title: Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning.
abstract: OBJECTIVE
  To compare performance of a deep-learning enhanced algorithm for automated detection of diabetic retinopathy (DR), to the previously published performance of that algorithm, the Iowa Detection Program (IDP)-without deep learning components-on the same publicly available set of fundus images and previously reported consensus reference standard set, by three US Board certified retinal specialists.
  
  
  METHODS
  We used the previously reported consensus reference standard of referable DR (rDR), defined as International Clinical Classification of Diabetic Retinopathy moderate, severe nonproliferative (NPDR), proliferative DR, and/or macular edema (ME). Neither Messidor-2 images, nor the three retinal specialists setting the Messidor-2 reference standard were used for training IDx-DR version X2.1. Sensitivity, specificity, negative predictive value, area under the curve (AUC), and their confidence intervals (CIs) were calculated.
  
  
  RESULTS
  Sensitivity was 96.8% (95% CI: 93.3%-98.8%), specificity was 87.0% (95% CI: 84.2%-89.4%), with 6/874 false negatives, resulting in a negative predictive value of 99.0% (95% CI: 97.8%-99.6%). No cases of severe NPDR, PDR, or ME were missed. The AUC was 0.980 (95% CI: 0.968-0.992). Sensitivity was not statistically different from published IDP sensitivity, which had a CI of 94.4% to 99.3%, but specificity was significantly better than the published IDP specificity CI of 55.7% to 63.0%.
  
  
  CONCLUSIONS
  A deep-learning enhanced algorithm for the automated detection of DR, achieves significantly better performance than a previously reported, otherwise essentially identical, algorithm that does not employ deep learning. Deep learning enhanced algorithms have the potential to improve the efficiency of DR screening, and thereby to prevent visual loss and blindness from this devastating disease.
authors.affiliation: Department of Ophthalmology and Visual Sciences, University of Iowa Hospitals and Clinics, Iowa City, Iowa, United States 2Iowa City Veterans Affairs Medical Center, Iowa City, Iowa, United States 3IDx LLC, Iowa City, Iowa, United States.
authors.forename: Michael David
authors.initials: MD
authors.lastname: Abràmoff
authors.affiliation: Department of Biostatistics, College of Public Health, University of Iowa, Iowa City, Iowa, United States.
authors.forename: Yiyue
authors.initials: Y
authors.lastname: Lou
authors.affiliation: Service d' Ophtalmologie, Hôpital Lariboisière, APHP, Paris, France.
authors.forename: Ali
authors.initials: A
authors.lastname: Erginay
authors.affiliation: IDx LLC, Iowa City, Iowa, United States.
authors.forename: Warren
authors.initials: W
authors.lastname: Clarida
authors.affiliation: IDx LLC, Iowa City, Iowa, United States.
authors.forename: Ryan
authors.initials: R
authors.lastname: Amelon
authors.affiliation: Department of Ophthalmology and Visual Sciences, University of Iowa Hospitals and Clinics, Iowa City, Iowa, United States 3IDx LLC, Iowa City, Iowa, United States.
authors.forename: James C
authors.initials: JC
authors.lastname: Folk
authors.affiliation: IDx LLC, Iowa City, Iowa, United States.
authors.forename: Meindert
authors.initials: M
authors.lastname: Niemeijer
mesh_terms: D000465:Algorithms; D001331:Automation / Q000379:methods; D003930:Diabetic Retinopathy / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D003941:Diagnostic Techniques, Ophthalmological; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D000072163:Ophthalmologists / Q000193:education*; D012372:ROC Curve; D012160:Retina / Q000000981:diagnostic imaging*; D012189:Retrospective Studies