Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning.

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Abstract

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.

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Journal

Journal ID (iso-abbrev): Invest. Ophthalmol. Vis. Sci.

Title: Investigative ophthalmology & visual science

Publisher: Association for Research in Vision and Ophthalmology (ARVO)

ISSN (Electronic): 1552-5783

ISSN (Print): 0146-0404

Publication date (Electronic): Oct 01 2016

Volume: 57

Issue: 13

Affiliations

[1 ] 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.

[2 ] Department of Biostatistics, College of Public Health, University of Iowa, Iowa City, Iowa, United States.

[3 ] Service d' Ophtalmologie, Hôpital Lariboisière, APHP, Paris, France.

[4 ] IDx LLC, Iowa City, Iowa, United States.

[5 ] Department of Ophthalmology and Visual Sciences, University of Iowa Hospitals and Clinics, Iowa City, Iowa, United States 3IDx LLC, Iowa City, Iowa, United States.

Article

Publisher Item ID: 2565719

DOI: 10.1167/iovs.16-19964

PubMed ID: 27701631

SO-VID: 09debdb3-f2f1-41c3-8c89-10e69d964a19

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