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    Google DeepMind searches for signs of retinopathy


    This time around, Google describes in detail how Moorfields staff stripped the data of information that could be used to identify patients.

    This study will combine traditional statistical methodology and machine learning algorithms to achieve “automatic grading and quantitative analysis,” the researchers report.

    The study will include all patients who attended Moorfields Eye Hospital NHS Foundation Trust sites between January 1, 2007, and February 29, 2016, who had digital retinal imaging (including fundus digital photographs and OCT) as part of their routine clinical care, except patients whose records are in hard copy and patients who have requested that their data not be shared.

    Deep neural networks of the type used by DeepMind may have millions or billions of parameters, “so large amounts of data are needed to automatically infer those parameters during learning,” De Fauw and his colleagues wrote.

    In addition to the images, DeepMind will analyse demographic information, primary and secondary diagnostic labels describing the pathology in the image and associated severity (such as grade of retinopathy), treatment information and a model of the imaging device.

    DeepMind will also analyse a second dataset in which pseudonyms are attached to the patients so that disease progression can be tracked over time.

    Trained graders at Moorfields will attach additional manual labels annotating pathological and anatomical features to a selection of the images. The researchers will compare the machine’s performance in identifying abnormalities to humans’ performance.

    No patients will be approached as part of the study.

    The research project agreement is for 5 years.

    In a separate eye-related project, Google is also developing contact lenses that monitor blood sugar levels in people with diabetes.




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