Standardized evaluation of diabetic retinopathy using artificial intelligence and its association with metabolic dysfunction-associated steatotic liver disease in Japan: A cross-sectional study

Metabolic dysfunction-associated steatotic liver disease (MASLD) is common in patients with obesity and diabetes and can lead to serious complications. This study aimed to evaluate fundus photographs using artificial intelligence to explore the relationships between diabetic retinopathy (DR), MASLD, and related factors. In this cross-sectional study, we included 1,736 patients with a history of diabetes treatment or glycated hemoglobin (HbA1c) levels of >= 6.5%. All participants were negative for hepatitis B surface antigen and hepatitis C virus antibody and were selected from 33,022 examinees at a health facility in Japan. Fundus photographs were analyzed using RetCAD software, and DR scores were quantified. The presence of DR was determined using two cutoffs: sensitivity (CO20) and specificity (CO50). Steatotic liver (SL) stages were assessed via ultrasound and fibrosis indices and classified into three groups: no SL (SL0), SL with low fibrosis (SL1), and SL with high fibrosis (SL2). Odds ratios (ORs) for the risk of DR associated with each SL stage were calculated using logistic regression, adjusted for age, sex, body mass index, HbA1c, C-reactive protein level, and alcohol consumption. The risk of DR was lower in the SL1 (OR: 0.63, 0.54) and SL2 (OR: 0.64, 0.77) groups compared to the SL0 group at CO20 for both the Fibrosis-4 Index (FIB-4) and the non-alcoholic fatty liver disease fibrosis score (NFS), respectively. Additionally, higher levels of cholinesterase were consistently associated with a reduced risk of DR (FIB-4: OR 0.52, NFS: OR 0.54) at CO50. This study demonstrates that MASLD was associated with a reduced risk of DR, with cholinesterase levels providing further protective effects, highlighting the need for further research into the protective mechanisms and refinement of DR evaluation techniques. The standardized AI evaluation method for DR offers a reliable approach for analyzing retinal changes.