Cited by 390Retinal Imaging and AnalysisOphthalmology and Visual Health ResearchRetinal Diseases and Treatments

The DOI matches its Crossref record; 26 of the 27 references it cites were found in a registry or an index.

EyePACS: An Adaptable Telemedicine System for Diabetic Retinopathy Screening

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Cuadros, Jorge, Bresnick, George·2009·Journal of Diabetes Science and Technology

DOI 10.1177/193229680900300315PMC PMC2769884

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Abstract

Background: Annual retinal screening of patients with diabetes is the standard clinical practice to prevent visual impairment and blindness from diabetic retinopathy. Telemedicine-based diabetic retinopathy screening (DRS) in primary care settings can effectively detect sight-threatening retinopathy and significantly increase compliance with annual retinal exams. EyePACS is a license-free Web-based DRS system designed to simplify the process of image capture, transmission, and review. The system provides a flexible platform for collaboration among clinicians about diabetic retinopathy. Methods: Primary clinic personnel (i.e., nursing, technical, or administrative staff) are trained and certified by the EyePACS program to acquire retinal images from standard digital retinal cameras. Relevant clinical data and eight high-resolution images per patient (two external and six retinal images) are encrypted and transmitted to a secure Internet server, using a standard computer and Web browser. Images are then interpreted by certified EyePACS reviewers or local eye care providers who are certified through the EyePACS Retinopathy Grading System. Reports indicating retinopathy level and referral recommendations are transmitted back to primary care providers through the EyePACS Web site or through interfaces between EyePACS and Health Level 7-compliant electronic medical records or chronic disease registries. Results: The pilot phase of the EyePACS DRS program in California (2005–2006) recorded 3562 encounters. Since 2006, EyePACS has been expanded to over 120 primary care sites throughout California and elsewhere recording over 34,000 DRSs. The overall rate of referral is 8.21% for sight-threatening retinopathy and 7.83% for other conditions (e.g., cataract and glaucoma). Conclusion: The use of license-free Web-based software, standard interfaces, and flexible protocols has allowed primary care providers to adopt retinopathy screening with minimal effort and resources.

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“To ensure robust feature learning, the primary training corpus was constructed from the EyePACS dataset, a large-scale (N > 88,000) and highly heterogeneous cohort that provides the essential structural variance required to train stable unsupervised representations [15].”
Diffusion Autoencoder for Unsupervised Artifact Restoration in Handheld Fundus Images
Palani, M., Puthumana, K., Das, A., et al. (2026) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi61048.2026.11515784
“Dataset Details And Evaluation Metrics : Train Set : We have utilized healthy fundus images from the EyePACS [15] dataset to train our model.”
GAN-Assisted Triple-Branch Swin Transformer for Diabetic Retinopathy Grading
Wang, B., Sun, X., & Gong, Z. (2026) · International Conference on Software Engineering and Computer Science (CSECS) · doi:10.1109/csecs69124.2026.11541759
“Similar imbalance patterns have been reported in several widely used real-world DR datasets, such as EyePACS [13], Messidor [14], IDRiD [15] and DDR [16], where severe and proliferative DR samples are consistently underrepresented.”
RetinaNeXt: ConvNeXt-Driven Deep Learning for Diabetic Retinopathy Severity Grading
Taheri, S., & Golrizkhatami, Z. (2026) · Sakarya University Journal of Computer and Information Sciences · doi:10.35377/saucis...1760380
Research progress on optical imaging and artificial intelligence assessment technologies for diabetic retinopathy
Zhao, Z., Zhang, Z., Wang, X., Wang, L., Xu, N., Ma, Z., Dun, X., Sun, T., Wang, Z., & Cheng, X. (2026) · Chinese Science Bulletin (Chinese Version) · doi:10.1360/csb-2026-0311
COA-Tuning: Collaborative Adapter Tuning with Prompt Enhancement for Class-Incremental Medical Image Learning
Wang, S., Song, J., Yang, Z., et al. (2026) · ACM Transactions on Computing for Healthcare · doi:10.1145/3821635
“Due to limited access to EyePACS [5], we adopt APTOS-2019 as a substitute for retinal disease classification.”
Morphological Transition Flow-based Feature Extraction for Five-Class Diabetic Retinopathy Classification
Esserkassi, B., Eddarouich, S., & Bourouhou, A. (2026) · Journal of Innovative Image Processing · doi:10.36548/jiip.2026.3.004
“Experiments employed the EyePACS dataset [15, 16], a publicly available collection of 34,860 fundus photographs acquired between 2013 and 2015 through the EyePACS telemedicine screening program [15] across geographically distributed clinical sites in the United States.”
DiffGeo-AOR: Diffusion-Optimized Medical Grading via Geometric Priors Enhanced Autoregressive Ordinal Regression
Yu, Q., Zhao, H., Xu, Y., Wang, M., Zhao, Y., Fu, H., Ye, X., Villavicencio, A., Lip, G. Y. H., Zheng, Y., et al. (2026) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2026.3710844
“EyeQ: Eye-Quality (EyeQ) Assessment Dataset [3] is a re-annotated subset of the EyePACS dataset [47] designed for fundus image quality assessment.”
Vision Transformers for Diabetic Retinopathy Severity: An Empirical Study of Preprocessing and Image Resolution
Tran, A. C., & Tran, N. C. (2026) · Procedia Computer Science · doi:10.1016/j.procs.2026.05.127
Artificial Intelligence for the Eye
Antony, B. J., & Mareels, I. (2026) · Springer eBooks · doi:10.1007/978-3-032-11023-7_7
Leading Towards a Translation Readiness Framework: A Systematic Review of Deep Learning Approaches for Eye Disease Diagnosis
Ali, U., Imam, A. A., & Apong, R. A. A. H. M. (2026) · Diagnostics · doi:10.3390/diagnostics16162500
“It usually starts with collecting image data, either from hospitals or public datasets such as EyePACS and APTOS [23,24].”
MMCAT-Net: multi-angle multimodal co-attention transformer for automated five-level nuclear cataract grading using AS-OCT and retinal OCT images
Han, S., Pei, Y., Lu, S., et al. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.111341
NUSAN: Medical Data Cleaning to Improve Class Separation with Minimal Structural Changes
Huynh, C. P. N., & Weimer, J. (2026) · IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies · doi:10.1109/chase69719.2026.00016
“EyePACS [32] is a large retinal fundus imaging dataset used for diabetic retinopathy screening.”
The German National Cohort: Ophthalmic Assessment, Baseline Profile, and Potential for Artificial Intelligence–Based Eye Research
Roa, C., Beuse, A., Schweig, A., et al. (2026) · Ophthalmology Science · doi:10.1016/j.xops.2026.101408
“44 For instance, Poplin et al 24 trained an Inception-v3 model on subsets of the UKB and the EyePACS data set 45 to predict cardiovascular risk factors, including sex, age, and blood pressure.”
Class balanced diabetic retinopathy image synthesis using a latent diffusion framework
Alam, T., Liew, T. H., Morol, M. K., et al. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-02184-1
“For instance, in the widely used EyePACS dataset [11], the ’No DR’ class constitutes approximately 73% of the total images, while the critical ’Severe’ and ’Proliferative’ classes combined account for less than 6% of the data.”
Ordinal Diffusion Models for Color Fundus Images
Schmidt, G., Berens, P., & Müller, S. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-38189-7_33
Real-world validation of an AI-based non-mydriatic fundus camera in the diagnosis of diabetic retinopathy in Liberia
Sam, V. F., Rizwan, M., Douglas, J. C., et al. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1938702
“The study by Varun Gulshan et al. (19), conducted as an in-clinic retrospective study, reported that in the EyePACS-1 dataset, the sensitivity was 97.5% and the specificity was 93.4%.”
Influential
From Retina to Cognition: Transfer Learning for MMSE-Derived Cognitive Status Classification in Diabetic Retinopathy
Alves, D. C., Jiménez, F. J. S., Cantón-Habas, V., Carrera-González, M. P., Jiménez, J. L. Á., & Ferrari, R. J. (2026) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms69103.2026.00160
“…contributions: • We propose a three-stage transfer learning pipeline using a ResNet-50 convolutional neural network backbone [8], comprising (i) ImageNet pretraining, (ii) retinal domain adaptation using EyePACS [9], and (iii) embedding-based cognitive status classification in a DR cohort.”
FDR26: A Fundus Dataset for Diabetic Retinopathy
Lamti, S., Kerzazi, N., Sekhsoukh, R., & Guechati, A. (2026) · Data in Brief · doi:10.1016/j.dib.2026.113379

Authors: Cuadros, J., & Bresnick, G.
Affiliations: University of California, Berkeley (US)
Corresponding author: Cuadros, Jorge
ORCID: Cuadros, Jorge
Bresnick, George
Venue: Journal of Diabetes Science and Technology
Publisher: SAGE Publications
Published: 2009-05
Volume: 3
Issue: 3
Pages: 509-516
ISSN: 1932-2968 (ISSN-L)
DOI: 10.1177/193229680900300315
PMC: PMC2769884
OpenAlex: W2115531174
Cited by: 390
References: 27
Citations by year: 2026: 38 · 2025: 30 · 2024: 48 · 2023: 53 · 2022: 47 · 2021: 43 · 2020: 38 · 2019: 19 · 2018: 17 · 2017: 6 · 2016: 11 · 2015: 11 · 2014: 6 · 2013: 7 · 2012: 5
FWCI: 4.03
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
Locations: Journal of Diabetes Science and Technology (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
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 screening, telemedicine, EyePACS, primary care, retinopathy grading, referral rates, electronic health record integration, Health Level 7, chronic disease registry
MeSH terms: Diabetic Retinopathy, Humans, Mass Screening, Referral and Consultation, Retrospective Studies, Severity of Illness Index, Software, Telemedicine, Remote Consultation, Internet, Electronic Health Records

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EyePACS: An Adaptable Telemedicine System for Diabetic Retinopathy Screening

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