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<p>Retinal inflammation is a key determinant of visual prognosis in uveitis, yet its assessment on fluorescein angiography remains subjective, labor-intensive, and insufficiently scalable for clinical trials or large cohort studies. Fluorescein angiography is the gold standard for assessing retinal inflammation. However, its scoring remains challenging, as the process is complex and time-consuming, limiting routine use in clinical trials and patient care. We present UveAI, a modular deep learning framework that grades all major retinal inflammatory signs in fluorescein angiography across posterior pole and periphery to generate an ASUWOG-aligned inflammation score. Trained on 3,220 FA images from 644 eyes (369 patients), UveAI integrates six transformer models detecting macular edema, optic disc hyperfluorescence, and vascular and capillary leakage in the posterior pole and periphery. On an independent test set, UveAI showed high concordance with an expert grader for total score (<i>R</i> = 0.96) and strong performance for individual signs (mean AUC = 0.952). Grad-CAM maps confirmed clinically relevant focus, supporting automated, standardised FA scoring in uveitis.</p>

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UveAI: clinic-ready scoring of retinal inflammation in uveitis on widefield fluorescein angiography using AI

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月13日

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  • Victor Amiot,
  • Roberto Pulvirenti,
  • Oscar Jimenez-del-Toro,
  • Muriel Ott,
  • Teodora-Elena Bogaciu,
  • Shalini Banerjee,
  • Christoph Amstutz,
  • Jean-Marc Odobez,
  • Christophe Chiquet,
  • Yan Guex-Crosier,
  • Ciara Bergin,
  • Ilenia Meloni,
  • André Anjos,
  • Florence Hoogewoud,
  • Mattia Tomasoni

摘要

Retinal inflammation is a key determinant of visual prognosis in uveitis, yet its assessment on fluorescein angiography remains subjective, labor-intensive, and insufficiently scalable for clinical trials or large cohort studies. Fluorescein angiography is the gold standard for assessing retinal inflammation. However, its scoring remains challenging, as the process is complex and time-consuming, limiting routine use in clinical trials and patient care. We present UveAI, a modular deep learning framework that grades all major retinal inflammatory signs in fluorescein angiography across posterior pole and periphery to generate an ASUWOG-aligned inflammation score. Trained on 3,220 FA images from 644 eyes (369 patients), UveAI integrates six transformer models detecting macular edema, optic disc hyperfluorescence, and vascular and capillary leakage in the posterior pole and periphery. On an independent test set, UveAI showed high concordance with an expert grader for total score (R = 0.96) and strong performance for individual signs (mean AUC = 0.952). Grad-CAM maps confirmed clinically relevant focus, supporting automated, standardised FA scoring in uveitis.