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      • Record: found
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      • Article: found

      Artificial Intelligence to Detect Papilledema from Ocular Fundus Photographs

      Author(s): 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1
      Publication date (Electronic):
      Journal: New England Journal of Medicine
      Publisher: Massachusetts Medical Society

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          Abstract

          Nonophthalmologist physicians do not confidently perform direct ophthalmoscopy. The use of artificial intelligence to detect papilledema and other optic-disk abnormalities from fundus photographs has not been well studied.

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          Deep learning.

          Yann LeCun, Yoshua Bengio, Geoffrey E Hinton (2015)
          Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.
          Article 0 comments Cited 8991 times – based on 0 reviews     Review now
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            Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs.

            Varun Gulshan, Lily Peng, Marc Coram … (2016)
            Deep learning is a family of computational methods that allow an algorithm to program itself by learning from a large set of examples that demonstrate the desired behavior, removing the need to specify rules explicitly. Application of these methods to medical imaging requires further assessment and validation.
            Article 0 comments Cited 1364 times – based on 0 reviews     Review now
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              Machine Learning in Medicine

              Alvin Rajkomar, Jeffrey S. Dean, Isaac Kohane (2019)
              Article 0 comments Cited 748 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Title: New England Journal of Medicine
                Abbreviated Title: N Engl J Med
                Publisher: Massachusetts Medical Society
                ISSN (Print): 0028-4793
                ISSN (Electronic): 1533-4406
                Publication date (Electronic): April 14 2020
                Affiliations
                [1 ]From the Singapore National Eye Center (D.M., D.T., S.S., C.-Y.C., T.Y.W.), Singapore Eye Research Institute (D.M., R.P.N., D.T., C.V., S.S., C.-Y.C., T.Y.W.), Duke–NUS Medical School (D.M., R.P.N., D.T., S.S., C.-Y.C., T.Y.W.), Institute of High Performance Computing, Agency for Science, Technology, and Research (J.Z., X.X., Y.L.), and Yong Loo Lin School of Medicine, National University of Singapore (S.S., T.Y.W.) — all in Singapore; Farabi Eye Hospital, Tehran University of Medical Science, Tehran,...
                Article
                DOI: 10.1056/NEJMoa1917130
                PubMed ID: 32286748
                SO-VID: d13c81e3-4686-41a9-b75a-0225bae391c2
                Copyright © © 2020
                License:

                http://www.nejmgroup.org/legal/terms-of-use.htm

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