Cited by 256Retinal Imaging and AnalysisRetinal Diseases and TreatmentsOphthalmology and Visual Health Research

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Pivotal Evaluation of an Artificial Intelligence System for Autonomous Detection of Referrable and Vision-Threatening Diabetic Retinopathy

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Ipp, Eli, Liljenquist, David, Bode, Bruce, Shah, Viral N., Silverstein, Steven, Regillo, Carl D., Lim, Jennifer I., Sadda, SriniVas, Domalpally, Amitha, Gray, Gerry, Bhaskaranand, Malavika, Ramachandra, Chaithanya, Solanki, Kaushal, EyeArt Study Group, DuBiner, Harvey B., Genter, Pauline, Graham, Joyce, Johnson, Alan, Levy-Clarke, Grace, Pesavento, Richard D., Sherman, Mark D., Kim, Brian T., Walman, Gerald B., Akturk, Halis K., Joseph, Hal, Joshee, Prakriti, Trippe, Bruce, Gilbert, John M., Blodi, Barbara A., Reed, Susan, Reimers, James, Lang, Kris, Cohn, Holly, Shaw, Ruth, Watson, Sheila, Ewen, Andrew, Barrett, Nancy, Swift, Maria, Gornbein, Jeffrey·2021·JAMA Network Open

DOI 10.1001/jamanetworkopen.2021.34254PMC PMC8593763

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Abstract

Importance: Diabetic retinopathy (DR) is a leading cause of blindness in adults worldwide. Early detection and intervention can prevent blindness; however, many patients do not receive their recommended annual diabetic eye examinations, primarily owing to limited access. Objective: To evaluate the safety and accuracy of an artificial intelligence (AI) system (the EyeArt Automated DR Detection System, version 2.1.0) in detecting both more-than-mild diabetic retinopathy (mtmDR) and vision-threatening diabetic retinopathy (vtDR). Design, Setting, and Participants: A prospective multicenter cross-sectional diagnostic study was preregistered (NCT03112005) and conducted from April 17, 2017, to May 30, 2018. A total of 942 individuals aged 18 years or older who had diabetes gave consent to participate at 15 primary care and eye care facilities. Data analysis was performed from February 14 to July 10, 2019. Interventions: Retinal imaging for the autonomous AI system and Early Treatment Diabetic Retinopathy Study (ETDRS) reference standard determination. Main Outcomes and Measures: Primary outcome measures included the sensitivity and specificity of the AI system in identifying participants' eyes with mtmDR and/or vtDR by 2-field undilated fundus photography vs a rigorous clinical reference standard comprising reading center grading of 4 wide-field dilated images using the ETDRS severity scale. Secondary outcome measures included the evaluation of imageability, dilated-if-needed analysis, enrichment correction analysis, worst-case imputation, and safety outcomes. Results: Of 942 consenting individuals, 893 patients (1786 eyes) met the inclusion criteria and completed the study protocol. The population included 449 men (50.3%). Mean (SD) participant age was 53.9 (15.2) years (median, 56; range, 18-88 years), 655 were White (73.3%), and 206 had type 1 diabetes (23.1%). Sensitivity and specificity of the AI system were high in detecting mtmDR (sensitivity: 95.5%; 95% CI, 92.4%-98.5% and specificity: 85.0%; 95% CI, 82.6%-87.4%) and vtDR (sensitivity: 95.1%; 95% CI, 90.1%-100% and specificity: 89.0%; 95% CI, 87.0%-91.1%) without dilation. Imageability was high without dilation, with the AI system able to grade 87.4% (95% CI, 85.2%-89.6%) of the eyes with reading center grades. When eyes with ungradable results were dilated per the protocol, the imageability improved to 97.4% (95% CI, 96.4%-98.5%), with the sensitivity and specificity being similar. After correcting for enrichment, the mtmDR specificity increased to 87.8% (95% CI, 86.3%-89.5%) and the sensitivity remained similar; for vtDR, both sensitivity (97.0%; 95% CI, 91.2%-100%) and specificity (90.1%; 95% CI, 89.4%-91.5%) improved. Conclusions and Relevance: This prospective multicenter cross-sectional diagnostic study noted safety and accuracy with use of the EyeArt Automated DR Detection System in detecting both mtmDR and, for the first time, vtDR, without physician assistance. These findings suggest that improved access to accurate, reliable diabetic eye examinations may increase adherence to recommended annual screenings and allow for accelerated referral of patients identified as having vtDR.

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Figure 1. Study Procedures in the Prospective Multicenter Cross-Sectional Diagnostic Study of the EyeArt Automated Diabetic Retinopathy Detection System The clinical reference standard was determined using 4-wide field stereoscopic dilated fundus photographs. The retinal coverage of the four 45-degree field of view images is equivalent to that of 7-field Early Treatment Diabetic Retinopathy Study images (30-degree field of view). Only 1 photograph from each stereo pair for the 4 retinal fields and anterior view is shown. R indicates right eye; L, left eye; ANT, anterior. 1W, 2W, 4W, and 5W are the nasal, central, superior, and inferior fields of the 4-wide field photography protocol, respectively. EyeArt is an artificial intelligence system for autonomous detection of more-than-mild diabetic retinopathy and vision-threatening diabetic retinopathy.CC BY-NC-ND · PubMed Central

Figure 2. Flow Diagram for Participant Disposition in the Prospective Multicenter Cross-Sectional Diagnostic Study of the EyeArt Automated Diabetic Retinopathy Detection System Final disposition of participants included in more-than-mild diabetic retinopathy (mtmDR) (A) and vision-threatening diabetic retinopathy (vtDR) (B) analyses.AI indicates artificial intelligence.CC BY-NC-ND · PubMed Central

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Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs
Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., et al. (2016) · JAMA · doi:10.1001/jama.2016.17216 · Cited by 7,697
IDF Diabetes Atlas: Global estimates for the prevalence of diabetes for 2015 and 2040
Ogurtsova, K., Fernandes, J. D. D. R., Huang, Y., Linnenkamp, U., Guariguata, L., Cho, N. H., Cavan, D. A., Shaw, J. E., & Makaroff, L. (2017) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2017.03.024 · Cited by 7,138
Global prevalence and major risk factors of diabetic retinopathy
Yau, J. W. Y., Rogers, S. L., Kawasaki, R., Lamoureux, E. L., Kowalski, J. W., Bek, T., Chen, S.-J., Dekker, J. M., Fletcher, A., Grauslund, J., et al. (2012) · Diabetes Care · doi:10.2337/dc11-1909 · Cited by 4,985
WORLD MEDICAL ASSOCIATION DECLARATION OF HELSINKI: Ethical Principles for Medical Research Involving Human Subjects
(2001) · Journal of obstetrics and gynaecology research · doi:10.1111/j.1447-0756.2001.tb01222.x · Cited by 3,515
Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes
Ting, D. S. W., Cheung, C. Y.-L., Lim, G., Tan, G. S. W., Quang, N. D., Gan, A., Hamzah, H., Garcia-Franco, R., San Yeo, I. Y., Lee, S. Y., et al. (2017) · JAMA · doi:10.1001/jama.2017.18152 · Cited by 2,402
Grading Diabetic Retinopathy from Stereoscopic Color Fundus Photographs—An Extension of the Modified Airlie House Classification
* (1991) · Ophthalmology · doi:10.1016/s0161-6420(13)38012-9 · Cited by 2,086
Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices
Abràmoff, M. D., Lavin, P. T., Birch, M., Shah, N., & Folk, J. C. (2018) · npj Digital Medicine · doi:10.1038/s41746-018-0040-6 · Cited by 1,638
Automated Identification of Diabetic Retinopathy Using Deep Learning
Gargeya, R., & Leng, T. (2017) · Ophthalmology · doi:10.1016/j.ophtha.2017.02.008 · Cited by 1,410
Untitled work
Higgins (2002) · doi:10.1002/(issn)1097-0258 · Cited by 1,248
Grading diabetic retinopathy from stereoscopic color fundus photographs--an extension of the modified Airlie House classification. ETDRS report number 10. Early Treatment Diabetic Retinopathy Study Research Group.
(1991) · PubMed · Cited by 1,240

Show all 29 references

Research works printed but not linked: 1

Centers for Disease Control and Prevention, 2018, Diabetes Report Card 2017

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Authors: Ipp, E., Liljenquist, D., Bode, B., Shah, V. N., Silverstein, S., Regillo, C. D., Lim, J. I., Sadda, S., Domalpally, A., Gray, G., Bhaskaranand, M., Ramachandra, C., Solanki, K., EyeArt Study Group, DuBiner, H. B., Genter, P., Graham, J., Johnson, A., Levy-Clarke, G., Pesavento, R. D., Sherman, M. D., Kim, B. T., Walman, G. B., Akturk, H. K., Joseph, H., Joshee, P., Trippe, B., Gilbert, J. M., Blodi, B. A., Reed, S., Reimers, J., Lang, K., Cohn, H., Shaw, R., Watson, S., Ewen, A., Barrett, N., Swift, M., & Gornbein, J.
Affiliations: University of California, Los Angeles (US)
Rocky Mountain Diabetes and Osteoporosis Center (US)
Atlanta Diabetes Associates (US)
University of Colorado Anschutz (US)
Kansas City Public Schools (US)
Thomas Jefferson University (US)
University of Illinois Chicago (US)
Doheny Eye Center, Arcadia, California
University of Wisconsin–Madison (US)
Data-Fi, LLC, Silver Spring, Maryland
Eyenuk (United States) (US)
for the EyeArt Study Group
Corresponding author: Ipp, Eli
ORCID: Ipp, Eli
Bode, Bruce
Shah, Viral N.
Silverstein, Steven
Regillo, Carl D.
Lim, Jennifer I.
Sadda, SriniVas
Domalpally, Amitha
Solanki, Kaushal
Genter, Pauline
Levy-Clarke, Grace
Kim, Brian T.
Barrett, Nancy
Gornbein, Jeffrey
Venue: JAMA Network Open
Publisher: American Medical Association (AMA)
Published: 2021-11-15
Volume: 4
Issue: 11
Pages: e2134254
ISSN: 2574-3805 (ISSN-L)
DOI: 10.1001/jamanetworkopen.2021.34254
PMC: PMC8593763
OpenAlex: W3213686666
Cited by: 256
References: 29
Citations by year: 2026: 62 · 2025: 64 · 2024: 60 · 2023: 49 · 2022: 19 · 2021: 2
FWCI: 17.17
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by-nc-nd
Locations: JAMA Network Open (publishedVersion, cc-by-nc-nd)
PubMed Central (publishedVersion, cc-by-nc-nd)
PubMed (publishedVersion)
Funders: University of Wisconsin-Madison
Topics: Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
Health Sciences › Medicine › Ophthalmology › Retinal Diseases and Treatments
Health Sciences › Medicine › Ophthalmology › Ophthalmology and Visual Health Research
Keywords: diabetic retinopathy, artificial intelligence, automated retinal analysis, vision-threatening diabetic retinopathy, retinal fundus photography, Early Treatment Diabetic Retinopathy Study, diagnostic accuracy, sensitivity and specificity, imageability, blindness prevention, primary care
MeSH terms: Adolescent, Adult, Aged, Aged, 80 and over, Artificial Intelligence, Cross-Sectional Studies, Diabetic Retinopathy, Female, Humans, Male, Middle Aged, Prospective Studies, Reference Standards, Referral and Consultation, Sensitivity and Specificity, Vision Disorders, Vision Screening, Young Adult

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Pivotal Evaluation of an Artificial Intelligence System for Autonomous Detection of Referrable and Vision-Threatening Diabetic Retinopathy

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