Relationships Between Retinal Vascular Characteristics and Systemic Indicators in Patients With Diabetes Mellitus

To develop a deep learning method for vessel segmentation in fundus images, measure retinal vessels, and study the connection between retinal vascular features and systemic indicators in diabetic patients.

We conducted a study on patients with diabetes mellitus (DM) at various stages of diabetic retinopathy (DR) using data from the Joint Asia Diabetes Evaluation (JADE) Register. All participants underwent comprehensive clinical assessments, including anthropometric measurements, laboratory tests, and fundus photography, during each follow-up visit (2.81 average follow-up visits). A custom U-Net deep learning model utilizing a variety of open-source datasets was developed for the segmentation and measurement of retinal vessels. We investigated the relationship between systemic indicators and the severity of DR, analyzing the correlation coefficients between systemic indicators and retinal vascular characteristics.

We enrolled a total of 637 patients diagnosed with DM and collected 3575 series of photographs for analysis. Some of the systemic indicators and retinal vascular metrics, including central retinal arteriolar equivalent, central retinal venular equivalent, arteriole-to-venule ratio, and fractal dimension, were significantly correlated with the severity of diabetic retinopathy (P < 0.05). Some physical characteristics, hematological parameters, renal function parameters, metabolism-related parameters, biochemical markers such as folic acid and fasting insulin, liver enzymes, and macrovascular indicators were significantly correlated with certain retinal vascular metrics (P < 0.05).

Multiple systemic indicators were identified as significantly associated with the advancement of diabetic retinopathy and retinal vascular metrics. Utilizing deep learning techniques for vessel segmentation and measurement on color fundus photographs can help elucidate the connections between retinal vascular characteristics and systemic indicators.