ARTICLE Amiot_CBM_2025/IDIAP Automatic transformer-based grading of multiple retinal inflammatory signs in uveitis on fluorescein angiography Amiot, Victor Jimenez-del-Toro, Oscar Guex-Croisier, Yan Ott, Muriel Bogaciu, Teodora-Elena Banerjee, Shalini Howell, Jeremy Amstutz, Christoph Chiquet, Christophe Bergin, Ciara Meloni, Ilenia Tomasoni, Mattia Hoogewoud, Florence Anjos, André deep learning fluorescein angiography inter-grader agreement Optic disease grading transformers Uveitis vasculitis Computers in Biology and Medicine 193 110327 0010-4825 2025 https://github.com/JulesGoninRIO/uveitis_transformers URL https://doi.org/10.1016/j.compbiomed.2025.110327 doi Grading fluorescein angiography (FA) for uveitis is complex, often leading to the oversight of retinal inflammation in clinical studies. This study aims to develop an automated method for grading retinal inflammation. Methods. Patients from Jules-Gonin Eye Hospital with active or resolved uveitis who underwent FA between 2018 and 2021 were included. FAs were acquired using a standardized protocol, anonymized, and annotated following the Angiography Scoring for Uveitis Working Group criteria, for four inflammatory signs of the posterior pole. Intergrader agreement was assessed by four independent graders. Four deep learning transformer models were developed, and performance was evaluated using the Ordinal Classification Index, accuracy, F1 scores, and Kappa scores. Saliency analysis was employed to visualize model predictions. Findings. A total of 543 patients (1042 eyes, 40987 images) were included in the study. The models closely matched expert graders in detecting vascular leakage (F1-score = 0·87, 1-OCI = 0·89), capillary leakage (F1-score = 0·86, 1-OCI = 0·89), macular edema (F1-score = 0·82, 1-OCI = 0·86), and optic disc hyperfluorescence (F1-score = 0·72, 1-OCI = 0·85). Saliency analysis confirmed that the models focused on relevant retinal structures. The mean intergrader agreement across all inflammatory signs was F1-score = 0·79 and 1-OCI = 0·83. Interpretation. We developed a vision transformer-based model for the automatic grading of retinal inflammation in uveitis, utilizing the largest dataset of FAs in uveitis to date. This approach provides significant clinical benefits for the evaluation of uveitis and paves the way for future advancements, including the identification of novel biomarkers through the integration of clinical data and other modalities.