Using DeepMind’s neural network learning system to diagnose eye diseases
Three institutions working together have applied DeepMind’s neural network learning system to the task of discovering and diagnosing eye diseases. Moorfields Eye Hospital has been working with Google’s DeepMind Health subsidiary and University College London in the effort, and have documented their progress in a paper published in Nature Medicine.
As the researchers note, eye doctors currently use a machine that carries out optical coherence tomography (OCT) on patients to find out if they have an eye disease. While the technique is quite useful and accurate, it requires highly trained doctors to spend time looking at results. The researchers suggest this creates a backlog that sometimes prevents patients from getting the care they need in time to save their vision. In this new effort, the researchers put together a system built on DeepMind’s neural network learning system to find out if such a system could help doctors more quickly assess whether a patient needs urgent care.
The system the team put together consisted of two neural networks—the first analyzes the results of OCT scans and provides a map of possible problem areas. The second studies the map provided by the first neural network and then offers clinicians a diagnosis and recommendation regarding what needs to happen next for a given patient and when. Best of all, it can do its work in mere minutes, increasing the chances of recovery for at-risk patients. The system was trained by giving it 15,000 OCT scans from 7,500 patients and related diagnoses made by doctors. The researchers stress that the system is not meant to replace trained eye doctors, but to assist in more quickly identifying those who are most in need of urgent care. The team is hoping their system can move to clinical trials in the near future.
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