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

Investigative Ophthalmology & Visual Science · 2016 · Vol. 57(13) · pp. 5200–5200

Michael D. Abràmoff✉(University of Iowa Hospitals and Clinics)Yiyue Lou(University of Iowa)Ali Erginay(Assistance Publique – Hôpitaux de Paris)Warren ClaridaRyan AmelonJames C. Folk(University of Iowa Hospitals and Clinics)Meindert Niemeijer

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Abstract

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.

Retinal Imaging and Analysis Retinal Diseases and Treatments Acute Ischemic Stroke ManagementMedicineDiabetic retinopathyArtificial intelligenceFundus (uterus)Deep learningConfidence intervalPredictive valueMacular edemaRetinalOphthalmology

MeSH terms

OphthalmologistsAlgorithmsAutomationDiabetic RetinopathyDiagnosis, Computer-AssistedDiagnostic Techniques, OphthalmologicalFemaleFollow-Up StudiesHumansMaleMiddle AgedRetinaRetrospective StudiesROC CurveNeural Networks, Computer

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