Differences in Artificial Intelligence-Based Macular Fluid Parameters Between Clinical Stages of Diabetic Macular Edema and Their Relationship with Visual Acuity

Background/Objectives: The aim of this study was to determine artificial intelligence-based macular fluid (MF) parameters in diabetic macular edema (DME) with optical coherence tomography (OCT) and examine stage-by-stage differences in MF parameters and their relationship with best-corrected visual acuity (BCVA). Methods: This study enrolled 104 eyes with treatment-na & iuml;ve DME. Intraretinal fluid (IRF) and subretinal fluid (SRF) were detected in horizontal OCT images based on the "Hokkaido University MF segmentation model" when DME was first observed together with BCVA testing. The MF area, the mean brightness, and the variance of brightness were compared between mild or moderate non-proliferative diabetic retinopathy (mNPDR, n = 33), severe NPDR (sNPDR, n = 52), and PDR eyes (n = 19). Correlations between logMAR BCVA and MF parameters were also examined. Results: All the MF parameters tended to increase with DR stages. Especially, the mean brightness of IRF was significantly greater in PDR than in mNPDR. The variance of brightness of IRF increased in sNPDR compared to mNPDR, whereas that of SRF increased in PDR compared to sNPDR. LogMAR BCVA showed positive correlations with MF areas and the variance of brightness of SRF. Conclusions: The qualitative and quantitative MF parameters may be useful for better understanding DME pathogenesis according to DR progression.