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Differential Artery–Vein Analysis in OCTA for Predicting Anti-VEGF Treatment Outcome of Diabetic Macular Edema

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posted on 2025-01-22, 07:20 authored by Xincheng Yao, Mansour Abtahi, ALBERT DADZIE, Behrouz Ebrahimi, Boda Huang, Yi-Ting Hsieh
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 incorporating differential AV analysis significantly enhanced prediction performance, with BCVA improvement accuracy increasing from 70.45% to 86.36% and CRT improvement accuracy rising 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.

History

Funder Name

National Eye Institute (P30 EY001792,R01 EY029673,R01 EY030101,R01 EY023522,R01 EY030842); Research to Prevent Blindness; Richard and Loan Hill Department of Biomedical Engineering, University of Illinois at Chicago

Preprint ID

120625

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