ArticleNPJ digital medicine2025
Multimodal foundation model and benchmark for comprehensive retinal OCT image analysis.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed.
- Motion-corrected Gaussian, mean, and parametric curvature mapping of the posterior eye with pupil-tracked whole-eye OCT.Biomedical optics express · 2026Article
- AI-driven multimodal retinal imaging for early detection and risk stratification of vascular and neurodegenerative diseases.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- Fundus to fluorescein angiography video generation as a retinal generative foundation model.NPJ digital medicine · 2026Article
- Comparative Analysis of General-Purpose vs. Domain-Specific Multimodal Models for Diabetic Retinopathy Classification.Diagnostics (Basel, Switzerland) · 2026Article
- Multimodal information-fused embodied intelligence toward autonomous robotic microsurgery.Innovation (Cambridge (Mass.)) · 2026Review
- One Size Fits All? Comparing Foundation and Task-specific Models for Retinal Fluid Segmentation.medRxiv : the preprint server for health sciences · 2026Article
- Angiography-free diagnosis of retinal diseases via interpretable multi-modal learning.NPJ digital medicine · 2026Article
- Shifting the retinal foundation models paradigm from slices to volumes for optical coherence tomography.NPJ digital medicine · 2026Article
- Article
- Benchmarking Vision Encoders for Image Classification in Ophthalmology.Computational and structural biotechnology journal · 2026Article
- NeoNet: A Novel Deep Learning Model for Retinal Disease Diagnosis and Localization.Sensors (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
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.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.