Association of eGFR with stages of diabetic retinopathy and age-related macular degeneration in Indian population.

PURPOSE: To investigate the influence of glomerular filtration rate in renal disease decline and its association with diabetic retinopathy (DR) and age-related macular degeneration (ARMD) in patients in South India. METHODS: A population-based cross-sectional study was conducted including participants with DR and ARMD recruited from urban and rural populations. The data collection included medical history, anthropometric measurements, and ophthalmic work-up. The estimated glomerular filtration rate (eGFR) was calculated using the equation of chronic kidney disease-epidemiology collaboration (CKD-EPI). The grading of AMD was done by a single experienced (more than 5 years) vitreoretinal surgeon as per the International ARM Epidemiological Study Group and it was staged based on grading in the worsened eye. RESULTS: A decline in eGFR was observed as the severity of DR increased ( P < 0.001). Baseline characteristics such as age ( P < 0.001), duration of diabetes ( P < 0.001), gender ( P < 0.001), creatinine ( P < 0.001), albumin to creatinine ratio (ACR; P < 0.001), albuminuria ( P = 0.023), blood urea ( P < 0.001), and high-density lipoprotein (HDL; P = 0.003) were found to be statistically significant. The risk for developing DR with CKD was found to be 5 times higher in male patients compared to female patients. Age and high blood urea level, diastolic blood pressure, mild and moderate DR were the risk factors associated with CKD. A decline in eGFR was observed as the severity of ARMD increased ( P < 0.001). The risk factors associated with CKD were age, gender, smoking, alcohol consumed, presence of hypertension, duration of diabetes, systolic and diastolic blood pressure, history of diabetes, body mass index (BMI), serum triglycerides, and serum HDL cholesterol. CONCLUSION: Reduced eGFR values were associated with an increase in the severity of DR and ARMD.

Diagnostic Utility of Swept-Source OCT-Based Biometry and Fundus Photographs Compared to Spectral Domain OCT in Center-Involving Diabetic Macular Edema.

PURPOSE: This study was aimed to evaluate the agreement between the swept-source optical coherence tomography (SS-OCT)-based biometry, fundus photographs, and their combination, in comparison to the gold standard spectral-domain optical coherence tomography (SD-OCT) for the detection of center-involving diabetic macular edema (CI-DME). METHODS: We conducted a retrospective cross-sectional study involving 55 subjects (78 eyes) diagnosed with diabetic macular edema (DME) detected clinically and on SD-OCT (Carl Zeiss Meditec AG). Post-mydriatic 45-degree color fundus photograph (Crystal-Vue NFC-700), 1 mm macular scan obtained from SS-OCT-based biometry (IOL-Master 700), and macula cube scan obtained from SD-OCT was used to detect and grade DME into CI-DME and NCI-DME. RESULTS: Our findings revealed that SS-OCT-based biometry was noted to have a high sensitivity of 1 (0.94-1.00) and a specificity of 0.63 (0.31-0.89) in detecting CI-DME compared to the gold standard (SD-OCT). When combined with data from fundus photographs, specificity decreased to 0.32 (0.15-0.53). Fundus photographs alone exhibited a low sensitivity of 0.52 (0.38-0.64) and a specificity of 0.45 (0.16-0.76) in CI-DME detection. CONCLUSION: In conclusion, SS-OCT-based biometry can be used as an effective tool for the detection of CI-DME in diabetic patients undergoing cataract surgery and can serve as a screening tool in centers without SD-OCT facilities.

Diabetic Macular Edema (DME); Center Involving Diabetic Macular Edema (CI-DME); Non-Center Involving Diabetic Macular Edema (NCI-DME); Swept-Source Optical Coherence Tomography (SS-OCT); Spectral-Domain Optical Coherence Tomography (SD-OCT); Anti-Vascular Endothelial Growth Factor (Anti-VEGF); Central Retinal Thickness (CRT); Intra Retinal Fluid (IRF); Sub Retinal Fluid (SRF); Diabetic Retinopathy (DR); Non Proliferative Diabetic Retinopathy (NPDR); Proliferative Diabetic Retinopathy (PDR); Best Corrected Visual Acuity (BCVA); Glycosylated hemoglobin (HbA1c); Mean Spherical Error (MSE); Standard Deviation (SD); Positive Predictive value (PPV); Predictive value (PPV); Negative predictive value (NPV); Area under the Curve (AUC).

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. In resource-constrained health-care systems, these algorithms may play an important role in determining referral and treatment. The survey provides a diversified overview of macular edema detection methods, including cutting-edge research, with the objective of providing pertinent information to research groups, health-care professionals, and diabetic patients about the applications of deep learning in retinal image detection and classification process. Electronic databases such as PubMed, IEEE Explore, BioMed, and Google Scholar were searched from inception to March 31, 2022, and the reference lists of published papers were also searched. The study followed the preferred reporting items for systematic review and meta-analysis (PRISMA) reporting guidelines. Examination of various deep learning models and their exhibition regarding precision, epochs, their capacity to detect anomalies for less training data, concepts, and challenges that go deep into the applications were analyzed. A total of 53 studies were included that evaluated the performance of deep learning models in a total of 1,414,169°CT volumes, B-scans, patients, and 472,328 fundus images. The overall area under the receiver operating characteristic curve (AUROC) was 0.9727. The overall sensitivity for detecting DME using OCT images was 96% (95% confidence interval [CI]: 0.94-0.98). The overall sensitivity for detecting DME using fundus images was 94% (95% CI: 0.90-0.96).