Comparison of the Quality of Discharge Letters Written by Large Language Models and Junior Clinicians: Single-Blinded Study.

BACKGROUND: Discharge letters are a critical component in the continuity of care between specialists and primary care providers. However, these letters are time-consuming to write, underprioritized in comparison to direct clinical care, and are often tasked to junior doctors. Prior studies assessing the quality of discharge summaries written for inpatient hospital admissions show inadequacies in many domains. Large language models such as GPT have the ability to summarize large volumes of unstructured free text such as electronic medical records and have the potential to automate such tasks, providing time savings and consistency in quality. OBJECTIVE: The aim of this study was to assess the performance of GPT-4 in generating discharge letters written from urology specialist outpatient clinics to primary care providers and to compare their quality against letters written by junior clinicians. METHODS: Fictional electronic records were written by physicians simulating 5 common urology outpatient cases with long-term follow-up. Records comprised simulated consultation notes, referral letters and replies, and relevant discharge summaries from inpatient admissions. GPT-4 was tasked to write discharge letters for these cases with a specified target audience of primary care providers who would be continuing the patient's care. Prompts were written for safety, content, and style. Concurrently, junior clinicians were provided with the same case records and instructional prompts. GPT-4 output was assessed for instances of hallucination. A blinded panel of primary care physicians then evaluated the letters using a standardized questionnaire tool. RESULTS: GPT-4 outperformed human counterparts in information provision (mean 4.32, SD 0.95 vs 3.70, SD 1.27; P=.03) and had no instances of hallucination. There were no statistically significant differences in the mean clarity (4.16, SD 0.95 vs 3.68, SD 1.24; P=.12), collegiality (4.36, SD 1.00 vs 3.84, SD 1.22; P=.05), conciseness (3.60, SD 1.12 vs 3.64, SD 1.27; P=.71), follow-up recommendations (4.16, SD 1.03 vs 3.72, SD 1.13; P=.08), and overall satisfaction (3.96, SD 1.14 vs 3.62, SD 1.34; P=.36) between the letters generated by GPT-4 and humans, respectively. CONCLUSIONS: Discharge letters written by GPT-4 had equivalent quality to those written by junior clinicians, without any hallucinations. This study provides a proof of concept that large language models can be useful and safe tools in clinical documentation.

Gaps and future of human-centered artificial intelligence in ophthalmology: Future Vision Forum consensus statement.

PURPOSE OF REVIEW: The Future Vision Forum discussed the current state of Human Centered Computing and the future of data collection, curation, and collation in ophthalmology. Although the uptake of electronic health record (EHR) systems and the digitization of healthcare data is encouraging, there are still barriers to implementing a specialty-wide clinical trial database. The article identifies several critical opportunities, including the need for standardization of image metadata and data, the establishment of a centralized trial database, incentives for clinicians and trial sponsors to participate, and resolving ethical concerns surrounding data ownership. FINDINGS: Recommendations to overcome these challenges include the standardization of image metadata using the Digital Imaging and Communications in Medicine (DICOM) guidelines, the establishment of a centralized trial database that uses federated learning (FL), and the use of FL to facilitate cross-institutional collaboration for rare diseases. Forum faculty suggests incentives will accelerate artificial intelligence, digital innovation projects, and data sharing agreements to empower patients to release their data. SUMMARY: A specialty-wide clinical trial database could provide invaluable insights into the natural history of disease, pathophysiology, why trials fail, and improve future clinical trial design. However, overcoming the barriers to implementation will require continued discussion, collaboration, and collective action from stakeholders across the ophthalmology community.

Explainable artificial intelligence in ophthalmology.

PURPOSE OF REVIEW: Despite the growing scope of artificial intelligence (AI) and deep learning (DL) applications in the field of ophthalmology, most have yet to reach clinical adoption. Beyond model performance metrics, there has been an increasing emphasis on the need for explainability of proposed DL models. RECENT FINDINGS: Several explainable AI (XAI) methods have been proposed, and increasingly applied in ophthalmological DL applications, predominantly in medical imaging analysis tasks. SUMMARY: We summarize an overview of the key concepts, and categorize some examples of commonly employed XAI methods. Specific to ophthalmology, we explore XAI from a clinical perspective, in enhancing end-user trust, assisting clinical management, and uncovering new insights. We finally discuss its limitations and future directions to strengthen XAI for application to clinical practice.

Democratizing Artificial Intelligence Imaging Analysis With Automated Machine Learning: Tutorial.

Deep learning-based clinical imaging analysis underlies diagnostic artificial intelligence (AI) models, which can match or even exceed the performance of clinical experts, having the potential to revolutionize clinical practice. A wide variety of automated machine learning (autoML) platforms lower the technical barrier to entry to deep learning, extending AI capabilities to clinicians with limited technical expertise, and even autonomous foundation models such as multimodal large language models. Here, we provide a technical overview of autoML with descriptions of how autoML may be applied in education, research, and clinical practice. Each stage of the process of conducting an autoML project is outlined, with an emphasis on ethical and technical best practices. Specifically, data acquisition, data partitioning, model training, model validation, analysis, and model deployment are considered. The strengths and limitations of available code-free, code-minimal, and code-intensive autoML platforms are considered. AutoML has great potential to democratize AI in medicine, improving AI literacy by enabling "hands-on" education. AutoML may serve as a useful adjunct in research by facilitating rapid testing and benchmarking before significant computational resources are committed. AutoML may also be applied in clinical contexts, provided regulatory requirements are met. The abstraction by autoML of arduous aspects of AI engineering promotes prioritization of data set curation, supporting the transition from conventional model-driven approaches to data-centric development. To fulfill its potential, clinicians must be educated on how to apply these technologies ethically, rigorously, and effectively; this tutorial represents a comprehensive summary of relevant considerations.

Global Prevalence of Diabetic Retinopathy and Projection of Burden through 2045: Systematic Review and Meta-analysis.

TOPIC: To provide updated estimates on the global prevalence and number of people with diabetic retinopathy (DR) through 2045. CLINICAL RELEVANCE: The International Diabetes Federation (IDF) estimated the global population with diabetes mellitus (DM) to be 463 million in 2019 and 700 million in 2045. Diabetic retinopathy remains a common complication of DM and a leading cause of preventable blindness in the adult working population. METHODS: We conducted a systematic review using PubMed, Medline, Web of Science, and Scopus for population-based studies published up to March 2020. Random effect meta-analysis with logit transformation was performed to estimate global and regional prevalence of DR, vision-threatening DR (VTDR), and clinically significant macular edema (CSME). Projections of DR, VTDR, and CSME burden were based on population data from the IDF Atlas 2019. RESULTS: We included 59 population-based studies. Among individuals with diabetes, global prevalence was 22.27% (95% confidence interval [CI], 19.73%-25.03%) for DR, 6.17% (95% CI, 5.43%-6.98%) for VTDR, and 4.07% (95% CI, 3.42%-4.82%) for CSME. In 2020, the number of adults worldwide with DR, VTDR, and CSME was estimated to be 103.12 million, 28.54 million, and 18.83 million, respectively; by 2045, the numbers are projected to increase to 160.50 million, 44.82 million, and 28.61 million, respectively. Diabetic retinopathy prevalence was highest in Africa (35.90%) and North American and the Caribbean (33.30%) and was lowest in South and Central America (13.37%). In meta-regression models adjusting for habitation type, response rate, study year, and DR diagnostic method, Hispanics (odds ratio [OR], 2.92; 95% CI, 1.22-6.98) and Middle Easterners (OR, 2.44; 95% CI, 1.51-3.94) with diabetes were more likely to have DR compared with Asians. DISCUSSION: The global DR burden is expected to remain high through 2045, disproportionately affecting countries in the Middle East and North Africa and the Western Pacific. These updated estimates may guide DR screening, treatment, and public health care strategies.

Updates in deep learning research in ophthalmology.

Ophthalmology has been one of the early adopters of artificial intelligence (AI) within the medical field. Deep learning (DL), in particular, has garnered significant attention due to the availability of large amounts of data and digitized ocular images. Currently, AI in Ophthalmology is mainly focused on improving disease classification and supporting decision-making when treating ophthalmic diseases such as diabetic retinopathy, age-related macular degeneration (AMD), glaucoma and retinopathy of prematurity (ROP). However, most of the DL systems (DLSs) developed thus far remain in the research stage and only a handful are able to achieve clinical translation. This phenomenon is due to a combination of factors including concerns over security and privacy, poor generalizability, trust and explainability issues, unfavorable end-user perceptions and uncertain economic value. Overcoming this challenge would require a combination approach. Firstly, emerging techniques such as federated learning (FL), generative adversarial networks (GANs), autonomous AI and blockchain will be playing an increasingly critical role to enhance privacy, collaboration and DLS performance. Next, compliance to reporting and regulatory guidelines, such as CONSORT-AI and STARD-AI, will be required to in order to improve transparency, minimize abuse and ensure reproducibility. Thirdly, frameworks will be required to obtain patient consent, perform ethical assessment and evaluate end-user perception. Lastly, proper health economic assessment (HEA) must be performed to provide financial visibility during the early phases of DLS development. This is necessary to manage resources prudently and guide the development of DLS.

Deep learning-based natural language processing in ophthalmology: applications, challenges and future directions.

PURPOSE OF REVIEW: Artificial intelligence (AI) is the fourth industrial revolution in mankind's history. Natural language processing (NLP) is a type of AI that transforms human language, to one that computers can interpret and process. NLP is still in the formative stages of development in healthcare, with promising applications and potential challenges in its applications. This review provides an overview of AI-based NLP, its applications in healthcare and ophthalmology, next-generation use case, as well as potential challenges in deployment. RECENT FINDINGS: The integration of AI-based NLP systems into existing clinical care shows considerable promise in disease screening, risk stratification, and treatment monitoring, amongst others. Stakeholder collaboration, greater public acceptance, and advancing technologies will continue to shape the NLP landscape in healthcare and ophthalmology. SUMMARY: Healthcare has always endeavored to be patient centric and personalized. For AI-based NLP systems to become an eventual reality in larger-scale applications, it is pertinent for key stakeholders to collaborate and address potential challenges in application. Ultimately, these would enable more equitable and generalizable use of NLP systems for the betterment of healthcare and society.

Artificial intelligence in myopia: current and future trends.

PURPOSE OF REVIEW: Myopia is one of the leading causes of visual impairment, with a projected increase in prevalence globally. One potential approach to address myopia and its complications is early detection and treatment. However, current healthcare systems may not be able to cope with the growing burden. Digital technological solutions such as artificial intelligence (AI) have emerged as a potential adjunct for myopia management. RECENT FINDINGS: There are currently four significant domains of AI in myopia, including machine learning (ML), deep learning (DL), genetics and natural language processing (NLP). ML has been demonstrated to be a useful adjunctive for myopia prediction and biometry for cataract surgery in highly myopic individuals. DL techniques, particularly convoluted neural networks, have been applied to various image-related diagnostic and predictive solutions. Applications of AI in genomics and NLP appear to be at a nascent stage. SUMMARY: Current AI research is mainly focused on disease classification and prediction in myopia. Through greater collaborative research, we envision AI will play an increasingly critical role in big data analysis by aggregating a greater variety of parameters including genomics and environmental factors. This may enable the development of generalizable adjunctive DL systems that could help realize predictive and individualized precision medicine for myopic patients.

Generative adversarial networks in ophthalmology: what are these and how can they be used?

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.

Coronavirus disease 2019 (COVID-19): an evidence map of medical literature.

BACKGROUND: Since the beginning of the COVID-19 outbreak in December 2019, a substantial body of COVID-19 medical literature has been generated. As of June 2020, gaps and longitudinal trends in the COVID-19 medical literature remain unidentified, despite potential benefits for research prioritisation and policy setting in both the COVID-19 pandemic and future large-scale public health crises. METHODS: In this paper, we searched PubMed and Embase for medical literature on COVID-19 between 1 January and 24 March 2020. We characterised the growth of the early COVID-19 medical literature using evidence maps and bibliometric analyses to elicit cross-sectional and longitudinal trends and systematically identify gaps. RESULTS: The early COVID-19 medical literature originated primarily from Asia and focused mainly on clinical features and diagnosis of the disease. Many areas of potential research remain underexplored, such as mental health, the use of novel technologies and artificial intelligence, pathophysiology of COVID-19 within different body systems, and indirect effects of COVID-19 on the care of non-COVID-19 patients. Few articles involved research collaboration at the international level (24.7%). The median submission-to-publication duration was 8 days (interquartile range: 4-16). CONCLUSIONS: Although in its early phase, COVID-19 research has generated a large volume of publications. However, there are still knowledge gaps yet to be filled and areas for improvement for the global research community. Our analysis of early COVID-19 research may be valuable in informing research prioritisation and policy planning both in the current COVID-19 pandemic and similar global health crises.

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.

Retinal vascular geometry and 6 year incidence and progression of diabetic retinopathy.

AIMS/HYPOTHESIS: We aimed to examine prospectively the association between a range of retinal vascular geometric variables measured from retinal photographs and the 6 year incidence and progression of diabetic retinopathy. METHODS: We conducted a prospective, population-based cohort study of Asian Malay individuals aged 40-80 years at baseline (n = 3280) who returned for a 6 year follow-up. Retinal vascular geometric variables (tortuosity, branching, fractal dimension, calibre) were measured from baseline retinal photographs using a computer-assisted program (Singapore I Vessel Assessment). Diabetic retinopathy was graded from baseline and follow-up photographs using the modified Airlie House classification system. Incidence of diabetic retinopathy was defined as a severity of ≥15 at follow-up among those without diabetic retinopathy at baseline. Incidence of referable diabetic retinopathy was defined as moderate or severe non-proliferative diabetic retinopathy, proliferative diabetic retinopathy or diabetic macular oedema at follow-up in participants who had had no or mild non-proliferative diabetic retinopathy at baseline. Progression of diabetic retinopathy was defined as an increase in severity of ≥2 steps at follow-up. Log-binomial models with an expectation-maximisation algorithm were used to estimate RR adjusting for age, sex, diabetes duration, HbA1c level, BP, BMI, estimated GFR and total and HDL-cholesterol at baseline. RESULTS: A total of 427 individuals with diabetes participated in the baseline and 6 year follow-up examinations. Of these, 19.2%, 7.57% and 19.2% developed incidence of diabetic retinopathy, incidence of referable diabetic retinopathy and diabetic retinopathy progression, respectively. After multivariate adjustment, greater arteriolar simple tortuosity (mean RR [95% CI], 1.34 [1.04, 1.74]), larger venular branching angle (RR 1.26 [1.00, 1.59]) and larger venular branching coefficient (RR 1.26 [1.03, 1.56]) were associated with incidence of diabetic retinopathy. Greater arteriolar simple tortuosity (RR 1.82 [1.32, 2.52]), larger venular branching coefficient (RR 1.46 [1.03, 2.07]), higher arteriolar fractal dimension (RR 1.59 [1.08, 2.36]) and larger arteriolar calibre (RR 1.83 [1.15, 2.90]) were associated with incidence of referable diabetic retinopathy. Greater arteriolar simple tortuosity (RR 1.34 [1.12, 1.61]) was associated with diabetic retinopathy progression. Addition of retinal vascular variables improved discrimination (C-statistic 0.796 vs 0.733, p = 0.031) and overall reclassification (net reclassification improvement 18.8%, p = 0.025) of any diabetic retinopathy risk beyond established risk factors. CONCLUSIONS/INTERPRETATION: Retinal vascular geometry measured from fundus photographs predicted the incidence and progression of diabetic retinopathy in adults with diabetes, beyond established risk factors.

CHOROIDAL VASCULARITY INDEX: A Novel Optical Coherence Tomography Based Parameter in Patients With Exudative Age-Related Macular Degeneration.

PURPOSE: To evaluate choroidal structural changes in exudative age-related macular degeneration (AMD) using choroidal vascularity index computed from image binarization on spectral domain optical coherence tomography with enhanced depth imaging. METHODS: This prospective case series included 42 consecutive patients with unilateral exudative AMD. Choroidal images were segmented into luminal area and stromal area. Choroidal vascularity index was defined as the ratio of luminal area to total choroid area. Mean choroidal vascularity index and mean choroidal thickness between study and fellow eyes of the same patient with dry AMD were compared using Student's t-test. RESULTS: There was a significantly lower choroidal vascularity index in eyes with exudative AMD (60.14 ± 4.55 vs. 62.75 ± 4.82, P < 0.01). Luminal area (P < 0.01) was decreased in eyes with exudative AMD but there was no significant difference in total choroid area (P = 0.05) and choroidal thickness (P = 0.93) between study and fellow eyes. CONCLUSION: Eyes with exudative AMD demonstrated reduced choroidal vascularity index but insignificant differences in choroidal thickness compared with their fellow eyes. Choroidal vascularity index may be a potential noninvasive tool for studying structural changes in choroid and monitoring choroidal disease in exudative AMD.

DETAILED CHARACTERIZATION OF CHOROIDAL MORPHOLOGIC AND VASCULAR FEATURES IN AGE-RELATED MACULAR DEGENERATION AND POLYPOIDAL CHOROIDAL VASCULOPATHY.

PURPOSE: To characterize and compare morphologic and vascular features of the choroid in patients with typical age-related macular degeneration (AMD) and polypoidal choroidal vasculopathy (PCV) and to determine if PCV subtypes can be identified based on these choroidal features. METHODS: Choroidal features of patients with AMD and PCV recruited from the prospectively planned Asian AMD Phenotyping Study were analyzed. Patients underwent choroidal imaging using spectral-domain optical coherence tomography with enhanced depth imaging. Raw optical coherence tomographic images were loaded on a custom-written application on MATLAB that enabled delineation for detailed morphologic and vascular analyses, including the curvature of the choroid-sclera interface, number of inflection points, choroidal thickness and choroidal vascular area within the macular (6 mm centered on fovea) and foveal (1.5 mm centered on fovea) regions. An inflection point represents the contour of the choroid-sclera interface, with >1 point signaling irregular shape. RESULTS: A total of 156 eyes of 156 patients (78 affected eyes of 78 patients with typical AMD and 78 affected eyes of 78 patients with PCV) were analyzed. Eyes with PCV had thicker baseline choroidal thickness and greater choroidal vascular area compared with those with typical AMD (P < 0.05); these differences were no longer significant after adjusting for age and hypertension (P > 0.05). Typical PCV subtype with choroidal thickness of ≥257 µm had significantly greater choroidal vascular area at macular (mean difference = 0.054 mm; P < 0.001) and foveal (mean difference = 0.199 mm; P < 0.001) regions compared with eyes with typical AMD. However, eyes with PCV without thick choroid had similar choroidal vascular area as eyes with typical AMD. CONCLUSION: Based on the choroidal vascular features, two subtypes of PCV can be classified: typical PCV with increased choroid vascularity and polypoidal choroidal neovascularization with low choroidal vascularity. These data provide further understanding of different AMD and PCV subtypes.

Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes.

Importance: A deep learning system (DLS) is a machine learning technology with potential for screening diabetic retinopathy and related eye diseases. Objective: To evaluate the performance of a DLS in detecting referable diabetic retinopathy, vision-threatening diabetic retinopathy, possible glaucoma, and age-related macular degeneration (AMD) in community and clinic-based multiethnic populations with diabetes. Design, Setting, and Participants: Diagnostic performance of a DLS for diabetic retinopathy and related eye diseases was evaluated using 494 661 retinal images. A DLS was trained for detecting diabetic retinopathy (using 76 370 images), possible glaucoma (125 189 images), and AMD (72 610 images), and performance of DLS was evaluated for detecting diabetic retinopathy (using 112 648 images), possible glaucoma (71 896 images), and AMD (35 948 images). Training of the DLS was completed in May 2016, and validation of the DLS was completed in May 2017 for detection of referable diabetic retinopathy (moderate nonproliferative diabetic retinopathy or worse) and vision-threatening diabetic retinopathy (severe nonproliferative diabetic retinopathy or worse) using a primary validation data set in the Singapore National Diabetic Retinopathy Screening Program and 10 multiethnic cohorts with diabetes. Exposures: Use of a deep learning system. Main Outcomes and Measures: Area under the receiver operating characteristic curve (AUC) and sensitivity and specificity of the DLS with professional graders (retinal specialists, general ophthalmologists, trained graders, or optometrists) as the reference standard. Results: In the primary validation dataset (n = 14 880 patients; 71 896 images; mean [SD] age, 60.2 [2.2] years; 54.6% men), the prevalence of referable diabetic retinopathy was 3.0%; vision-threatening diabetic retinopathy, 0.6%; possible glaucoma, 0.1%; and AMD, 2.5%. The AUC of the DLS for referable diabetic retinopathy was 0.936 (95% CI, 0.925-0.943), sensitivity was 90.5% (95% CI, 87.3%-93.0%), and specificity was 91.6% (95% CI, 91.0%-92.2%). For vision-threatening diabetic retinopathy, AUC was 0.958 (95% CI, 0.956-0.961), sensitivity was 100% (95% CI, 94.1%-100.0%), and specificity was 91.1% (95% CI, 90.7%-91.4%). For possible glaucoma, AUC was 0.942 (95% CI, 0.929-0.954), sensitivity was 96.4% (95% CI, 81.7%-99.9%), and specificity was 87.2% (95% CI, 86.8%-87.5%). For AMD, AUC was 0.931 (95% CI, 0.928-0.935), sensitivity was 93.2% (95% CI, 91.1%-99.8%), and specificity was 88.7% (95% CI, 88.3%-89.0%). For referable diabetic retinopathy in the 10 additional datasets, AUC range was 0.889 to 0.983 (n = 40 752 images). Conclusions and Relevance: In this evaluation of retinal images from multiethnic cohorts of patients with diabetes, the DLS had high sensitivity and specificity for identifying diabetic retinopathy and related eye diseases. Further research is necessary to evaluate the applicability of the DLS in health care settings and the utility of the DLS to improve vision outcomes.

Cost-effectiveness of a National Telemedicine Diabetic Retinopathy Screening Program in Singapore.

PURPOSE: To determine the incremental cost-effectiveness of a new telemedicine technician-based assessment relative to an existing model of family physician (FP)-based assessment of diabetic retinopathy (DR) in Singapore from the health system and societal perspectives. DESIGN: Model-based, cost-effectiveness analysis of the Singapore Integrated Diabetic Retinopathy Program (SiDRP). PARTICIPANTS: A hypothetical cohort of patients aged 55 years with type 2 diabetes previously not screened for DR. METHODS: The SiDRP is a new telemedicine-based DR screening program using trained technicians to assess retinal photographs. We compared the cost-effectiveness of SiDRP with the existing model in which FPs assess photographs. We developed a hybrid decision tree/Markov model to simulate the costs, effectiveness, and incremental cost-effectiveness ratio (ICER) of SiDRP relative to FP-based DR screening over a lifetime horizon. We estimated the costs from the health system and societal perspectives. Effectiveness was measured in terms of quality-adjusted life-years (QALYs). Result robustness was calculated using deterministic and probabilistic sensitivity analyses. MAIN OUTCOME MEASURES: The ICER. RESULTS: From the societal perspective that takes into account all costs and effects, the telemedicine-based DR screening model had significantly lower costs (total cost savings of S$173 per person) while generating similar QALYs compared with the physician-based model (i.e., 13.1 QALYs). From the health system perspective that includes only direct medical costs, the cost savings are S$144 per person. By extrapolating these data to approximately 170 000 patients with diabetes currently being screened yearly for DR in Singapore's primary care polyclinics, the present value of future cost savings associated with the telemedicine-based model is estimated to be S$29.4 million over a lifetime horizon. CONCLUSIONS: While generating similar health outcomes, the telemedicine-based DR screening using technicians in the primary care setting saves costs for Singapore compared with the FP model. Our data provide a strong economic rationale to expand the telemedicine-based DR screening program in Singapore and elsewhere.

Biomarkers of Diabetic Retinopathy.

Diabetic retinopathy (DR), a leading cause of acquired vision loss, is a microvascular complication of diabetes. While traditional risk factors for diabetic retinopathy including longer duration of diabetes, poor blood glucose control, and dyslipidemia are helpful in stratifying patient's risk for developing retinopathy, many patients without these traditional risk factors develop DR; furthermore, there are persons with long diabetes duration who do not develop DR. Thus, identifying biomarkers to predict DR or to determine therapeutic response is important. A biomarker can be defined as a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention. Incorporation of biomarkers into risk stratification of persons with diabetes would likely aid in early diagnosis and guide treatment methods for those with DR or with worsening DR. Systemic biomarkers of DR include serum measures including genomic, proteomic, and metabolomics biomarkers. Ocular biomarkers including tears and vitreous and retinal vascular structural changes have also been studied extensively to prognosticate the risk of DR development. The current studies on biomarkers are limited by the need for larger sample sizes, cross-validation in different populations and ethnic groups, and time-efficient and cost-effective analytical techniques. Future research is important to explore novel DR biomarkers that are non-invasive, rapid, economical, and accurate to help reduce the incidence and progression of DR in people with diabetes.

Diabetic retinopathy: global prevalence, major risk factors, screening practices and public health challenges: a review.

Diabetes retinopathy (DR) is a leading cause of vision loss in middle-aged and elderly people globally. Early detection and prompt treatment allow prevention of diabetes-related visual impairment. Patients with diabetes require regular follow-up with primary care physicians to optimize their glycaemic, blood pressure and lipid control to prevent development and progression of DR and other diabetes-related complications. Other risk factors of DR include higher body mass index, puberty and pregnancy, and cataract surgery. There are weaker associations with some genetic and inflammatory markers. With the rising incidence and prevalence of diabetes and DR, public health systems in both developing and developed countries will be faced with increasing costs of implementation and maintenance of a DR screening program for people with diabetes. To reduce the impact of DR-related visual loss, it is important that all stakeholders continue to look for innovative ways of managing and preventing diabetes, and optimize cost-effective screening programs within the community.