title: Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs.
abstract: 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.
  
  
  
  To apply deep learning to create an algorithm for automated detection of diabetic retinopathy and diabetic macular edema in retinal fundus photographs.
  
  
  
  A specific type of neural network optimized for image classification called a deep convolutional neural network was trained using a retrospective development data set of 128 175 retinal images, which were graded 3 to 7 times for diabetic retinopathy, diabetic macular edema, and image gradability by a panel of 54 US licensed ophthalmologists and ophthalmology senior residents between May and December 2015. The resultant algorithm was validated in January and February 2016 using 2 separate data sets, both graded by at least 7 US board-certified ophthalmologists with high intragrader consistency.
  
  
  
  Deep learning-trained algorithm.
  
  
  
  The sensitivity and specificity of the algorithm for detecting referable diabetic retinopathy (RDR), defined as moderate and worse diabetic retinopathy, referable diabetic macular edema, or both, were generated based on the reference standard of the majority decision of the ophthalmologist panel. The algorithm was evaluated at 2 operating points selected from the development set, one selected for high specificity and another for high sensitivity.
  
  
  
  The EyePACS-1 data set consisted of 9963 images from 4997 patients (mean age, 54.4 years; 62.2% women; prevalence of RDR, 683/8878 fully gradable images [7.8%]); the Messidor-2 data set had 1748 images from 874 patients (mean age, 57.6 years; 42.6% women; prevalence of RDR, 254/1745 fully gradable images [14.6%]). For detecting RDR, the algorithm had an area under the receiver operating curve of 0.991 (95% CI, 0.988-0.993) for EyePACS-1 and 0.990 (95% CI, 0.986-0.995) for Messidor-2. Using the first operating cut point with high specificity, for EyePACS-1, the sensitivity was 90.3% (95% CI, 87.5%-92.7%) and the specificity was 98.1% (95% CI, 97.8%-98.5%). For Messidor-2, the sensitivity was 87.0% (95% CI, 81.1%-91.0%) and the specificity was 98.5% (95% CI, 97.7%-99.1%). Using a second operating point with high sensitivity in the development set, for EyePACS-1 the sensitivity was 97.5% and specificity was 93.4% and for Messidor-2 the sensitivity was 96.1% and specificity was 93.9%.
  
  
  
  In this evaluation of retinal fundus photographs from adults with diabetes, an algorithm based on deep machine learning had high sensitivity and specificity for detecting referable diabetic retinopathy. Further research is necessary to determine the feasibility of applying this algorithm in the clinical setting and to determine whether use of the algorithm could lead to improved care and outcomes compared with current ophthalmologic assessment.
authors.affiliation: Google Inc, Mountain View, California.
authors.forename: Varun
authors.initials: V
authors.lastname: Gulshan
authors.affiliation: Google Inc, Mountain View, California.
authors.forename: Lily
authors.initials: L
authors.lastname: Peng
authors.affiliation: Google Inc, Mountain View, California.
authors.forename: Marc
authors.initials: M
authors.lastname: Coram
authors.affiliation: Google Inc, Mountain View, California.
authors.forename: Martin C
authors.initials: MC
authors.lastname: Stumpe
authors.affiliation: Google Inc, Mountain View, California.
authors.forename: Derek
authors.initials: D
authors.lastname: Wu
authors.affiliation: Google Inc, Mountain View, California.
authors.forename: Arunachalam
authors.initials: A
authors.lastname: Narayanaswamy
authors.affiliation: Google Inc, Mountain View, California2Department of Computer Science, University of Texas, Austin.
authors.forename: Subhashini
authors.initials: S
authors.lastname: Venugopalan
authors.affiliation: Google Inc, Mountain View, California.
authors.forename: Kasumi
authors.initials: K
authors.lastname: Widner
authors.affiliation: Google Inc, Mountain View, California.
authors.forename: Tom
authors.initials: T
authors.lastname: Madams
authors.affiliation: EyePACS LLC, San Jose, California4School of Optometry, Vision Science Graduate Group, University of California, Berkeley.
authors.forename: Jorge
authors.initials: J
authors.lastname: Cuadros
authors.affiliation: Aravind Medical Research Foundation, Aravind Eye Care System, Madurai, India.
authors.forename: Ramasamy
authors.initials: R
authors.lastname: Kim
authors.affiliation: Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Chennai, Tamil Nadu, India.
authors.forename: Rajiv
authors.initials: R
authors.lastname: Raman
authors.affiliation: Google Inc, Mountain View, California.
authors.forename: Philip C
authors.initials: PC
authors.lastname: Nelson
authors.affiliation: Verily Life Sciences, Mountain View, California8Cardiovascular Division, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.
authors.forename: Jessica L
authors.initials: JL
authors.lastname: Mega
authors.affiliation: Google Inc, Mountain View, California.
authors.forename: Dale R
authors.initials: DR
authors.lastname: Webster
mesh_terms: D000465:Algorithms; D003930:Diabetic Retinopathy / Q000000981:diagnostic imaging*; D005260:Female; D005654:Fundus Oculi; D006801:Humans; D000069550:Machine Learning; D008269:Macular Edema / Q000000981:diagnostic imaging*; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D015588:Observer Variation; D000072163:Ophthalmologists; D010781:Photography; D012680:Sensitivity and Specificity