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

By Michael David Abràmoff, P. Lavin, M. Birch, Nilay Shah, J. Folk. 2018

1,414 citations recorded by the source corpus.

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

Subfields: Ophthalmology, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic Retinopathy (2020, background): “Prospective studies that involve evaluations of deep learning models within a clinical environment are beginning to emerge [1].”
Role of Artificial Intelligence Applications in Real-Life Clinical Practice: Systematic Review (2020, background): “…year APa (sensitivity, specificity, imageability rate) 10 primary care clinics (United States) 819 patientsObservational study (prospective) Abràmoff et al, 2018 [25] COb (reading time, mucosal break detection rate) The University of Tokyo Hospital (Japan) 6 physiciansExperimental study…”
Introducing Artificial Intelligence Training in Medical Education (2019, neutral): “A recently launched AI system for autonomous detection of diabetic retinopathy carries medical malpractice and liability insurance [40,41].”
Artificial intelligence as the next step towards precision pathology (2020, background): “Moreover, the US Drug and Food Administration (FDA) has approved a deep learning-based autonomous AI diagnostic system to detect diabetic retinopathy on the images of retinal fundus [18].”
Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review (2021, background): “13-16 Although several publications have raised concern about the methodological quality of prediction models developed with conventional statistical techniques, 6 17 18 a formal methodological and risk of bias assessment of supervised machine learning based prediction model studies across all…”
Digital technology, tele-medicine and artificial intelligence in ophthalmology: A global perspective (2020, background): “Abramoff et al., (2018) reported the first DL-enhanced algorithm for referable DR and VTDR, with a sensitivity of 87.2% and a specificity of 90.7% in detection of referable DR (worse than mild DR) with a gradability rate of 96.1%.”
With an eye to AI and autonomous diagnosis (2018, positive): “In this issue of npj Digital Medicine, Abramoff and colleagues report the findings from a prospective study that evaluates the performance of a diabetic retinopathy diagnostic system (IDx-DR) in a primary care setting.(1) This represents an important clinical milestone as, in April 2018, these resul”