Creating a retinal image database to develop an automated screening tool for diabetic retinopathy in India.

Diabetic retinopathy (DR), a prevalent microvascular complication of diabetes, is the fifth leading cause of blindness worldwide. Given the critical nature of the disease, it is paramount that individuals with diabetes undergo annual screening for early and timely detection of DR, facilitating prompt ophthalmic assessment and intervention. However, screening for DR, which involves assessing visual acuity and retinal examination through ophthalmoscopy or retinal photography, presents a significant global challenge due to the massive volume of individuals requiring annual reviews.

Leveraging Artificial Intelligence for Diabetic Retinopathy Screening and Management: History and Current Advances.

Aim: Regular screening of large number of people with diabetes for diabetic retinopathy (DR) with the support of available human resources alone is a global challenge. Digital health innovation is a boon in screening for DR. Multiple artificial intelligence (AI)-based deep learning (DL) algorithms have shown promise for accurate diagnosis of referable DR (RDR).

Assessing the appropriateness and completeness of ChatGPT-4's AI-generated responses for queries related to diabetic retinopathy.

Objective: To evaluate the appropriateness of responses generated by an online chat-based artificial intelligence (AI) model for diabetic retinopathy (DR) related questions.

Design: Cross-sectional study.

Methods: A set of 20 questions framed from the patient's perspective addressing DR-related queries, such as the definition of disease, symptoms, prevention methods, treatment options, diagnostic methods, visual impact, and complications, were formulated for input into ChatGPT-4.

Wide-field imaging with smartphone based fundus camera: grading of severity of diabetic retinopathy and locating peripheral lesions in diabetic retinopathy.

Aim: To assess the performance of smartphone based wide-field retinal imaging (WFI) versus ultra-wide-field imaging (UWFI) for assessment of sight-threatening diabetic retinopathy (STDR) as well as locating predominantly peripheral lesions (PPL) of DR.

Methods: Individuals with type 2 diabetes with varying grades of DR underwent nonmydriatic UWFI with Daytona Plus camera followed by mydriatic WFI with smartphone-based Vistaro camera at a tertiary care diabetes centre in South India in 2021-22. Grading of DR as well as identification of PPL (DR lesions beyond the posterior pole) in the retinal images of both cameras was performed by senior retina specialists.

The Utility of ChatGPT in Diabetic Retinopathy Risk Assessment: A Comparative Study with Clinical Diagnosis.

Purpose: To evaluate the ability of an artificial intelligence (AI) model, ChatGPT, in predicting the diabetic retinopathy (DR) risk.

Methods: This retrospective observational study utilized an anonymized dataset of 111 patients with diabetes who underwent a comprehensive eye examination along with clinical and biochemical assessments. Clinical and biochemical data along with and without central subfield thickness (CST) values of the macula from OCT were uploaded to ChatGPT-4, and the response from the ChatGPT was compared to the clinical DR diagnosis made by an ophthalmologist.

Population-Based Artificial Intelligence Assessment of Relationship Between the Risk Factors for Diabetic Retinopathy in Indian Population.

Purpose: Risk factors (RFs), like 'body mass index (BMI),' 'age,' and 'gender' correlate with Diabetic Retinopathy (DR) diagnosis and have been widely studied. This study examines how these three secondary RFs independently affect the predictive capacity of primary RFs.

Methods: The dataset consisted of four population-based studies on the prevalence of DR and associated RFs in India between 2001 and 2010.

Machine Learning-Based Diagnosis and Ranking of Risk Factors for Diabetic Retinopathy in Population-Based Studies from South India.

This paper discusses the importance of investigating DR using machine learning and a computational method to rank DR risk factors by importance using different machine learning models. The dataset was collected from four large population-based studies conducted in India between 2001 and 2010 on the prevalence of DR and its risk factors. We deployed different machine learning models on the dataset to rank the importance of the variables (risk factors).

Role of cystatin C in the detection of sight-threatening diabetic retinopathy in Asian Indians with type 2 diabetes.

Aim: To study the association between cystatin C and sight-threatening diabetic retinopathy (STDR) in Asian Indians with type 2 diabetes (T2DM).

Methods: In a cross-sectional study carried out at two tertiary centres in India in 2022, individuals with T2DM underwent clinical and ophthalmic assessments and estimation of serum cystatin C. Grading of DR was done by retina specialists.

Compliance with follow-up in patients with diabetic macular edema: Eye care center vs. diabetes care center.

Purpose: The study was conducted to compare the compliance to intravitreal injection treatment and follow-up in patients with center-involving diabetic macular edema (CI-DME) and treatment outcomes between a tertiary eye care facility and a tertiary diabetes care center.

Methods: A retrospective review was conducted on treatment naïve DME patients who had received intravitreal anti-vascular endothelial growth factor (anti-VEGF) injections in 2019. Participants were people with type 2 diabetes who were under regular care at the eye care center or the diabetes care center in Chennai.

Burden of undiagnosed and suboptimally controlled diabetes in selected regions of India: Results from the SMART India population-level diabetes screening study.

Aims: To estimate the prevalence of undiagnosed diabetes and suboptimally controlled diabetes and the associated risk factors by community screening in India.

Methods: In this multi-centre, cross-sectional study, house-to-house screening was conducted in people aged ≥40 years in urban and rural areas across 10 states and one union territory in India between November 2018 and March 2020. Participants underwent anthropometry, clinical and biochemical assessments.

Association of OCT and OCT angiography measures with the development and worsening of diabetic retinopathy in type 2 diabetes.

Objective: To assess if optical coherence tomography (OCT) and OCT angiography (OCTA) measures are associated with the development and worsening of diabetic retinopathy (DR) over four years.

Methods: 280 participants with type 2 diabetes underwent ultra-wide field fundus photography, OCT and OCTA. OCT-derived macular thickness measures, retinal nerve fibre layer and ganglion cell-inner plexiform layer thickness and OCTA-derived foveal avascular zone area, perimeter, circularity, vessel density (VD) and macular perfusion (MP) were examined in relation to the development and worsening of DR over four years.

Deep learning-based detection of diabetic macular edema using optical coherence tomography and fundus images: A meta-analysis.

Diabetic macular edema (DME) is an important cause of visual impairment in the working-age group. Deep learning methods have been developed to detect DME from two-dimensional retinal images and also from optical coherence tomography (OCT) images. The performances of these algorithms vary and often create doubt regarding their clinical utility.

Using deep learning to detect diabetic retinopathy on handheld non-mydriatic retinal images acquired by field workers in community settings.

Diabetic retinopathy (DR) at risk of vision loss (referable DR) needs to be identified by retinal screening and referred to an ophthalmologist. Existing automated algorithms have mostly been developed from images acquired with high cost mydriatic retinal cameras and cannot be applied in the settings used in most low- and middle-income countries. In this prospective multicentre study, we developed a deep learning system (DLS) that detects referable DR from retinal images acquired using handheld non-mydriatic fundus camera by non-technical field workers in 20 sites across India.

Prevalence of diabetic retinopathy in India stratified by known and undiagnosed diabetes, urban-rural locations, and socioeconomic indices: results from the SMART India population-based cross-sectional screening study.

Background: National and subnational estimates of the prevalence of diabetic retinopathy and vision-threatening diabetic retinopathy (VTDR) are needed to inform the stepwise implementation of systematic retinal screening for people with diabetes in India to decrease the rate of blindness. We aimed to assess these national and subnational estimates and to stratify the prevalence of diabetic retinopathy and VTDR on the basis of people with known versus undiagnosed diabetes, urban versus rural residence, and epidemiological transition level (ETL) and Socio-demographic Index (SDI) categories of states.

Methods: We did a multicentre cross-sectional screening study for diabetic retinopathy using a complex cluster sampling design in people aged 40 years or older in ten Indian states and one union territory between Dec 20, 2018, and March 20, 2020.

Silver nanoparticles synthesized by Euphorbia hirta exhibited antibacterial activity and induced apoptosis through downregulation of PI3Kγ mediated PI3K/Akt/mTOR/p70S6K in human lung adenocarcinoma A549 cells.

Plant extracts were successfully applied to synthesize nanoparticles, and expected such biological processes of effective for chemotherapeutic applications and safe for human use. Our study planned to evaluate the anticancer efficacy of silver nanoparticles (AgNPs) synthesized by Euphorbia hirta on human lung adenocarcinoma A549 cells. The E.

Development and validation of predictive risk models for sight threatening diabetic retinopathy in patients with type 2 diabetes to be applied as triage tools in resource limited settings.

Background: Delayed diagnosis and treatment of sight threatening diabetic retinopathy (STDR) is a common cause of visual impairment in people with Type 2 diabetes. Therefore, systematic regular retinal screening is recommended, but global coverage of such services is challenging. We aimed to develop and validate predictive models for STDR to identify 'at-risk' population for retinal screening.

Early retinal functional alteration in relation to diabetes duration in patients with type 2 diabetes without diabetic retinopathy.

To examine the retinal structure and function in relation to diabetes duration and glycemia in patients without diabetic retinopathy (DR). 85 adults with type 2 diabetes without DR or macular edema underwent dilated indirect ophthalmoscopy, optical coherence tomography (OCT), ultra-wide field fundus photography, multifocal electroretinography (mfERG) and HbA assessment. Patients were stratified as those with diabetes duration < 10 years and ≥ 10 years.

Correlating the patterns of diabetic macular edema, optical coherence tomography biomarkers and grade of diabetic retinopathy with stage of renal disease.

Purpose: To correlate optical coherence tomography (OCT)-based morphological patterns of diabetic macular edema (DME), biomarkers and grade of diabetic retinopathy (DR) in patients with various stages of chronic kidney disease (CKD) secondary to diabetes.

Design: Multicentric retrospective cross-sectional study was conducted at seven centers across India.

Methods: Data from medical records of patients with DME and CKD were entered in a common excel sheet across all seven centers.

Inter-observer agreement in grading severity of diabetic retinopathy in wide-field fundus photographs.

Objective: To examine the inter-observer agreement between two retina specialists in grading diabetic retinopathy (DR) severity in ultra-wide-field fundus photographs.

Methods: Two hundred and seventy patients with diabetes, who visited the vitreoretinal specialty at a tertiary eye care hospital, with or without DR underwent comprehensive ophthalmic examination, dilated retinal exam and Optos ultra-wide-field (UWF) retinal photography. Optos images were graded for DR severity based on the International Clinical Diabetic Retinopathy Disease Severity Scale by two retina specialists with same number of years of experience, masked to the clinical details of the participants.

Tele-Ophthalmology Versus Face-to-Face Retinal Consultation for Assessment of Diabetic Retinopathy in Diabetes Care Centers in India: A Multicenter Cross-Sectional Study.

To evaluate the effectiveness of tele-ophthalmology (TO) versus face-to-face screening for diabetic retinopathy (DR) in diabetes care centers (DCC) across India. This is an observational, multicenter, retrospective, cross-sectional study of DR screening in individuals with diabetes performed across 35 branches of a chain of DCC in 20 cities in India over 1 year. In 30 DCC, DR screening was performed by TO, where retinal images obtained using Fundus on Phone camera were uploaded through the telemedicine network for centralized DR grading by eight retina specialists.

Prevalence and risk factors for diabetic retinopathy in prediabetes in Asian Indians.

Aim: To assess the prevalence of diabetic retinopathy (DR) and associated risk factors in Asian Indians with prediabetes.

Methods: In a cross-sectional study conducted at two tertiary care diabetes centres in Chennai, India, clinical and biochemical assessment and nonmydriatic ultra-wide field fundus photography was performed in individuals with prediabetes (impaired fasting glucose [IFG] and/or impaired glucose tolerance [IGT]) based on oral glucose tolerance test (OGTT) and/or glycated hemoglobin (HbA1c) between 5.7% and 6.

Effects of Metabolic Surgery on Diabetic Kidney Disease and Diabetic Retinopathy Among Obese Asian Indians with Type 2 Diabetes.

Introduction: To evaluate the effect of metabolic surgery on microvascular changes associated with diabetic retinopathy (DR) and diabetic kidney disease (DKD) in obese Asian Indians with type 2 diabetes (T2DM), one year after metabolic surgery.

Methods: This is a follow up study in 21 obese Asian Indians with T2DM who underwent metabolic surgery (MS). Diabetic microvascular complications were assessed before and one-year post surgery using urinary albumin, protein creatinine ratio, eGFR, retinal colour photography and Optical coherence tomography (OCT).