Deep-learning in hospitals

Algorithm predicts retinal thickness. From left to right: Fundus image, OCT image, true retinal thickness, predicted retinal thickness. Image: Helmholtz Zentrum München Novel deep learning method enables clinic-ready automated screening for diabetes-related eye disease - Researchers created a novel deep learning method that makes automated screenings for eye diseases such as diabetic retinopathy more efficient. Reducing the amount of expensive annotated image data that is required for the training of the algorithm, the method is attractive for clinics. In the use case of diabetic retinopathy, the researchers developed a screening algorithm. In recent years, clinics have taken first steps towards artificial intelligence and deep learning to automate medical screenings. However, training a deep learning algorithm for accurate screening and diagnosis prediction requires large sets of annotated data and clinics often struggle with expensive expert labelling.