Automatic transformer-based grading of multiple retinal inflammatory signs in uveitis on fluorescein angiography

Abstract

Background

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.