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Uveitis Research Network
A Global Consortium for Data-Driven Discovery, Precision Medicine, and AI in Uveitis
Mission
The Uveitis Research Network (URN) is an international, multi-institutional consortium dedicated to advancing the understanding, diagnosis, and management of uveitis through large-scale data integration, advanced analytics, and artificial intelligence. URN aims to unify heterogeneous clinical, imaging, and biological data to enable objective disease quantification, severity scoring, biomarker discovery, and personalized treatment strategies. This initiative is led by Dr. Siamak Yousefi from Bascom Palmer Eye Institute (BPEI).
Scientific Scope
URN focuses on:
- Intermediate, posterior, and panuveitis
Core scientific goals include:
- Accumulating datasets
- Performing annotation and quantification of data
- Developing AI models to score activity and diagnosis
Current Collaborators
University of Miami, Bascom Palmer Eye Institute / Miller School of Medicine
- Siamak Yousefi
- Janet L. Davis
- Victor L. Perez
- Seyed Amin Nabavi
- Asma Poursoroush
- Amr Saad Mohamed Elsawy
National Eye Institute (NEI), National Institutes of Health (NIH)
- Mary Mattapallil
- Rachel Caspi
- Vijayaraj Nagarajan
- Yingyos Jittayasothorn
- Reiko Yamane
- Xiaoqin Huang
- Jian Sun
University of Cologne, Germany
- Avik Shome
University of Bristol, United Kingdom
- Laurence Nicholson
University of Auckland, New Zealand
- Ilva Rupenthal
King Edward Medical University, Pakistan
- Hammad Khan
Other Collaborators
- Mohammad Mohammadi
- Abbas Khoder
- Shaurya Madukuri
- Mohsen Karimi
Datasets and Modalities
URN is designed as a multi-modal, longitudinal data ecosystem, including:
Imaging
- Color fundus photography
- Widefield fundus imaging
- Optical coherence tomography (OCT)
- OCT angiography (OCTA)
- Fluorescein angiography (FA)
- Indocyanine green angiography (ICGA)
Clinical Data
- Demographics and disease history
- Anatomic classification and activity grading
- Treatment history (steroids, immunomodulators, biologics)
- Visual acuity and clinical outcomes
- Recurrence and flare timelines
Laboratory & Systemic Data
- Inflammatory markers
- Autoimmune and infectious testing
- Systemic disease associations
Genomics (where available)
- Targeted genotyping
- Polygenic risk scores
- Future expansion to multi-omics
AI and Analytics Framework
URN will develop:
- AI models for image-based quantification
- AI models for classification and severity scoring
Collaborative Model
URN operates as a federated, collaborative network, enabling:
- Multi-center data contribution with local governance
- Standardized annotation protocols and grading schemas
- Shared AI development pipelines
- Authorship and data-use policies that promote fairness and transparency
Collaborators include:
- Academic uveitis centers
- International ophthalmology institutes
- AI and data science laboratories
- Industry partners (imaging, diagnostics, therapeutics)
Impact
URN aims to:
- Establish global standards for uveitis quantification
- Reduce subjectivity in disease assessment
- Enable precision medicine in uveitis
- Accelerate clinical trials and biomarker discovery
- Improve long-term visual outcomes for patients worldwide
Get Involved
URN welcomes:
- Clinical centers with uveitis expertise
- Researchers in imaging, AI, immunology, and genetics
- Industry and regulatory partners
- Trainees and early-career investigators