Vision loss among the elderly is one of the most pressing healthcare challenges today. By the age of 65, nearly one in three individuals experience some form of sight-reducing disease. Among these, age-related macular degeneration (AMD) stands out as the leading cause of blindness in the developed world.
In Europe alone, 25% of adults over 60 are affected by AMD. While the ‘dry’ form of AMD causes only mild vision loss, 15% of patients progress to the more severe exudative form (exAMD)—a condition that can result in rapid and irreversible blindness. But what if we could predict who’s at risk before it’s too late?
A New Era of Prevention Using AI
A recent study published in Nature Medicine presents a revolutionary leap forward in eye care. In collaboration with Moorfields Eye Hospital and Google Health, researchers have developed an artificial intelligence (AI) system capable of predicting whether a patient with dry AMD will progress to exAMD within six months.
This breakthrough introduces a powerful early warning system—something clinicians and patients have long needed.
The Dataset Behind the Discovery
To train this system, researchers used a unique dataset of 2,795 anonymized retinal scans from high-risk AMD patients treated at seven Moorfields locations across London. These patients underwent high-resolution 3D Optical Coherence Tomography (OCT) scans at each visit, capturing detailed structural images of their retinas.
Working with retinal specialists, the team labeled the exact scan where exAMD first became visible. This gave the AI the foundation to learn the early signs of progression.
How the AI System Works
The model consists of two deep convolutional neural networks. One processes the raw OCT scans, while the other works on anatomically segmented data—a structured representation of known retinal features such as drusen (fat deposits) and retinal pigment epithelium (RPE) loss.
By combining these views, the system gains a comprehensive understanding of the eye’s condition, and predicts whether exAMD will develop in the next 6 months. This timeframe allows doctors to plan at least two follow-up visits in advance—providing a meaningful head start for intervention.
Matching—and Exceeding—Expert Performance
To benchmark the model, researchers conducted a clinical study with six seasoned eye experts (three ophthalmologists and three optometrists with over 10 years of experience). The task: predict exAMD progression based on the same data.
Even for these experts, the challenge proved difficult and subjective. But the AI system matched—and sometimes outperformed—their predictions, with more consistent accuracy and lower variability.
Visualizing Risk in Real Time
Another powerful feature of the system is its anatomical transparency. It not only delivers a prediction but also segments the retina into meaningful regions, enabling clinicians to track tissue-level changes over time.
A compelling case study shows scans over a 13-month period. The AI model not only identifies subtle changes before visible symptoms occur, but also provides risk scores aligned with these changes—offering a roadmap for timely treatment decisions.
Clinical Promise and Real-World Challenges
While the model offers incredible promise, it’s not yet ready for routine clinical use. Further testing is needed across diverse global populations and real-world hospital settings. Importantly, clinicians must weigh the risks of false positives—where patients might receive unnecessary treatments based on inaccurate predictions.
To address this, the researchers propose different operating thresholds for the model. For instance, at a specificity of 90%, the model achieves a sensitivity of 34%—identifying a significant portion of at-risk eyes while keeping false alarms low.
This level of foresight could guide clinical trials, improve monitoring schedules, and potentially pave the way for early intervention therapies to preserve vision.
Looking Ahead
“AMD is an incredibly complex disease that profoundly affects the lives of millions. With this work, we haven’t solved AMD—but we’ve just added another big piece of the puzzle.”
— Pearse Keane, NIHR Clinician Scientist
This AI breakthrough represents a major milestone in preventative healthcare, and the implications stretch beyond ophthalmology. The model code has been open-sourced for researchers to build upon, and Moorfields Eye Hospital will share the dataset via the Ryan Initiative for Macular Research—fueling further innovation.
At RCEE Networks, we are inspired by the transformative potential of AI in healthcare. From early detection to smarter patient care, technology is making the impossible possible—helping us see the future, before it’s too late.
