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Related Experiment Video

Updated: Dec 21, 2025

Author Spotlight: Ex Vivo OCT-Based Multimodal Imaging of Human Donor Eyes for Research into Age-Related Macular Degeneration

Author Spotlight: Ex Vivo OCT-Based Multimodal Imaging of Human Donor Eyes for Research into Age-Related Macular Degeneration

Published on: May 26, 2023

Predicting conversion to wet age-related macular degeneration using deep learning.

Jason Yim¹, Reena Chopra^1,2, Terry Spitz³

¹DeepMind, London, UK.

Nature Medicine

|May 20, 2020

Summary

An artificial intelligence system predicts progression to exudative age-related macular degeneration (exAMD) in the second eye. This AI tool aids in early detection and intervention for visual deterioration.

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Author Spotlight: Unraveling the Pathogenesis of Age-Related Macular Degeneration and Discovering Potential Therapies

Published on: July 28, 2023

Related Experiment Videos

Last Updated: Dec 21, 2025

Author Spotlight: Ex Vivo OCT-Based Multimodal Imaging of Human Donor Eyes for Research into Age-Related Macular Degeneration

Author Spotlight: Ex Vivo OCT-Based Multimodal Imaging of Human Donor Eyes for Research into Age-Related Macular Degeneration

Published on: May 26, 2023

Author Spotlight: Understanding Age-Related Macular Degeneration Pathophysiology with QAF Workflow

Author Spotlight: Understanding Age-Related Macular Degeneration Pathophysiology with QAF Workflow

Published on: May 26, 2023

Author Spotlight: Unraveling the Pathogenesis of Age-Related Macular Degeneration and Discovering Potential Therapies

Author Spotlight: Unraveling the Pathogenesis of Age-Related Macular Degeneration and Discovering Potential Therapies

Published on: July 28, 2023

Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Exudative age-related macular degeneration (exAMD) significantly causes vision loss.
Early detection of exAMD in the second eye is crucial for timely intervention.

Purpose of the Study:

To develop and validate an AI system for predicting exAMD progression in the fellow eye.
To assess the AI system's performance in identifying high-risk patients.

Main Methods:

Utilized 3D optical coherence tomography (OCT) images and automatic tissue maps.
Developed a combined AI model for exAMD progression prediction within a 6-month timeframe.
Evaluated AI performance using sensitivity and specificity metrics.

Main Results:

The AI system achieved 80% sensitivity at 55% specificity, and 34% sensitivity at 90% specificity.
Automatic tissue segmentation identified pre-conversion anatomical changes and high-risk subgroups.
The AI system outperformed five out of six human experts in prediction accuracy.

Conclusions:

AI-powered prediction of exAMD progression is feasible and accurate.
The developed AI system can aid in clinical decision-making for exAMD management.
AI has the potential to reduce interobserver variability in exAMD diagnosis.