Deep Learning Predicts OCT Measures of Diabetic Macular Thickening From Color Fundus Photographs.

Journal: Investigative ophthalmology & visual science

PMID: 30821810

Abstract

PURPOSE: To develop deep learning (DL) models for the automatic detection of optical coherence tomography (OCT) measures of diabetic macular thickening (MT) from color fundus photographs (CFPs).

Authors

Filippo Arcadu

Pharma Research and Early Development (pRED), Roche Innovation Center Basel, Basel, Switzerland.
Fethallah Benmansour

Pharma Research and Early Development (pRED), Roche Innovation Center Basel, Basel, Switzerland.
Andreas Maunz

Pharma Research and Early Development (pRED), Roche Innovation Center Basel, Basel, Switzerland.
John Michon

Genentech, Inc., San Francisco, California, United States.
Zdenka Haskova

Genentech, Inc., San Francisco, California, United States.
Dana McClintock

Genentech, Inc., San Francisco, California, United States.
Anthony P Adamis

Genentech, Inc., San Francisco, California, United States.
Jeffrey R Willis

Genentech, Inc., San Francisco, California, United States.
Marco Prunotto

Pharma Research and Early Development (pRED), Roche Innovation Center Basel, Basel, Switzerland.

Keywords

Angiogenesis Inhibitors Deep Learning Diabetic Retinopathy Diagnostic Techniques, Ophthalmological False Positive Reactions Female Fundus Oculi Humans Image Interpretation, Computer-Assisted Intravitreal Injections Macula Lutea Macular Edema Male Middle Aged Neural Networks, Computer Photography Predictive Value of Tests Randomized Controlled Trials as Topic Ranibizumab Retrospective Studies Sensitivity and Specificity Tomography, Optical Coherence Vascular Endothelial Growth Factor A

External Resources

View on PubMed Access via DOI PubMed (30821810)

Deep Learning Predicts OCT Measures of Diabetic Macular Thickening From Color Fundus Photographs.

Abstract

Authors

Keywords

External Resources

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