Using a Deep Learning Algorithm and Integrated Gradients Explanation to Assist Grading for Diabetic Retinopathy.

Journal: Ophthalmology

Published Date: Apr 1, 2019

PURPOSE: To understand the impact of deep learning diabetic retinopathy (DR) algorithms on physician readers in computer-assisted settings.

Rory Sayres

Google Research, Google, LLC, Mountain View, California.
Ankur Taly

Google Research, Google, LLC, Mountain View, California.
Ehsan Rahimy

Department of Ophthalmology, Palo Alto Medical Foundation, Palo Alto, California.
Katy Blumer

Google Research, Google, Mountain View, CA, USA.
David Coz

Google Research, Google, LLC, Mountain View, California.
Naama Hammel

Google Research, Google, LLC, Mountain View, California.
Jonathan Krause

Artificial Intelligence Laboratory, Computer Science Department, Stanford University, Stanford, CA 94305.
Arunachalam Narayanaswamy

Google Inc, Mountain View, California.
Zahra Rastegar

Google Research, Google, LLC, Mountain View, California.
Derek Wu

Google Inc, Mountain View, California.
Shawn Xu

Verily Life Sciences, South San Francisco, California.
Scott Barb

Department of Ophthalmology, Emory University, Atlanta, Georgia.
Anthony Joseph

Ophthalmic Consultants of Boston, Boston, Massachusetts.
Michael Shumski

Magruder Laser Vision, Orlando, Florida.
Jesse Smith

Denver Health Medical Center, Denver, Colorado; Department of Ophthalmology, University of Colorado School of Medicine, Aurora, Colorado.
Arjun B Sood

Department of Ophthalmology, Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston, Massachusetts.
Greg S Corrado

Google Health, Palo Alto, CA USA.
Lily Peng

Google Inc, Mountain View, California.
Dale R Webster

Google Inc, Mountain View, California.

Algorithms Deep Learning Diabetic Retinopathy Diagnosis, Computer-Assisted Female Humans Male Ophthalmologists Photography Reference Standards Reproducibility of Results ROC Curve Sensitivity and Specificity

View on PubMed Access via DOI PubMed (30553900)