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@TECHREPORT{Pulvirenti_Idiap-Internal-RR-21-2026,
                      author = {Pulvirenti, Roberto and Amiot, Victor and Kaisari, Eirini and Amstutz, Christoph and Jimenez-del-Toro, Oscar and Bergin, Ciara and Meloni, Ilenia and Anjos, Andr{\'{e}} and Tomasoni, Mattia and Hoogewoud, Florence},
         mainresearchprogram = {AI for Life},
                       month = jul,
                       title = {External Validation of an AI Model for Grading Retinal Inflammation in Uveitis},
                        type = {Idiap-RR},
                      number = {Idiap-Internal-RR-21-2026},
                        year = {2026},
                 institution = {Idiap},
                    abstract = {Background: Fluorescein angiography is central to assessing retinal inflammation in uveitis but requires expert interpretation and is subject to interobserver variability. This study evaluated UveAI, a deep learning model for automated fluorescein angiography grading, on an independent external dataset without retraining.
Methods: This validation study included fluorescein angiography examinations acquired between 2021 and 2023 at an independent referral centre using the Spectralis-Heidelberg system. Images were de-identified and graded without clinical information. Six retinal inflammatory signs were assessed: optic disc hyperfluorescence, macular oedema, posterior pole vascular leakage, posterior pole capillary leakage, peripheral vascular leakage and peripheral capillary leakage. Agreement between UveAI and expert composite grading was assessed using Pearson correlation (r) and a linear mixed-effects model. Individual sign performance was evaluated using the ordinal classification index (1-OCI) and area under the receiver operating characteristic curve (AUC). 
Results: Of 114 eligible eyes, 60 with complete posterior and peripheral imaging were included. UveAI predictions correlated with expert composite inflammation scores (r=0.91, 95\% CI 0.84 to 0.95; p<0.001), with near one-to-one correspondence on mixed-effects modelling (slope 0.98, 95\% CI 0.86 to 1.10; intercept 0.07, 95\% CI −1.29 to 1.43). Mean agreement was comparable with previously reported intergrader agreement (1-OCI, mean [SD], 0.84 [0.03] vs 0.82 [0.02]). Across individual signs, AUC ranged from 0.89 to 0.94.
Conclusion: UveAI reproduced expert grading on an independent external dataset without retraining, indicating generalisability within the workflow conditions of this study.},
                         pdf = {https://publications.idiap.ch/attachments/internals/2026/Pulvirenti_Idiap-Internal-RR-21-2026.pdf}
}