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


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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 (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

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          A Randomized, Placebo-Controlled, Clinical Trial of High-Dose Supplementation With Vitamins C and E, Beta Carotene, and Zinc for Age-Related Macular Degeneration and Vision Loss

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            Automated analysis of retinal images for detection of referable diabetic retinopathy.

            The diagnostic accuracy of computer detection programs has been reported to be comparable to that of specialists and expert readers, but no computer detection programs have been validated in an independent cohort using an internationally recognized diabetic retinopathy (DR) standard. To determine the sensitivity and specificity of the Iowa Detection Program (IDP) to detect referable diabetic retinopathy (RDR). In primary care DR clinics in France, from January 1, 2005, through December 31, 2010, patients were photographed consecutively, and retinal color images were graded for retinopathy severity according to the International Clinical Diabetic Retinopathy scale and macular edema by 3 masked independent retinal specialists and regraded with adjudication until consensus. The IDP analyzed the same images at a predetermined and fixed set point. We defined RDR as more than mild nonproliferative retinopathy and/or macular edema. A total of 874 people with diabetes at risk for DR. Sensitivity and specificity of the IDP to detect RDR, area under the receiver operating characteristic curve, sensitivity and specificity of the retinal specialists' readings, and mean interobserver difference (κ). The RDR prevalence was 21.7% (95% CI, 19.0%-24.5%). The IDP sensitivity was 96.8% (95% CI, 94.4%-99.3%) and specificity was 59.4% (95% CI, 55.7%-63.0%), corresponding to 6 of 874 false-negative results (none met treatment criteria). The area under the receiver operating characteristic curve was 0.937 (95% CI, 0.916-0.959). Before adjudication and consensus, the sensitivity/specificity of the retinal specialists were 0.80/0.98, 0.71/1.00, and 0.91/0.95, and the mean intergrader κ was 0.822. The IDP has high sensitivity and specificity to detect RDR. Computer analysis of retinal photographs for DR and automated detection of RDR can be implemented safely into the DR screening pipeline, potentially improving access to screening and health care productivity and reducing visual loss through early treatment.
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              Fundus Photographic Risk Factors for Progression of Diabetic Retinopathy


                Author and article information

                NPJ Digit Med
                NPJ Digit Med
                NPJ Digital Medicine
                Nature Publishing Group UK (London )
                28 August 2018
                28 August 2018
                : 1
                : 39
                [1 ]ISNI 0000 0004 1936 8294, GRID grid.214572.7, Department of Ophthalmology and Visual Sciences, , University of Iowa, ; Iowa City, IA 52242 USA
                [2 ]ISNI 0000 0004 0419 4535, GRID grid.484403.f, Veterans Administration Medical Center, ; Iowa City, IA 52242 USA
                [3 ]IDx LLC, Coralville, IA 52241 USA
                [4 ]ISNI 0000 0004 1936 8294, GRID grid.214572.7, Institute for Vision Research, , University of Iowa, ; Iowa City, IA 52242 USA
                [5 ]Boston Biostatistics Research Foundation, Inc., 3 Cahill Park Drive, Framingham, MA 01702 USA
                [6 ]ISNI 0000000122483208, GRID grid.10698.36, Department of Family Medicine, Director of Academic Services, , University of North Carolina School of Medicine, ; Charlotte, NC 28204 USA
                [7 ]ISNI 0000 0004 0459 5494, GRID grid.280434.9, The Emmes Corporation, ; 401 North Washington Street, Suite 700, Rockville, MD 20850 USA
                Author information
                © The Author(s) 2018

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                : 28 May 2018
                : 6 July 2018
                : 10 July 2018
                Funded by: IDx LLC, Coralville, IA
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                © The Author(s) 2018

                eye manifestations,biomedical engineering
                eye manifestations, biomedical engineering


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