# Assessment of Correlation Between Diabetic Retinopathy and Metabolic Biomarkers Using Artificial Intelligence

## TL;DR

This study explores how artificial intelligence can detect diabetic retinopathy and finds it correlates with certain metabolic biomarkers, suggesting ways to improve early detection and treatment.

## Contribution

The study provides new insights into the diagnostic accuracy of AI in DR detection and identifies key metabolic biomarkers associated with DR prevalence.

## Key findings

- AI using nonmydriatic cameras showed high sensitivity and specificity in detecting diabetic retinopathy.
- Significant associations were found between DR and HbA1c, CRP, urinary albumin, and diabetes duration.
- Triglyceride levels showed a possible but weaker association with DR prevalence.

## Abstract

One of the main causes of blindness in the world, diabetic retinopathy (DR) is a dangerous condition that impairs vision in diabetics. Preventing visual loss requires early recognition of DR and prompt treatments. Artificial intelligence (AI) software combined with nonmydriatic fundus cameras has demonstrated encouraging gains in DR screening effectiveness. However, there are not many studies that systematically compare the diagnostic effectiveness of various nonmydriatic cameras and AI software in the field of endocrinology, where managing diabetes and its complications is crucial. By offering vital information for enhancing diabetes care plans and fortifying preventative actions in the context of endocrine health, this study seeks to close this knowledge gap.

This clinical study was conducted at the Akdeniz University endocrinology clinic with 900 volunteer patients who had previously been diagnosed with diabetes but had undiagnosed DR. Fundus images of each patient were captured using three different nonmydriatic fundus cameras. These images were then assessed for varying degrees of DR, ranging from mild to more severe forms, including vtDR and clinically significant diabetic macular edema, utilizing EyeCheckup AI software. Additionally, patients underwent pupil dilation for wide‐angle fundus photography, resulting in four distinct wide‐angle images being taken. Three retina specialists evaluated these four wide‐field fundus images based on the DR treatment guidelines set forth by the American Academy of Ophthalmology. The effectiveness of the AI in detecting DR was determined through statistical analysis, comparing the diagnoses made by the physicians with those provided by the AI. Furthermore, patients filled out a questionnaire regarding their medical history and underwent a lipid panel blood test along with urine tests. These assessments included various metabolic measurements such as HbA1c levels, diabetes duration, low‐density lipoprotein (LDL) cholesterol, high‐density lipoprotein (HDL) cholesterol, triglycerides, urinary albumin levels, glomerular filtration rate (GFR), creatinine, and C‐reactive protein (CRP) levels.

Our study revealed a significant association between the prevalence of DR and diabetes duration, HbA1c, CRP, and urinary albumin levels. The p values of this association were 0.000, 0.000, 0.003, and 0.002, respectively. It was also noted that there may be an association between triglyceride levels and DR prevalence; the p value of this association was 0.079, with more data needed to establish a strong link. The study also revealed that AI performed satisfactorily in detecting DR from fundus images. The sensitivity and specificity of the different cameras used are as follows: Canon CR2 AF: 95.65% sensitivity, 95.92% specificity; Topcon TRC‐NW400: 95.19% sensitivity, 96.46% specificity; and Optomed Aurora: 90.48% sensitivity, 97.21% specificity.

Our research has shown a significant association between the prevalence of DR and elevated levels of HbA1c, CRP, and urinary albumin and duration of diabetes. This suggests that these biomarkers may serve as valuable predictive indicators in assessing the likelihood of DR. Consequently, the inclusion of these parameters in routine clinical assessments could improve proactive screening strategies, thus enabling early detection and intervention of DR. This, in turn, could reduce the risk of vision loss in affected patients. The study also demonstrates the potential of nonmydriatic fundus cameras used in combination with AI software to detect DR at an early stage.

ClinicalTrials.gov identifier: NCT04805541