality of life.

PURPOSE: We are developing an age-related macular degeneration (AMD) health-related quality of life (HRQoL) item bank, applicable to Western and Asian populations. We report primarily on content generation and refinement, but also compare the HRQoL issues reported in our study with Western studies and current AMD-HRQoL questionnaires. METHODS: In this cross-sectional, qualitative study of AMD patients attending the Singapore National Eye Centre (May-December 2019), items/domains were generated from: (1) AMD-specific questionnaires; (2) published articles; (3) focus groups/semi-structured interviews with AMD patients (n = 27); and (4) written feedback from retinal experts. Following thematic analysis, items were systematically refined to a minimally representative set and pre-tested using cognitive interviews with 16 AMD patients. RESULTS: Of the 27 patients (mean ± standard deviation age 67.9 ± 7.0; 59.2% male), 18 (66.7%), two (7.4%), and seven (25.9%) had no, early-intermediate, and late/advanced AMD (better eye), respectively. Whilst some HRQoL issues, e.g. activity limitation, mobility, lighting, and concerns were similarly reported by Western patients and covered by other questionnaires, others like anxiety about intravitreal injections, work tasks, and financial dependency were novel. Overall, 462 items within seven independent HRQoL domains were identified: Activity limitation, Lighting, Mobility, Emotional, Concerns, AMD management, and Work. Following item refinement, items were reduced to 219, with 31 items undergoing amendment. CONCLUSION: Our 7-domain, 219-item AMD-specific HRQoL instrument will undergo psychometric testing and calibration for computerized adaptive testing. The future instrument will enable users to precisely, rapidly, and comprehensively quantify the HRQoL impact of AMD and associated treatments, with item coverage relevant across several populations.

Artificial intelligence for diabetic retinopathy screening, prediction and management.

PURPOSE OF REVIEW: Diabetic retinopathy is the most common specific complication of diabetes mellitus. Traditional care for patients with diabetes and diabetic retinopathy is fragmented, uncoordinated and delivered in a piecemeal nature, often in the most expensive and high-resource tertiary settings. Transformative new models incorporating digital technology are needed to address these gaps in clinical care. RECENT FINDINGS: Artificial intelligence and telehealth may improve access, financial sustainability and coverage of diabetic retinopathy screening programs. They enable risk stratifying patients based on individual risk of vision-threatening diabetic retinopathy including diabetic macular edema (DME), and predicting which patients with DME best respond to antivascular endothelial growth factor therapy. SUMMARY: Progress in artificial intelligence and tele-ophthalmology for diabetic retinopathy screening, including artificial intelligence applications in 'real-world settings' and cost-effectiveness studies are summarized. Furthermore, the initial research on the use of artificial intelligence models for diabetic retinopathy risk stratification and management of DME are outlined along with potential future directions. Finally, the need for artificial intelligence adoption within ophthalmology in response to coronavirus disease 2019 is discussed. Digital health solutions such as artificial intelligence and telehealth can facilitate the integration of community, primary and specialist eye care services, optimize the flow of patients within healthcare networks, and improve the efficiency of diabetic retinopathy management.

DIABETIC MACULAR ISCHEMIA: Correlation of Retinal Vasculature Changes by Optical Coherence Tomography Angiography and Functional Deficit.

PURPOSE: To examine the relationship between macular microvasculature parameters and functional changes in persons with diabetic retinopathy (DR). METHODS: Cross-sectional study of 76 eyes with varying levels of DR. Optical coherence tomography angiography (OCTA) quantified superficial and deep perifoveal vessel densities and foveal avascular zone areas. Retinal sensitivity was measured using microperimetry. Optical coherence tomography angiography parameters and retinal sensitivity were correlated. RESULTS: Deep perifoveal vessel density decreased with increasing severity of DR (adjusted mean 51.93 vs. 49.89 vs. 47.96, P-trend = 0.005). Superficial and deep foveal avascular zone area increased with increasing DR severity (adjusted mean: 235.0 µm vs. 303.4 µm vs. 400.9 µm, P-trend = 0.003 [superficial]; 333.1 µm vs. 513.3 µm vs. 530.2 µm, P-trend = 0.001 [deep]). Retinal sensitivity decreased with increasing DR severity (adjusted mean: 25.12 dB vs. 22.34 dB vs. 20.67 dB, P-trend = 0.003). Retinal sensitivity correlated positively with deep perifoveal vessel density (Pearson's ρ = 0.276, P = 0.020) and inversely with superficial foveal avascular zone area (Pearson's ρ = -0.333, P = 0.010). CONCLUSION: Alterations in retinal microvasculature can be observed with OCTA with increasing severity of DR. These changes are correlated with reduced retinal sensitivity. Optical coherence tomography angiography is useful to detect and quantify the microvasculature properties of eyes with diabetic macular ischemia.

Artificial Intelligence Screening for Diabetic Retinopathy: the Real-World Emerging Application.

PURPOSE OF REVIEW: This paper systematically reviews the recent progress in diabetic retinopathy screening. It provides an integrated overview of the current state of knowledge of emerging techniques using artificial intelligence integration in national screening programs around the world. Existing methodological approaches and research insights are evaluated. An understanding of existing gaps and future directions is created. RECENT FINDINGS: Over the past decades, artificial intelligence has emerged into the scientific consciousness with breakthroughs that are sparking increasing interest among computer science and medical communities. Specifically, machine learning and deep learning (a subtype of machine learning) applications of artificial intelligence are spreading into areas that previously were thought to be only the purview of humans, and a number of applications in ophthalmology field have been explored. Multiple studies all around the world have demonstrated that such systems can behave on par with clinical experts with robust diagnostic performance in diabetic retinopathy diagnosis. However, only few tools have been evaluated in clinical prospective studies. Given the rapid and impressive progress of artificial intelligence technologies, the implementation of deep learning systems into routinely practiced diabetic retinopathy screening could represent a cost-effective alternative to help reduce the incidence of preventable blindness around the world.

Surgical Approaches for Cell Therapeutics Delivery to the Retinal Pigment Epithelium and Retina.

Developing successful surgical strategies to deliver cell therapeutics to the back of the eye is an essential pillar to success for stem cell-based applications in blinding retinal diseases. Within this chapter, we have attempted to gather all key considerations during preclinical animal trials.Guidance is provided for choices on animal models, options for immunosuppression, as well as anesthesia. Subsequently we cover surgical strategies for RPE graft delivery, both as suspension as well as in monolayers in small rodents, rabbits, pigs, and nonhuman primate. A detailed account is given in particular on animal variations in vitrectomy and subretinal surgery, which requires a considerable learning curve, when transiting from human to animal. In turn, however, many essential subretinal implantation techniques in large-eyed animals are directly transferrable to human clinical trial protocols.A dedicated subchapter on photoreceptor replacement provides insights on preparation of suspension as well as sheet grafts, to subsequently outline the basics of subretinal delivery via both the transscleral and transvitreal route. In closing, a future outlook on vision restoration through retinal cell-based therapeutics is presented.

Ethnic Differences in the Prevalence and Risk Factors of Diabetic Retinopathy: The Singapore Epidemiology of Eye Diseases Study.

PURPOSE: To evaluate the prevalence and risk factors for diabetic retinopathy (DR) in the Singapore Epidemiology of Eye Diseases (SEED) Study. DESIGN: Population-based, cross-sectional study. PARTICIPANTS: Persons of Malay, Indian, and Chinese ethnicity aged 40+ years, living in Singapore. METHODS: Diabetes was defined as nonfasting plasma glucose ≥200 mg/dl (11.1 mmol/l), glycated hemoglobin A1c (HbA1c) >6.5%, self-reported physician-diagnosed diabetes, or the use of glucose-lowering medication. Retinal photographs, were graded for the presence and severity of DR using the modified Airlie House classification system. MAIN OUTCOME MEASURES: Diabetic retinopathy, diabetic macular edema (DME), vision-threatening diabetic retinopathy (VTDR), defined as the presence of severe nonproliferative or proliferative DR, or clinically significant macular edema (CSME). RESULTS: Of the 10 033 subjects, 2877 (28.7%) had diabetes and gradable photographs for analysis. The overall age-standardized prevalence (95% confidence interval [CI]) was 28.2% (25.9-30.6) for any DR, 7.6% (6.5-9.0) for DME, and 7.7% (6.6-9.0) for VTDR. Indians had a higher prevalence of any DR (30.7% vs. 26.2% in Chinese and 25.5% in Malays, P = 0.012); a similar trend was noted for any DME (P = 0.001) and CSME (P = 0.032). Independent risk factors for any DR were Indian ethnicity (odds ratio [OR], 1.41; 95% CI, 1.09-1.83, vs. Chinese), diabetes duration (OR, 1.10; 95% CI, 1.08-1.11, per year), HbA1c (OR, 1.25; 95% CI, 1.18-1.32, per %), serum glucose (OR, 1.03; 95% CI, 1.00-1.06, per mmol/l), and systolic blood pressure (OR, 1.14; 95% CI, 1.09-1.19, per 10 mmHg). Diastolic blood pressure (OR, 0.74; 95% CI, 0.65-0.84, per 10 mmHg increase), total cholesterol (OR, 0.87; 95% CI, 0.80-0.95, per mmol/l increase), and low-density lipoprotein (LDL) cholesterol (OR, 0.83; 95% CI, 0.74-0.92, per mmol/l increase) were associated with lower odds of any DR. Risk factors were largely similar across the 3 ethnic groups. CONCLUSIONS: Indian Singaporeans have a higher prevalence of DR and DME compared with Chinese and Malays. Major risk factors for DR in this study were similar across the 3 ethnic groups. Addressing these risk factors may reduce the impact of DR in Asia, regardless of ethnicity.

Chronic Vascular Arrest as a Predictor of Bevacizumab Treatment Failure in Retinopathy of Prematurity.

PURPOSE: To describe a pattern of retinopathy of prematurity (ROP) disease regression and chronic vascular arrest after intravitreal bevacizumab treatment that is not observed after peripheral laser ablation. DESIGN: Single-institution retrospective cohort study. PARTICIPANTS: Consecutive sample of 58 eyes in 30 patients treated for type 1 ROP. METHODS: Initial treatment with either a single intravitreal injection of bevacizumab in off-label use (n = 33 eyes) or peripheral laser ablation (n = 25 eyes) as part of standard clinical care. There was bias in recommending off-label bevacizumab for smaller infants with type 1 ROP. MAIN OUTCOME AND MEASURES: Reactivation or persistence of ROP, as determined by clinical examination, fundus photography, and fluorescein angiography. RESULTS: All eyes treated initially with bevacizumab demonstrated irregular progression of the leading vascular edge in a stereotyped pattern, suggestive of scalloped regression. Recurrence, based on angiographic demonstration of leakage, or chronic vascular arrest, confirmed based on angiographic demonstration of peripheral ischemia, was noted in 30 eyes (91%) in the bevacizumab group, at a median interval of 14.9 weeks after injection (corrected gestational age, 49.3 weeks). Univariate logistic regression indicated that the need for rescue treatment was associated with decreased birth weight (odds ratio [OR], -0.007; P = 0.04) and age of initial treatment (OR, -0.35; P = 0.05), but not gender, race, or gestational age. Multivariate logistic regression indicated that only decreased birth weight (OR, -0.018; P = 0.04) was associated with need for rescue treatment. CONCLUSIONS: Treating ROP with intravitreal bevacizumab results in a characteristic scalloped regression pattern that is highly associated with treatment using biologic anti-vascular endothelial growth factor agents. The presence of this pattern in conjunction with chronic vascular arrest and peripheral retinal ischemia persisting beyond standard screening timelines has significant implications for the management of ROP. Fluorescein angiography is important in assessing vascular maturation in these infants.

Implementation of Artificial Intelligence in Retinopathy of Prematurity Care: Challenges and Opportunities.

The diagnosis of retinopathy of prematurity (ROP) is primarily image-based and suitable for implementation of artificial intelligence (AI) systems. Increasing incidence of ROP, especially in low and middle-income countries, has also put tremendous stress on health care systems. Barriers to the implementation of AI include infrastructure, regulatory, legal, cost, sustainability, and scalability. This review describes currently available AI and imaging systems, how a stable telemedicine infrastructure is crucial to AI implementation, and how successful ROP programs have been run in both low and middle-income countries and high-income countries. More work is needed in terms of validating AI systems with different populations with various low-cost imaging devices that have recently been developed. A sustainable and cost-effective ROP screening program is crucial in the prevention of childhood blindness.

Blood biomarkers improve the prediction of prevalent and incident severe chronic kidney disease.

BACKGROUND: The prevalence of chronic kidney disease (CKD) is high. Identification of cases with CKD or at high risk of developing it is important to tailor early interventions. The objective of this study was to identify blood metabolites associated with prevalent and incident severe CKD, and to quantify the corresponding improvement in CKD detection and prediction. METHODS: Data from four cohorts were analyzed: Singapore Epidemiology of Eye Diseases (SEED) (n = 8802), Copenhagen Chronic Kidney Disease (CPH) (n = 916), Singapore Diabetic Nephropathy (n = 714), and UK Biobank (UKBB) (n = 103,051). Prevalent CKD (stages 3-5) was defined as estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m2; incident severe CKD as CKD-related mortality or kidney failure occurring within 10 years. We used multivariable regressions to identify, among 146 blood metabolites, those associated with CKD, and quantify the corresponding increase in performance. RESULTS: Chronic kidney disease prevalence (stages 3-5) and severe incidence were 11.4% and 2.2% in SEED, and 2.3% and 0.2% in UKBB. Firstly, phenylalanine (Odds Ratio [OR] 1-SD increase = 1.83 [1.73, 1.93]), tyrosine (OR = 0.75 [0.71, 0.79]), docosahexaenoic acid (OR = 0.90 [0.85, 0.95]), citrate (OR = 1.41 [1.34, 1.47]) and triglycerides in medium high density lipoprotein (OR = 1.07 [1.02, 1.13]) were associated with prevalent stages 3-5 CKD. Mendelian randomization analyses suggested causal relationships. Adding these metabolites beyond traditional risk factors increased the area under the curve (AUC) by 3% and the sensitivity by 7%. Secondly, lactate (HR = 1.33 [1.08, 1.64]) and tyrosine (HR = 0.74 [0.58, 0.95]) were associated with incident severe CKD among individuals with eGFR < 90 mL/min/1.73 m2 at baseline. These metabolites increased the c-index by 2% and sensitivity by 5% when added to traditional risk factors. CONCLUSION: The performance improvements of CKD detection and prediction achieved by adding metabolites to traditional risk factors are modest and further research is necessary to fully understand the clinical implications of these findings.

Artificial Intelligence, Digital Imaging, and Robotics Technologies for Surgical Vitreoretinal Diseases.

OBJECTIVE: To review recent technological advancement in imaging, surgical visualization, robotics technology, and the use of artificial intelligence in surgical vitreoretinal (VR) diseases. BACKGROUND: Technological advancements in imaging enhance both preoperative and intraoperative management of surgical VR diseases. Widefield imaging in fundal photography and OCT can improve assessment of peripheral retinal disorders such as retinal detachments, degeneration, and tumors. OCT angiography provides a rapid and noninvasive imaging of the retinal and choroidal vasculature. Surgical visualization has also improved with intraoperative OCT providing a detailed real-time assessment of retinal layers to guide surgical decisions. Heads-up display and head-mounted display utilize 3-dimensional technology to provide surgeons with enhanced visual guidance and improved ergonomics during surgery. Intraocular robotics technology allows for greater surgical precision and is shown to be useful in retinal vein cannulation and subretinal drug delivery. In addition, deep learning techniques leverage on diverse data including widefield retinal photography and OCT for better predictive accuracy in classification, segmentation, and prognostication of many surgical VR diseases. CONCLUSION: This review article summarized the latest updates in these areas and highlights the importance of continuous innovation and improvement in technology within the field. These advancements have the potential to reshape management of surgical VR diseases in the very near future and to ultimately improve patient care. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Pull-through insertion of EndoArt for complex eyes.

Purpose: To report an alternative technique to implant the EndoArt using a pull-through insertion. This technique is helpful in complex eyes, especially in eyes with unstable iris lens diaphragm. Observation: We present a case of advanced pseudophakic bullous keratopathy with aniridia, previous vitrectomy, and tube implants in which the initial attempt to implant the EndoArt failed, and the device was lost to the vitreous cavity. An alternative surgical technique, a pull-through insertion, was used to implant a second device successfully. The patient was followed over a period of 1 year. Corneal edema gradually improved over time, and all epithelial bullae resolved. The central corneal thickness (CCT) decreased from 911um to 691 µm. Conclusion and Importance: EndoArt is a treatment for endothelial failure in complex eyes. In addition, the pull-through insertion technique can help improve control over the implant in very complicated eyes.

Optical Coherence Tomography and Optical Coherence Tomography Angiography in Pediatric Retinal Diseases.

Indirect ophthalmoscopy and handheld retinal imaging are the most common and traditional modalities for the evaluation and documentation of the pediatric fundus, especially for pre-verbal children. Optical coherence tomography (OCT) allows for in vivo visualization that resembles histology, and optical coherence tomography angiography (OCTA) allows for non-invasive depth-resolved imaging of the retinal vasculature. Both OCT and OCTA were extensively used and studied in adults, but not in children. The advent of prototype handheld OCT and OCTA have allowed for detailed imaging in younger infants and even neonates in the neonatal care intensive unit with retinopathy of prematurity (ROP). In this review, we discuss the use of OCTA and OCTA in various pediatric retinal diseases, including ROP, familial exudative vitreoretinopathy (FEVR), Coats disease and other less common diseases. For example, handheld portable OCT was shown to detect subclinical macular edema and incomplete foveal development in ROP, as well as subretinal exudation and fibrosis in Coats disease. Some challenges in the pediatric age group include the lack of a normative database and the difficulty in image registration for longitudinal comparison. We believe that technological improvements in the use of OCT and OCTA will improve our understanding and care of pediatric retina patients in the future.

Correlation of anterior segment optical coherence tomography measurements with graft trephine diameter following descemet stripping automated endothelial keratoplasty.

BACKGROUND: To assess repeatability of the Zhongshan Assessment Program (ZAP) software measurement of Anterior Segment Optical Coherence Tomography (ASOCT) images and correlate with graft trephine diameter following Descemet Stripping Automated Endothelial Keratoplasty (DSAEK) METHODS: Retrospectively evaluated interventional case series. 121 consecutive eyes undergoing DSAEK over a 26 month period underwent ASOCT imaging 1 month after their surgery. ASOCT images were processed using ZAP software which measured the graft and cornea parameters including anterior and posterior graft arc length and cord length, posterior cornea arc length (PCAL) and anterior chamber width. RESULTS: The graft measurements showed good repeatability on ASOCT using ZAP with high intra class coefficient and small variation in the coefficient of variation. On ASOCT, the mean recipient PCAL was 12.99+/-0.69 mm and the anterior chamber width was 11.16+/-0.57 mm. The mean Graft anterior arc length was 9.69+/-0.66 mm and the mean Graft anterior cord length was 8.92+/-2.94 mm. The mean graft posterior arc length was 9.24+/-0.75 mm and the mean graft posterior cord length was 8.15+/-0.57 mm. Graft posterior arc length (rho=0.788, p< 0.001) correlated best with intra-operative graft trephine diameter. The mean ratio of posterior graft arc length to PCAL was 0.712 +/- 0.056. CONCLUSIONS: We have validated the repeatability of the ZAP software for DSAEK graft measurements from ASOCT images and shown that the graft arc length parameters calculated from the ASOCT images correlate well with the intra-operative graft trephine diameter. This software may help surgeons determine the optimal DSAEK graft size based on pre-operative ASOCT measurements of the recipient eye.

Evaluating a Deep Learning Diabetic Retinopathy Grading System Developed on Mydriatic Retinal Images When Applied to Non-Mydriatic Community Screening.

Artificial Intelligence has showcased clear capabilities to automatically grade diabetic retinopathy (DR) on mydriatic retinal images captured by clinical experts on fixed table-top retinal cameras within hospital settings. However, in many low- and middle-income countries, screening for DR revolves around minimally trained field workers using handheld non-mydriatic cameras in community settings. This prospective study evaluated the diagnostic accuracy of a deep learning algorithm developed using mydriatic retinal images by the Singapore Eye Research Institute, commercially available as Zeiss VISUHEALTH-AI DR, on images captured by field workers on a Zeiss Visuscout® 100 non-mydriatic handheld camera from people with diabetes in a house-to-house cross-sectional study across 20 regions in India. A total of 20,489 patient eyes from 11,199 patients were used to evaluate algorithm performance in identifying referable DR, non-referable DR, and gradability. For each category, the algorithm achieved precision values of 29.60 (95% CI 27.40, 31.88), 92.56 (92.13, 92.97), and 58.58 (56.97, 60.19), recall values of 62.69 (59.17, 66.12), 85.65 (85.11, 86.18), and 65.06 (63.40, 66.69), and F-score values of 40.22 (38.25, 42.21), 88.97 (88.62, 89.31), and 61.65 (60.50, 62.80), respectively. Model performance reached 91.22 (90.79, 91.64) sensitivity and 65.06 (63.40, 66.69) specificity at detecting gradability and 72.08 (70.68, 73.46) sensitivity and 85.65 (85.11, 86.18) specificity for the detection of all referable eyes. Algorithm accuracy is dependent on the quality of acquired retinal images, and this is a major limiting step for its global implementation in community non-mydriatic DR screening using handheld cameras. This study highlights the need to develop and train deep learning-based screening tools in such conditions before implementation.

Detecting visually significant cataract using retinal photograph-based deep learning.

Age-related cataracts are the leading cause of visual impairment among older adults. Many significant cases remain undiagnosed or neglected in communities, due to limited availability or accessibility to cataract screening. In the present study, we report the development and validation of a retinal photograph-based, deep-learning algorithm for automated detection of visually significant cataracts, using more than 25,000 images from population-based studies. In the internal test set, the area under the receiver operating characteristic curve (AUROC) was 96.6%. External testing performed across three studies showed AUROCs of 91.6-96.5%. In a separate test set of 186 eyes, we further compared the algorithm's performance with 4 ophthalmologists' evaluations. The algorithm performed comparably, if not being slightly more superior (sensitivity of 93.3% versus 51.7-96.6% by ophthalmologists and specificity of 99.0% versus 90.7-97.9% by ophthalmologists). Our findings show the potential of a retinal photograph-based screening tool for visually significant cataracts among older adults, providing more appropriate referrals to tertiary eye centers.

Acceptance and Perception of Artificial Intelligence Usability in Eye Care (APPRAISE) for Ophthalmologists: A Multinational Perspective.

Background: Many 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. Methods: This 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. Results: One 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. Conclusion: Ophthalmologists 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.

Hints for Gentle Submacular Injection in Non-Human Primates Based on Intraoperative OCT Guidance.

Purpose: Delivery of Advanced Therapy Medicinal Products to the submacular space is increasingly evolving into a therapeutic modality. Cell replacement for age-related macular degeneration (AMD) and gene therapy for RPE65 are recent successful examples. Herein, a nonhuman primate (NHP) model was used to investigate surgical means to detach the macula. Methods: Sixteen eyes of 13 healthy macaques underwent a 25-gauge vitrectomy and subretinal injection of balanced salt solution monitored by microscope-integrated intraoperative optical coherence tomography (miOCT). The animals were followed with OCT and histology. Results: The miOCT monitoring allowed a more precise definition of surgical trauma ranging from an initial full-thickness foveal tear, or induction of a cystoid macular edema (CME), until no foveal defect was discernible, as the technique improved. However, as the subretinal fluid wave detached the fovea, the aforementioned lesions formed, whereas persistent retinal adhesion reproducibly proved to remain in the distal parafoveal semi-annulus. Measures to reduce foveal trauma during submacular fluid injection included reducing intraocular pressure, injection volume, and velocity, as well as the retinal location for bleb initiation, use of a vitreous tamponade, and a dual-bore subretinal cannula. Conclusions: A stable very low intraocular pressure and careful subretinal injection may avoid tangential macular stretching or mechanical CME formation, while vitreous tamponade may facilitate a more lamellar subretinal flow, all thereby reducing foveal trauma during submacular injection in NHP. Translational Relevance: These results can be relevant to any submacular surgery procedure used today, as they synergistically reduce the risk of compromising foveal integrity.

Digital health during COVID-19: lessons from operationalising new models of care in ophthalmology.

The COVID-19 pandemic has resulted in massive disruptions within health care, both directly as a result of the infectious disease outbreak, and indirectly because of public health measures to mitigate against transmission. This disruption has caused rapid dynamic fluctuations in demand, capacity, and even contextual aspects of health care. Therefore, the traditional face-to-face patient-physician care model has had to be re-examined in many countries, with digital technology and new models of care being rapidly deployed to meet the various challenges of the pandemic. This Viewpoint highlights new models in ophthalmology that have adapted to incorporate digital health solutions such as telehealth, artificial intelligence decision support for triaging and clinical care, and home monitoring. These models can be operationalised for different clinical applications based on the technology, clinical need, demand from patients, and manpower availability, ranging from out-of-hospital models including the hub-and-spoke pre-hospital model, to front-line models such as the inflow funnel model and monitoring models such as the so-called lighthouse model for provider-led monitoring. Lessons learnt from operationalising these models for ophthalmology in the context of COVID-19 are discussed, along with their relevance for other specialty domains.