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Michael D. Abràmoff
University of Iowa
398Publications
48H-index
17.4kCitations
Publications 398
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#1Elliott H. Sohn (UI: University of Iowa)H-Index: 22
#2Ian C. Han (UI: University of Iowa)H-Index: 10
Last.Michael D. Abràmoff (UI: University of Iowa)H-Index: 48
view all 3 authors...
#1Andrzej GrzybowskiH-Index: 21
#2Piotr BronaH-Index: 2
Last.Daniel S. W. Ting (NUS: National University of Singapore)H-Index: 2
view all 7 authors...
Diabetes is a global eye health issue. Given the rising in diabetes prevalence and ageing population, this poses significant challenge to perform diabetic retinopathy (DR) screening for these patients. Artificial intelligence (AI) using machine learning and deep learning have been adopted by various groups to develop automated DR detection algorithms. This article aims to describe the state-of-art AI DR screening technologies that have been described in the literature, some of which are already ...
#1Daniel Shu Wei Ting (NUS: National University of Singapore)H-Index: 8
#2Lawrence Carin (Duke University)H-Index: 59
Last.Michael D. Abràmoff (UI: University of Iowa)H-Index: 48
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Abstract The advent of computer graphic processing units, improvement in mathematical models and availability of big data has allowed artificial intelligence (AI) using machine learning (ML) and deep learning (DL) techniques to achieve robust performance for broad applications in social-media, the internet of things, the automotive industry and healthcare. DL systems in particular provide improved capability in image, speech and motion recognition as well as in natural language processing. In me...
Early age-related macular degeneration (AMD) is characterized by degeneration of the choriocapillaris, the vascular supply of retinal photoreceptor cells. We assessed vascular loss during disease progression in the choriocapillaris and larger vessels in the deeper choroid. Human donor maculae from controls ( n = 99), early AMD ( n = 35), or clinically diagnosed with geographic atrophy (GA; n = 9, collected from outside the zone of retinal pigment epithelium degeneration) were evaluated using Ule...
#1Ramon Pires (State University of Campinas)H-Index: 8
#2Sandra Eliza Fontes de Avila (State University of Campinas)H-Index: 12
Last.Anderson Rocha (State University of Campinas)H-Index: 23
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Abstract Prior art on automated screening of diabetic retinopathy and direct referral decision shows promising performance; yet most methods build upon complex hand-crafted features whose performance often fails to generalize. Objective We investigate data-driven approaches that extract powerful abstract representations directly from retinal images to provide a reliable referable diabetic retinopathy detector. Methods We gradually build the solution based on convolutional neural networks, adding...
#1Frank D. VerbraakH-Index: 23
#2Michael D. Abràmoff (UI: University of Iowa)H-Index: 48
Last.Amber A. van der Heijden (VUmc: VU University Medical Center)H-Index: 8
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OBJECTIVE To determine the diagnostic accuracy in a real-world primary care setting of a deep learning–enhanced device for automated detection of diabetic retinopathy (DR). RESEARCH DESIGN AND METHODS Retinal images of people with type 2 diabetes visiting a primary care screening program were graded by a hybrid deep learning–enhanced device (IDx-DR-EU-2.1; IDx, Amsterdam, the Netherlands), and its classification of retinopathy (vision-threatening [vt]DR, more than mild [mtm]DR, and mild or more ...
#2Philip LavinH-Index: 1
Last.James C. Folk (UI: University of Iowa)H-Index: 39
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Artificial Intelligence (AI) has long promised to increase healthcare affordability, quality and accessibility but FDA, until recently, had never authorized an autonomous AI diagnostic system. This pivotal trial of an AI system to detect diabetic retinopathy (DR) in people with diabetes enrolled 900 subjects, with no history of DR at primary care clinics, by comparing to Wisconsin Fundus Photograph Reading Center (FPRC) widefield stereoscopic photography and macular Optical Coherence Tomography ...
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