The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
The Value of Automated Diabetic Retinopathy Screening with the EyeArt System: A Study of More Than 100,000 Consecutive Encounters from People with Diabetes
Open accessAbstract
Background: Current manual diabetic retinopathy (DR) screening using eye care experts cannot scale to screen the growing population of diabetes patients who are at risk for vision loss. EyeArt system is an automated, cloud-based artificial intelligence (AI) eye screening technology designed to easily detect referral-warranted DR immediately through automated analysis of patient's retinal images. Methods: This retrospective study assessed the diagnostic efficacy of the EyeArt system v2.0 analyzing 850,908 fundus images from 101,710 consecutive patient visits, collected from 404 primary care clinics. Presence or absence of referral-warranted DR (more than mild nonproliferative DR [NPDR]) was automatically detected by the EyeArt system for each patient encounter, and its performance was compared against a clinical reference standard of quality-assured grading by rigorously trained certified ophthalmologists and optometrists. Results: Of the 101,710 visits, 75.7% were nonreferable, 19.3% were referable to an eye care specialist, and in 5.0%, the DR level was unknown as per the clinical reference standard. EyeArt screening had 91.3% (95% confidence interval [CI]: 90.9–91.7) sensitivity and 91.1% (95% CI: 90.9–91.3) specificity. For 5446 encounters with potentially treatable DR (more than moderate NPDR and/or diabetic macular edema), the system provided a positive “refer” output to 5363 encounters achieving sensitivity of 98.5%. Conclusions: This study captures variations in real-world clinical practice and shows that an AI DR screening system can be safe and effective in the real world. This study demonstrates the value of this easy-to-use, automated tool for endocrinologists, diabetologists, and general practitioners to address the growing need for DR screening and monitoring.
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“In the screening and evaluation of diabetic complications, the screening of DR using retinal images was studied well in multi-ethnic cohorts, with comparable accuracy compared with professional graders (discussed in detail in the conclusion and perspective).103,104,105,106,107,108,109”
From Artificial intelligence in diabetes management: Advancements, opportunities, and challenges (2023) · cites this paper as [105]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“the system could achieve 91.3% sensitivity and 91.1% specificity for detecting referral-warranted DR (more than mild non-proliferative DR)”
From Artificial intelligence in diabetes management: Advancements, opportunities, and challenges (2023) · cites this paper as Bhaskaranand et al.105
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“Multiple artificial intelligence (AI) algorithms have been developed and validated for DR5–8 screening and it is the most frequently implemented deep learning application in prospective real-world studies9.”
From Referral uptake after diabetic retinopathy screening with artificial intelligence-assisted care pathways: a systematic review and meta-analysis (2026) · cites this paper as [5]
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“Artificial intelligence (AI) is an accurate screening tool for diabetic retinopathy (DR), the leading cause of blindness among working-aged adults.”
From Referral uptake after diabetic retinopathy screening with artificial intelligence-assisted care pathways: a systematic review and meta-analysis (2026) · cites this paper as [5]
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In the citing paper
“Based on the claims database in 2023, DR testing has become one of the most widely adopted and fastest-growing AI-aided medical procedures in the United States, only second to coronary artery disease.”
From Autonomous Artificial Intelligence in Diabetic Retinopathy Testing—Lessons Learned on Successful Health System Adoption (2026) · cites this paper as [21]
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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
Self-citationAbràmoff, 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
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JOLLIFFE, F. R., Gardner, M. J., & Altman, D. G. (1990) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.2307/2982808 · Cited by 1,319
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EyePACS: An Adaptable Telemedicine System for Diabetic Retinopathy Screening
Cuadros, J., & Bresnick, G. (2009) · Journal of Diabetes Science and Technology · doi:10.1177/193229680900300315 · Cited by 390 · cited 3×
“Repeat grading by a retinal subspecialist who is an expert in the EyePACS grading protocol may also be included.(17) A third limitation of this study is that data on the mean age of the patients or the mean duration of diabetes are unavailable to assess the performance of the EyeArt system, stratified on these factors.”
BackgroundMethodInfluentialSelf-citationvan der Heijden, A. A., Abramoff, M. D., Verbraak, F., van Hecke, M. V., Liem, A., & Nijpels, G. (2018) · Acta Ophthalmologica · doi:10.1111/aos.13613 · Cited by 296
Automated Early Detection of Diabetic Retinopathy
Abràmoff, M. D., Reinhardt, J. M., Russell, S. R., Folk, J. C., Mahajan, V. B., Niemeijer, M., & Quellec, G. (2010) · Ophthalmology · doi:10.1016/j.ophtha.2010.03.046 · Cited by 287
Automated Diabetic Retinopathy Image Assessment Software
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Self-citationAbràmoff, M. D., Niemeijer, M., Suttorp-Schulten, M. S. A., Viergever, M. A., Russell, S. R., & van Ginneken, B. (2008) · Diabetes Care · doi:10.2337/dc07-1312 · Cited by 277
Artificial Intelligence With Deep Learning Technology Looks Into Diabetic Retinopathy Screening
Wong, T. Y., & Bressler, N. M. (2016) · JAMA · doi:10.1001/jama.2016.17563 · Cited by 271
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Research works printed but not linked: 4
- National Eye Institute: Facts about diabetic eye disease. 2015. https://nei.nih.gov/health/diabetic/retinopathy (accessed November25 2018). · DOI
- Solanki K, , Ramachandra C, , Bhat S, et al.: EyeArt system: automated, high-throughput, image analysis for diabetic retinopathy screening. IOVS, 2015; 56. Abstract 1429.
- Simonyan K Zisserman A: Very Deep Convolutional Networks for Large-Scale Image Recognition. ArXiv 2014. http://arxiv.org/abs/1409.1556. · arXiv
- Solanki K, , Bhaskaranand M, , Bhat S, et al.: Comprehensive clinical validation study of an automated DR screening system against 7-field ETDRS stereoscopic reference standard. Am Acad Ophthalmol, 2016. Abstract 30049096.
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Items 201–211 of 211
Wu, Z., & Li, L. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1778534 · Cited by 1
Enhancing Diagnosis and Treatment of Retinal Diseases Through Artificial Intelligence
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Espaillat, A. (2026) · Springer eBooks · doi:10.1007/978-3-032-19336-0_7
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Autonomous AI for Diabetic Retinopathy Screening: Evidence, Regulation, and Health-System Fit
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Development of the ALEBON Surgical Complexity Index for OphthalmologyPreprint
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Metadata
- Authors
- Bhaskaranand, M., Ramachandra, C., Bhat, S., Cuadros, J., Nittala, M. G., Sadda, S. R., & Solanki, K.
- Affiliations
- Eyenuk (United States) (US)
- EyePACS LLC, San Jose, California.
- Doheny Eye Institute (US)
- Corresponding author
- Bhaskaranand, Malavika
- Venue
- Diabetes Technology & Therapeutics
- Publisher
- SAGE Publications
- Published
- 2019-10-21
- Volume
- 21
- Issue
- 11
- Pages
- 635-643
- ISSN
- 1520-9156 (ISSN-L)
- 1557-8593
- PMC
- PMC6812728
- OpenAlex
- W2963963432
- Cited by
- 211
- References
- 20
- Citations by year
- 2026: 25 · 2025: 37 · 2024: 31 · 2023: 41 · 2022: 24 · 2021: 30 · 2020: 23
- FWCI
- 12.73
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Diabetes Technology & Therapeutics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (R44TR000377)
- National Eye Institute
- National Institute of Biomedical Imaging and Bioengineering (R44EB013585)
- National Center for Advancing Translational Sciences
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
- Health Sciences › Medicine › Ophthalmology › Ophthalmology and Visual Health Research
- Health Sciences › Medicine › Ophthalmology › Retinal Diseases and Treatments
- Keywords
- diabetic retinopathy, automated retinal screening, artificial intelligence, fundus images, non-proliferative diabetic retinopathy, diabetic macular edema, primary care screening, diagnostic accuracy, blindness prevention
- MeSH terms
- Artificial Intelligence, Diabetic Retinopathy, Humans, Image Interpretation, Computer-Assisted, Macular Edema, Mass Screening, Middle Aged, Ophthalmology, Reference Standards, Retrospective Studies, Observer Variation
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