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Updated: Jun 28, 2026
10:14
Ex Vivo OCT-Based Multimodal Imaging of Human Donor Eyes for Research into Age-Related Macular Degeneration
Published on: May 26, 2023
ORBIT-AMD: Ordinal Risk, Bilateral Imaging, and Trajectory Learning for Age-Related Macular Degeneration in
Xuehao Cui1,2,3,4, Dejia Wen1,2, Patrick Yu-Wai-Man3,4
1Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin, China.
Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|June 27, 2026
Summary
A new AI framework, ORBIT-AMD, accurately detects and predicts age-related macular degeneration (AMD) progression using multimodal imaging. This trajectory-aware approach improves risk stratification for better patient management.
Area of Science:
- Ophthalmology
- Artificial Intelligence
- Medical Imaging
Background:
- Age-related macular degeneration (AMD) is a progressive eye disease often analyzed with static AI models.
- Current AI approaches lack trajectory-awareness, failing to capture AMD's longitudinal nature.
Purpose of the Study:
- To develop ORBIT-AMD, a multimodal trajectory-learning framework for age-related macular degeneration.
- To improve AMD risk stratification and prioritize patient reviews.
Main Methods:
- Integrated color fundus photography and optical coherence tomography.
- Employed bilateral eye-graph attention, concept bottlenecks, and ordinal staging.
- Utilized cause-specific discrete-time survival prediction and protocol alignment.
Main Results:
- Achieved high AUROC values for prevalent late-AMD detection (0.984 internal, 0.975 external).
- Demonstrated strong performance in five-year late-AMD progression prediction (0.767-0.825 AUROC).
- Concept bottleneck provided auditable, lesion-level predictive explanations.
Conclusions:
- ORBIT-AMD offers a trajectory-aware framework for AMD management.
- Site-specific calibration and clinical workflow evaluation are crucial before deployment.
- Prospective validation is necessary for clinical implementation.
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