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

Filippo Arcadu | Zendy; Fethallah Benmansour | Zendy; Andreas Maunz | Zendy; John Michon | Zendy; Zdenka Hašková | Zendy; Dana McClintock | Zendy; Anthony P. Adamis | Zendy; Jeffrey R. Willis | Zendy; Marco Prunotto | Zendy

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

Author(s) -

Filippo Arcadu,

Fethallah Benmansour,

Andreas Maunz,

John Michon,

Zdenka Hašková,

Dana McClintock,

Anthony P. Adamis,

Jeffrey R. Willis,

Marco Prunotto

Publication year - 2019

Publication title -

investigative ophthalmology and visual science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.935

H-Index - 218

eISSN - 1552-5783

pISSN - 0146-0404

DOI - 10.1167/iovs.18-25634

Subject(s) - optical coherence tomography , medicine , confidence interval , receiver operating characteristic , ophthalmology , fundus (uterus) , diabetic macular edema , cutoff , artificial intelligence , nuclear medicine , diabetic retinopathy , computer science , diabetes mellitus , physics , quantum mechanics , endocrinology

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).

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