Artificial Intelligence-Assisted Perfusion Density as Biomarker for Screening Diabetic Nephropathy

Purpose: To identify a reliable biomarker for screening diabetic nephropathy (DN) using artificial intelligence (AI)-assisted ultra-widefield swept-source optical coherence tomography angiography (UWF SS-OCTA). Methods: This study analyzed data from 169 patients (287 eyes) with type 2 diabetes mellitus (T2DM), resulting in 15,211 individual data points. These data points included basic demographic information, clinical data, and retinal and choroidal data obtained through UWF SS-OCTA for each eye. Statistical analysis, 10-fold cross-validation, and the random forest approach were employed for data processing. Results: The degree of retinal microvascular damage in the diabetic retinopathy (DR) with the DN group was significantly greater than in the DR without DN group, as measured by SS-OCTA parameters. There were strong associations between perfusion density (PD) and DN diagnosis in both the T2DM population (r = - 0.562 to - 0.481, < 0.001) and the DR population (r = - 0.397 to - 0.357, P < 0.001). The random forest model showed an average classification accuracy of 85.8442% for identifying DN patients based on perfusion density in the T2DM population and 82.5739% in the DR population. Conclusions: Quantitative analysis of microvasculature reveals a correlation between DR and DN. UWF PD may serve as a significant and noninvasive biomarker for evaluating DN in patients through deep learning. AI-assisted SS-OCTA could be a rapid and reliable tool for screening DN.