Background and aims: Diabetic retinopathy (DR) is a microvascular complication of diabetes and represents the leading cause of blindness in working-age adults. The aim of this study was to investigate the risk factors for DR in patients with type 2 diabetes (T2DM) with and without diabetic nephropathy (DN). Methods: A total of 160 patients with T2DM were included in the study. Photodocumented retinopathy status was determined according to the EURODIAB protocol. Renal function was determined using creatinine-based estimated glomerular filtration rate (eGFR) and albumin-to-creatinine ratio (ACR). Binary univariate and multiple logistic regression analyses were performed to determine the main predictors of DR. Results: The prevalence of DR in this studied sample was 46.3%. No significant correlation was observed between DR and age, body mass index, serum lipids, and renal function. Binary logistic regression analysis (no DR/DR) showed that longer diabetes duration (p = 0.008), poor glycemic control (HbA1c) (p = 0.008), higher systolic blood pressure (p = 0.001), and diastolic blood pressure (p = 0.003) were the main predictors of DR in patients with T2DM. However, the influence of systolic blood pressure (AOR = 1.06, p = 0.004) and diastolic blood pressure (AOR = 1.12, p = 0.007) on DR development remained significant even after adjustment for diabetes duration and HbA1c. Conclusions: Our results suggest that systolic and diastolic blood pressure are independent risk factors for DR in patients with T2DM.

Our study aimed to assess the role of a hand-held fundus camera and artificial intelligence (AI)-based grading system in diabetic retinopathy (DR) screening and determine its diagnostic accuracy in detecting DR compared with clinical examination and a standard fundus camera. This cross-sectional instrument validation study, as a part of the International Diabetes Federation (IDF) Diabetic Retinopathy Screening Project, included 160 patients (320 eyes) with type 2 diabetes (T2DM). After the standard indirect slit-lamp fundoscopy, each patient first underwent fundus photography with a standard 45° camera VISUCAM Zeiss and then with a hand-held camera TANG (Shanghai Zhi Tang Health Technology Co., Ltd.). Two retina specialists independently graded the images taken with the standard camera, while the images taken with the hand-held camera were graded using the DeepDR system and an independent IDF ophthalmologist. The three screening methods did not differ in detecting moderate/severe nonproliferative and proliferative DR. The area under the curve, sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio, kappa (ĸ) agreement, diagnostic odds ratio, and diagnostic effectiveness for a hand-held camera compared to clinical examination were 0.921, 89.1%, 100%, 100%, 91.4%, infinity, 0.11, 0.86, 936.48, and 94.9%, while compared to the standard fundus camera were 0.883, 83.2%, 100%, 100%, 87.3%, infinity, 0.17, 0.78, 574.6, and 92.2%. The results of our study suggest that fundus photography with a hand-held camera and AI-based grading system is a short, simple, and accurate method for the screening and early detection of DR, comparable to clinical examination and fundus photography with a standard camera.

Background: Diabetic retinopathy (DR) is the most common cause of preventable blindness among working-age adults. This study aimed to evaluate the impact of the regularity of fundus examinations and risk factor control in patients with type 2 diabetes (T2DM) on the prevalence and severity of DR. Methods: One hundred and fifty-six T2DM patients were included in this cross-sectional study. Results: In this sample, the prevalence of DR was 46.2%. Patients with no DR mainly did not examine the fundus regularly, while most patients with mild/moderate nonproliferative DR (NPDR) underwent a fundus examination regularly. In 39.7% of patients, this was the first fundus examination due to diabetes, and 67% of them had sight-threatening DR (STDR). Diabetes duration (p = 0.007), poor glycemic control (HbA₁c) (p = 0.006), higher systolic blood pressure (SBP) (p < 0.001), and diastolic blood pressure (DBP) (p = 0.002) were the main predictors of DR. However, the impact of SBP (AOR 1.07, p = 0.003) and DBP (AOR 1.13, p = 0.005) on DR development remained significant even after adjustment for diabetes duration and HbA₁c. The DR prevalence was higher in patients with higher blood pressure (≥130/80 mmHg) than in those with target blood pressure (<130/80 mmHg) (p = 0.043). None of the patients with target blood pressure had STDR. The peaks in SBP and DBP were observed in T2DM with DR and the first fundus examination due to diabetes. Conclusions: In this T2DM sample, DR prevalence was very high and strongly related to blood pressure and a lack of regular fundus examinations. These results indicate the necessity of establishing systematic DR screening in routine diabetes care and targeting blood pressure levels according to T2DM guidelines.

Background/Objectives: We aimed to evaluate the diagnostic accuracy of the MONA.health artificial intelligence (AI) software (Version 1.0.0; MONA.health, Leuven, Belgium) and compare its advantages in screening for diabetic retinopathy (DR) and diabetic macular edema (DME) with standard fundus photography. Methods: This cross-sectional, real-life instrument validation study was conducted at the Vuk Vrhovac University Clinic in Zagreb during routine DR screening and included 296 patients (592 eyes) with diabetes. Following standard fundus photography using a 45° Zeiss VISUCAM NM/FA camera (Carl Zeiss Meditec AG, Jena, Germany), each patient also underwent imaging with an automated portable retinal camera (NFC-600, Crystalvue Ophthalmic Instruments, Taoyuan City, Taiwan). Two retina specialists independently graded images from the standard camera, while images from the NFC-600 were analyzed using the MONA.health AI software. Results: Among the 592 eyes, human grading identified 81 with any DR, including 17 with mild NPDR, 64 with referable DR (moderate/severe NPDR or PDR), and 13 with DME. The MONA.health AI software identified 65 eyes with referable DR and 19 with DME. For MONA DR screening compared to the standard fundus camera, the area under the curve, sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio, kappa agreement, diagnostic odds ratio, and diagnostic effectiveness were 99.74%, 100%, 99.81%, 99.33%, 100%, 528.00, 0.00, 0.99, infinity, and 99.85%, respectively. For MONA DME screening, these metrics were 97.97%, 100%, 98.95%, 85.93%, 100%, 95.67, 0.00, 0.81, infinity, and 99.02%, respectively. The MONA AI screening process required 1 day of training and approximately 5 min for image capture and analysis, compared to 7 days of training and 13 min for image acquisition and grading with the standard method. Conclusions: These findings demonstrate that the MONA.health AI software matches the accuracy of standard fundus photography for screening and early detection of referable DR and DME, while offering a faster, simpler, and more user-friendly workflow that significantly reduces the time to obtain screening results.

Background/Objectives: This study aimed to evaluate the association between the thickness of the macular ganglion cell-inner plexiform layer (GC-IPL), a marker of retinal neurodegeneration, and diabetic retinopathy (DR), a microvasculopathy, in type 2 diabetic patients (T2DM), and to determine the related risk factors. Methods: This cross-sectional study included 50 eyes of 25 T2DM with a median age of 64 and a median diabetes duration of 13 years. Complete diabetological, nephrological, and ophthalmological examination was performed, including color fundus photography according to the EURODIAB methodology and optical coherence tomography (OCT) of the macula. Patients with proliferative DR and diabetic macular edema were not included in the study. Data were analyzed using the software package Statistica™ 14.0.1.25 (TIBCO Inc., USA). Results: Fifty eyes were divided into two groups: no DR (n = 34) and non-proliferative DR (NPDR) (n = 16). The NPDR group had longer diabetes duration (p = 0.042), higher HbA₁c (p = 0.002), lower HDL cholesterol (p = 0.036), and also lower macular GC-IPL thickness (p = 0.027) than those without DR. The correlation between DR and GC-IPL was significantly negative (R = −0.319, p = 0.024). DR was positively related to diabetes duration (p = 0.047) and HbA₁c (p = 0.003), while the relation between GC-IPL and diabetes duration (p = 0.042) and HbA₁c (p = 0.043) was negative. Binary logistic regression analysis showed that HbA₁c (OR = 2.77, p = 0.007) and HDL cholesterol (OR = 0.08, p = 0.031) were the main predictors for DR, whereas the best model for predicting the GC-IPL thickness (R² = 0.223) obtained from stepwise regression analysis included HDL cholesterol, triglycerides, estimated glomerular filtration rate, and albumin/creatinine ratio. Conclusions: The negative correlation between macular GC-IPL and DR in T2DM indicates the coexistence of two parts, neurodegenerative and microvascular, in one diabetic eye complication, linked by the same well-known risk factors: diabetes duration and HbA₁c.