Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices

Michael D Abràmoff; Philip T Lavin; Michele Birch; Nilay Shah; James C Folk

doi:10.1038/s41746-018-0040-6

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Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices

Journal article

Open access

Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices

Michael D Abràmoff, Philip T Lavin, Michele Birch, Nilay Shah and James C Folk

NPJ Digital Medicine, Vol.1(1), 39

2018

DOI: 10.1038/s41746-018-0040-6

PMCID: PMC6550188

PMID: 31304320

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Abstract

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 (OCT), by FPRC certified photographers, and FPRC grading of Early Treatment Diabetic Retinopathy Study Severity Scale (ETDRS) and Diabetic Macular Edema (DME). More than mild DR (mtmDR) was defined as ETDRS level 35 or higher, and/or DME, in at least one eye. AI system operators underwent a standardized training protocol before study start. Median age was 59 years (range, 22–84 years); among participants, 47.5% of participants were male; 16.1% were Hispanic, 83.3% not Hispanic; 28.6% African American and 63.4% were not; 198 (23.8%) had mtmDR. The AI system exceeded all pre-specified superiority endpoints at sensitivity of 87.2% (95% CI, 81.8–91.2%) (>85%), specificity of 90.7% (95% CI, 88.3–92.7%) (>82.5%), and imageability rate of 96.1% (95% CI, 94.6–97.3%), demonstrating AI’s ability to bring specialty-level diagnostics to primary care settings. Based on these results, FDA authorized the system for use by health care providers to detect more than mild DR and diabetic macular edema, making it, the first FDA authorized autonomous AI diagnostic system in any field of medicine, with the potential to help prevent vision loss in thousands of people with diabetes annually. ClinicalTrials.gov NCT02963441

Biomedical Engineering

Eye manifestations

Details

Title: Subtitle: Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices
Creators: Michael D Abràmoff - Iowa City, IA 52242 USA
Philip T Lavin - Boston Biostatistics Research Foundation, Inc., 3 Cahill Park Drive, Framingham, MA 01702 USA
Michele Birch - Charlotte, NC 28204 USA
Nilay Shah - 401 North Washington Street, Suite 700, Rockville, MD 20850 USA
James C Folk - IDx LLC, Coralville, IA 52241 USA
Resource Type: Journal article
Publication Details: NPJ Digital Medicine, Vol.1(1), 39
DOI: 10.1038/s41746-018-0040-6
PMID: 31304320
PMCID: PMC6550188
NLM abbreviation: NPJ Digit Med
ISSN: 2398-6352
eISSN: 2398-6352
Publisher: Nature Publishing Group UK; London
Grant note: ;
Language: English
Date published: 2018
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Ophthalmology and Visual Sciences
Record Identifier: 9983806385302771

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