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COMPUTERIZED SYSTEMS FOR PREDICTION OF GEOGRAPHIC ATROPHY PROGRESSION USING DEEP LEARNING APPLIED TO CLINICAL IMAGING
COMPUTERIZED SYSTEMS FOR PREDICTION OF GEOGRAPHIC ATROPHY PROGRESSION USING DEEP LEARNING APPLIED TO CLINICAL IMAGING
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机译:应用于临床影像学的深度学习预测地理萎缩进展的计算机系统
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摘要
An electronic device is disclosed. The device receives retinal images and patient data corresponding to the retinal images. The device can train a first machine learning model (“model”) based on a first group of the retinal images and patient data corresponding to the first group and a second model based on a second group of the retinal images and patient data corresponding to the second group. The electronic device can generate a first prediction based on the first subset of a third group of the retinal images and a second prediction based on the second subset of the third group. After training the first model and the second model, the device can train a third model to predict a geographic atrophy progression in an eye of a patient based on the first and second predictions, the first and second subsets, and patient data corresponding to the first and second subset.
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