Abstract
This study evaluates the role of differential artery-vein (AV) analysis in optical coherence tomography angiography (OCTA) for treatment outcome prediction of diabetic macular edema (DME). Deep learning AV segmentation in OCTA enabled the robust extraction of quantitative AV features, including perfusion intensity density (PID), blood vessel density (BVD), vessel skeleton density (VSD), vessel area flux (VAF), blood vessel caliber (BVC), blood vessel tortuosity (BVT), and vessel perimeter index (VPI). Support vector machine (SVM) classifiers were employed to predict changes in best-corrected visual acuity (BCVA) and central retinal thickness (CRT). Comparative analysis revealed that differential AV analysis significantly enhanced prediction performance, with BCVA accuracy improved from 70.45% to 86.36% and CRT accuracy enhanced from 68.18% to 79.55% compared to traditional OCTA analysis. These findings underscore the potential of AV analysis as a transformative tool for advancing personalized therapeutic strategies and improving clinical decision-making in managing DME.