Machine Learning Quantification of Fluid Volume in Eyes With Retinal Vein Occlusion Treated With Aflibercept: The REVOLT Study

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Purpose: To evaluate the combined relationship between ischemia, retinal fluid, and layer thickness measurements with visual acuity (VA) outcomes in patients with retinal vein occlusion (RVO). Methods: Swept-source optical coherence tomography (OCT) data were used to assess retinal layer thickness and quantify intraretinal fluid (IRF) and subretinal fluid (SRF) using a deep learning–based macular fluid segmentation algorithm for treatment-naïve eyes diagnosed with visual impairment resulting from central RVO (CRVO) or branch RVO (BRVO). Patients received 3 loading doses of 2 mg intravitreal aflibercept injections and were then put on a treat-and-extend regimen. Image analysis was performed at baseline and postoperatively at 3 months and 6 months. The baseline OCT morphologic features and fluid measurements were correlated with the changes in best-corrected VA (BCVA) using the Pearson correlation coefficient ( r ). Results: The study comprised 49 eyes. A combined model incorporating thickness in the outer plexiform layer (OPL), retinal nerve fiber layer (RNFL), and presence of IRF had the strongest overall correlation for CRVO ( r = 0.865; P < .05). For BRVO, the addition of IRF to the OPL–inner nasal model had a strong correlation ( r = 0.803; P < .05). The baseline ischemic index in the deep capillary complex showed a notable correlation with the 6-month change in BCVA for CRVO ( r = 0.9101; P < .001) and BRVO ( r = 0.9200; P < .001). Conclusions: A combined model of IRF volume, OPL, and RNFL layer thicknesses, along with ischemic indices, provides the best correlation to BCVA changes. Combined fluid and layer segmentation of OCT images provides clinically useful biomarkers for patients with RVO. These results give insight into the pathology of RVOs and describe the relationship between deep capillary complex ischemia and OPL/RNFL thickness in BCVA outcomes.

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