A new breakthrough in eye research shows that artificial intelligence can predict who is at risk of losing their sight – years before traditional methods would ever detect it. This could prevent unnecessary treatments and protect the vision of patients who truly need early intervention.
AI gives doctors a head start
By analyzing thousands of eye scans, the algorithm can detect early warning signs that human specialists cannot see with the naked eye.
Focus on keratoconus
The study centers on keratoconus, a condition where the cornea bulges outward. It often begins in adolescence and can lead to severe vision loss if left untreated.
A complex challenge
Some patients manage well with contact lenses, while others deteriorate quickly and end up needing invasive procedures or even corneal transplants.
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One treatment makes the difference
A technique known as “cross-linking” can halt progression, but only if performed before permanent damage occurs. Identifying those patients early is crucial.
The algorithm behind the breakthrough
Researchers in London trained AI with more than 36,000 eye scans from nearly 7,000 patients, combined with health and clinical data.
Predictions from the very first visit
Even after a single scan, AI could accurately forecast whether the disease would worsen or remain stable – a game changer for patient care.
Smarter sorting of patients
The system divides people into low-risk patients, who can be monitored less frequently, and high-risk patients, who should receive prompt treatment.
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A boost for healthcare systems
By reducing unnecessary check-ups, doctors can dedicate more time to those in urgent need, while easing pressure on overstretched clinics.
Beyond keratoconus
The same technology may soon be adapted to detect eye infections, inherited conditions, and other sight-threatening diseases.
A step closer to saving vision
Once safety tests are complete, the algorithm could be rolled out in clinics, preventing thousands of people from going blind unnecessarily.
This article is based on information from ScienceDaily
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