Artificial intelligence (AI) has become a fundamental tool for assisting clinicians in analyzing ophthalmic images, such as optical coherence tomography (OCT). However, developing AI models often requires extensive annotation, and existing models tend to underperform on independent, unseen data. Foundation models (FMs), large AI models trained on vast unlabeled datasets, have shown promise in overcoming these challenges. Nonetheless, available FMs for ophthalmology lack extensive validation, especially for segmentation tasks, and focus on a single imaging modality. In this context, we propose MIRAGE, a novel multimodal FM for the analysis of OCT and scanning laser ophthalmoscopy (SLO) images. Additionally, we propose a new evaluation benchmark with OCT/SLO classification and segmentation tasks. The comparison with general and specialized FMs and segmentation methods shows the superiority of MIRAGE in both types of tasks, highlighting its suitability as a basis for the development of robust AI systems for retinal OCT image analysis. Both MIRAGE and the evaluation benchmark are publicly available: https://github.com/j-morano/MIRAGE.

翻译：人工智能（AI）已成为辅助临床医生分析眼科图像（如光学相干断层扫描（OCT））的基本工具。然而，开发AI模型通常需要大量标注，且现有模型在独立、未见数据上往往表现不佳。基础模型（FMs）是在海量无标签数据集上训练的大型AI模型，已显示出克服这些挑战的潜力。尽管如此，现有的眼科FMs缺乏广泛的验证，特别是在分割任务上，并且专注于单一成像模态。在此背景下，我们提出了MIRAGE，一种用于分析OCT和扫描激光检眼镜（SLO）图像的新型多模态FM。此外，我们提出了一个包含OCT/SLO分类和分割任务的新评估基准。与通用及专用FMs和分割方法的比较表明，MIRAGE在两类任务上均具有优越性，突显了其作为开发鲁棒性视网膜OCT图像分析AI系统基础的适用性。MIRAGE与评估基准均已公开：https://github.com/j-morano/MIRAGE。