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<p>Branch retinal vein occlusion (BRVO) can cause persistent visual impairment, and predicting long-term best-corrected visual acuity (BCVA) after anti-vascular endothelial growth factor treatment remains clinically challenging. This retrospective proof-of-concept study developed multimodal neural networks to predict 12-month BCVA classes using retinal images and clinical metadata from treatment-naive BRVO eyes. The best internal model used OCT-horizontal scans, OCTA images, baseline BCVA, central subfield thickness, age, and sex. We evaluated performance using adjacent accuracy, exact accuracy, and mean absolute error under five-fold cross-validation, and we analyzed attribution localization using Pathway Attribution. We additionally performed a supplementary exploratory cross-disease feasibility analysis using an available diabetic macular edema (DME) OCT cohort with 24-month visual acuity outcomes. This analysis was interpreted only as supplementary exploratory feasibility evidence and not as disease-matched BRVO external validation. Overall, the results suggest that multimodal imaging and clinical metadata may provide complementary prognostic information after BRVO, but the modest predictive performance, retrospective single-center design, lack of disease-matched external validation, and limited attribution reliability require cautious interpretation and further prospective validation.</p>

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Interpretability of multimodal neural networks for prediction of visual acuity in patients with branch retinal vein occlusion

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月23日

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  • Soyoun Won,
  • Kiyoung Kim,
  • Youngseob Won,
  • Samra Irshad,
  • Sungyoung Lee,
  • Seung-Young Yu,
  • Seong Tae Kim

摘要

Branch retinal vein occlusion (BRVO) can cause persistent visual impairment, and predicting long-term best-corrected visual acuity (BCVA) after anti-vascular endothelial growth factor treatment remains clinically challenging. This retrospective proof-of-concept study developed multimodal neural networks to predict 12-month BCVA classes using retinal images and clinical metadata from treatment-naive BRVO eyes. The best internal model used OCT-horizontal scans, OCTA images, baseline BCVA, central subfield thickness, age, and sex. We evaluated performance using adjacent accuracy, exact accuracy, and mean absolute error under five-fold cross-validation, and we analyzed attribution localization using Pathway Attribution. We additionally performed a supplementary exploratory cross-disease feasibility analysis using an available diabetic macular edema (DME) OCT cohort with 24-month visual acuity outcomes. This analysis was interpreted only as supplementary exploratory feasibility evidence and not as disease-matched BRVO external validation. Overall, the results suggest that multimodal imaging and clinical metadata may provide complementary prognostic information after BRVO, but the modest predictive performance, retrospective single-center design, lack of disease-matched external validation, and limited attribution reliability require cautious interpretation and further prospective validation.