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Treatment for geographic atrophy (a major cause of vision loss in patients with ‘dry’ AMD) a step closer after artificial intelligence breakthrough by Moorfields/UCL

2024-06-20 13:22:57

A team led by Dr Konstantinos Balaskas at Moorfields Eye Hospital Reading Centre has developed a fully automated, deep-learning model (algorithm) that can detect and quantify geographic atrophy using standard optical coherence tomography (OCT) scans. The algorithm will be hugely beneficial to clinicians providing care for patients with geographic atrophy (GA), who will need a reliable and fast way to assess the severity of geographic atrophy, how fast it progresses and how well it responds to emerging new treatments With research into ground-breaking new treatments for GA showing great promise, this new model helps pave the way for their effective use for patient benefit. The study has been published in the Lancet Digital Health.

GA is a severe, irreversible form of age-related macular degeneration affecting over 5 million people globally, including 22% of people aged over 90. Although difficult to detect in its early stages, it can eventually lead to severe vision loss and blindness, and there is currently no effective treatment. This is in contrast to ‘wet’ AMD, which can be successfully managed with regular injections in the eye. However, trials are underway into therapies for GA have shown promise and results from Phase 3 trials by Apellis, using a breakthrough treatment called APL-2, are due to be released soon. Further testing and the eventual rollout of this therapy could be greatly enhanced by the new algorithm, developed entirely in-house by the artificial intelligence (AI) team at Moorfields. 

Currently, to diagnose patients with GA, experts must examine the patient manually, using either colour fundus photography or spectral-domain optical coherence tomography (SD-OCT) and fundus autofluorescence (FAF). Examining the many segments of an OCT to determine the precise nature of the GA is time-consuming (up to an hour per eye scan) and prone to human error and variability. In order to develop successful treatments, it is vital for researchers and clinicians to be able to assess precisely how GA manifests itself in the individual eye, and to do so at scale. The Moorfields team set out to train an algorithm using carefully graded OCT scans so that it could learn to recognise the early signs, precise localisation and progression of GA automatically. They then tested the algorithm using a completely different set of human-graded OCT scans from patients at Moorfields Eye Hospital. 

The findings, show that using the OCTs alone, the new AI system was able to match, and even outperform, predictions made by specialist human graders, all in a fraction of a second.

The algorithm consists of separate models looking at different aspects of GA: one predicts disease progression; the other can determine specific features of the disease. It was developed using 5049 individual segments (from 984 OCT scans taken from 200 patients). The scans were part of the FILLY study into a novel treatment for GA and taken from patients based in the USA, Australia and New Zealand . The results were validated using 884 manually graded segments from 192 OCT scans, collected as part of routine patient assessment from 110 patients at Moorfields.

Given that the AI works with OCT technology already used routinely in clinics, Dr Balaskas and his team are confident that their AI will prove invaluable both for research and in clinical practice all over the world. It should enable researchers to rapidly assess patients, diagnose GA, assess their suitability for treatment and the effectiveness, over time, of any medication. The team also hope that further research will yield an AI that can predict progression to GA in otherwise healthy patients, which could allow future preventive treatment to be administered at the earliest opportunity.

If treatments for GA currently at the clinical trial stage do prove successful, the number of AMD patients receiving intravitreal injections could double, with major implications for healthcare planning and implementation globally. This would make AI systems such as this one especially valuable in managing effective diagnosis and treatment of GA.

Konstantinos Balaskas, Director of the Reading Centre and Clinical Trials and Digital Eye Health Lead at Moorfields Ophthalmic Reading Centre, said: “A particular challenge for the management of GA at-scale is the need to reliably, objectively and rapidly quantify and monitor growth of area of GA on the retina using OCT scans and assess its response to potential treatments. We are the first academic/reading centre to develop an AI tool to do this. Developing it in-house and doing so at a standard required for publication in one of the top medical journals is a major accomplishment for our team, Moorfields, UCL and the NIHR. We hope it is a major step towards an effective treatment pathway for GA, which affects millions of people worldwide and often leads to debilitating sight loss.”

The current research builds on Moorfields’ earlier success in the development of AI to detect early signs of severe ‘wet’ AMD in patients. Pearse Keane, Consultant Ophthalmologist at Moorfields and Artificial Intelligence Lead at the Reading Centre, commented: “In 2016, Moorfields Eye Hospital initiated a collaboration with DeepMind, leading to the development of a ground-breaking AI system for evaluation of sight-threatening macular disease. As a result of this work, Moorfields has developed world-leading expertise in the development and application of AI systems for healthcare. This publication in The Lancet Digital Health represents the first fruits of this learning; a state-of-the-art AI system developed entirely within the NHS, for the assessment of age-related macular degeneration (AMD). Given that a recent study estimated that  around a quarter of those aged 60 and over in Europe have the early or intermediate forms of this disease, we believe this work could bring huge patient benefits, both within the UK and globally. Perhaps as importantly, we believe this work presages a new era of AI-enabled healthcare, driven by healthcare professionals, and where the NHS can be a world leader.”

This research also heralds an exciting future for the INSIGHT Health Data Research Hub, which aims to make the de-identified, manually graded OCT scans used to test the GA algorithm available to other researchers. INSIGHT, an NHS-led partnership, was set up to provide access to anonymous patient data for approved research in a safe and efficient way, with a robust governance structure. The data available through INSIGHT, which comes from Moorfields Eye Hospital and University Hospitals Birmingham, includes millions of routinely collected retinal images, scans, eye test results and related hospital records. INSIGHT encourages research that improves eye care and healthcare in general, and ensures it is carried out to the benefit of patients and the NHS in an ethical and transparent way.

The research was funded by Apellis Pharmaceuticals, with support from the NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology.

For further information, please contact gordon.harrison3@nhs.net  

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