EyePACS: An Adaptable Telemedicine System for Diabetic Retinopathy Screening
Open accessAbstract
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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The development of an open-source system, EyePACS, supporting diabetic retinopathy and other ocular disorders on a variety of platforms, has encouraged widespread adoption of the telemedicine approach.
“The development of an open-source system, EyePACS [55], supporting diabetic retinopathy as well as other ocular disorders on a variety of platforms, has encouraged widespread adoption of the telemedicine approach [56].”
From Recent directions in telemedicine: Review of trends in research and practice (2015) · cites this paper as [56]
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“We derived our predictive algorithm from a data set of 75 137 color fundus images obtained from the EyePACS public data set (EyePACS LLC, Berkeley, CA).(17) The images represented a heterogeneous cohort of patients with all stages of DR.”
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“LAT [260] Eye Fundus 2D Messidor-1 [261] Messidor-2 [261] EyePACS [262] AuC Kappa Hybrid Formulate lesion discovery as a weakly supervised lesion localization problem via a transformer decoder.”
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“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.”
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“Kaggle Dataset: The dataset [30] provided by EyePACS [29] contains 88,702 color fundus images of which 80% were used for training and 20% for validation.”
MethodInfluentialInterpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks
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“In order to contribute to research for automated diabetic retinopathy detection (DRD) algorithms, the California Healthcare Foundation and EyePACS created a public dataset consisting of 35,126 RGB fundus images [37,38].”
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“…is that large datasets of images have been collected within DR screening programs for diabetic patients over the past decades (Massin et al., 2008; Cuadros and Bresnick, 2009): those images were interpreted by human readers, which allows e ffi - cient training of supervised deep learning classifiers.”
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“We also used existing retinal images from the EyePACS dataset [29] to train models.”
MethodExplAIn: Explanatory artificial intelligence for diabetic retinopathy diagnosis
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“tion map is thus available per lesion type. 1https://idrid.grand-challenge.org/Segmentation/ 4.1.3. Development Dataset (D) The development dataset originates from two DR screening programs: EyePACS (Cuadros and Bresnick, 2009) and OPHDIAT. Development images from EyePACS were acquired in multiple primary care sites t…”
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“EyePACS dataset [25]: EyePACS is the fundus image dataset collected from EyePACS, LLC , a free platform for retinopathy screening, used to classify different levels of cataract.”
MethodDiabetic retinopathy classification using VGG16 neural network
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“2014) and EyePACS/Kaggle (Cuadros and Bresnick 2009) are the most frequently used.”
MethodYang, Y., Shang, F., Wu, B., Yang, D., Wang, L., Xu, Y., Zhang, W., & Zhang, T. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3062638 · Cited by 66 · see citing works
“3) LIQ-EyePACs is a subset of the EyePACS dataset [45] for evaluating the robustness of the grading methods, which contains some low image quality samples.”
BackgroundYang, Y., Cai, Z., Qiu, S., & Xu, P. (2024) · PLoS ONE · doi:10.1371/journal.pone.0299265 · Cited by 71 · see citing works
Mexico-California Diabetes collaborative group, Barquera, S., Schillinger, D., Aguilar-Salinas, C. A., Schenker, M. B., Rodríguez, L. A., Hernández-Alcaraz, C., & Sepúlveda-Amor, J. (2018) · Globalization and Health · doi:10.1186/s12992-018-0390-5 · Cited by 62 · see citing works
“early detection system for retinopathy in Mexico, taking advantage of the telemedicine experience developed in California [55], and is carrying out a funded diabetes prevention and control intervention in food banks in”
BackgroundA scoping review of fair machine learning techniques when using real-world data
Huang, Y., Guo, J., Chen, W.-H., Lin, H.-Y., Tang, H., Wang, F., Xu, H., & Bian, J. (2024) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2024.104622 · Cited by 61 · see citing works
Dai, G., He, W., Xu, L., Pazo, E. E., Lin, T., Liu, S., & Zhang, C. (2020) · PLoS ONE · doi:10.1371/journal.pone.0230111 · Cited by 57 · see citing works
Telemedicine and Diabetic Retinopathy: Review of Published Screening Programs
Newman-Casey, P. A. (2015) · Journal of Endocrinology and Diabetes · doi:10.15226/2374-6890/2/4/00131 · Cited by 57 · see citing works
“If an outside grader was used, the grader had to undergo EyePACS certification [15].”
MethodHuemer, J. C., Wagner, S. K., & Sim, D. A. (2020) · Clinical ophthalmology · doi:10.2147/opth.s261629 · Cited by 62 · see citing works
BRSET: A Brazilian Multilabel Ophthalmological Dataset of Retina Fundus Photos
Nakayama, L. F., Restrepo, D. S., de Matos, J. C. R. G., Ribeiro, L. Z., Malerbi, F. K., Celi, L. A., & Regatieri, C. V. (2024) · PLOS Digital Health · doi:10.1371/journal.pdig.0000454 · Cited by 55 · see citing works
Pieczyński, J., Kuklo, P., & Grzybowski, A. E. (2021) · Ophthalmology and Therapy · doi:10.1007/s40123-021-00353-2 · Cited by 54 · see citing works
General deep learning model for detecting diabetic retinopathy
Chen, P.-N., Lee, C.-C., Liang, C., Pao, S., Huang, K.-H., & Lin, K.-F. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04005-x · Cited by 54 · see citing works
“The EyePACS [25] dataset was applied with pre-trained NASNet-Large [26] to tune the hyper-parameters.”
MethodVijayan, T. B., Sangeetha, M. K., Kumaravel, A., et al. (2020) · IETE Journal of Research · doi:10.1080/03772063.2020.1844082 · Cited by 54 · see citing works
Abramovich, O., Pizem, H., Eijgen, J. V., Oren, I., Melamed, J., Stalmans, I., Blumenthal, E. Z., & Behar, J. A. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107522 · Cited by 53 · see citing works
“EyePACS [25] 88,702 59,910 - 3636x2473 (On Average) Not Graded Semi supervised learning examples”
BackgroundMutawa, A. M., Alnajdi, S., & Sruthi, S. (2023) · Applied Sciences · doi:10.3390/app13095685 · Cited by 53 · see citing works
Fundus Image Enhancement Method Based on CycleGAN
You, Q., Wan, C., Sun, J., et al. (2019) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2019.8856950 · Cited by 53 · see citing works
Uncertainty-Aware Deep Learning Methods for Robust Diabetic Retinopathy Classification
Jaskari, J., Sahlsten, J., Damoulas, T., Knoblauch, J., Särkkä, S., Kärkkäinen, L., Hietala, K., & Kaski, K. K. (2022) · IEEE Access · doi:10.1109/access.2022.3192024 · Cited by 51 · see citing works
“In addition, we study the out-of-distribution performance using three benchmark datasets: the EyePACS [15], the Messidor-2 [16], [21], and the APTOS [17].”
MethodChalakkal, R. J., Abdulla, W. H., & Hong, S. C. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-817440-1.00003-6 · Cited by 51 · see citing works
Bresnick, G., Cuadros, J. A., Khan, M., Fleischmann, S., Wolff, G., Limon, A., Chang, J., Jiang, L., Cuadros, P., & Pedersen, E. R. (2020) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2019-001154 · Cited by 59 · see citing works
“13 The same consultants evaluated the retinal images over the entire study period.”
Self-citationPeng, J., Zou, H., Wang, W., et al. (2011) · BMC Health Services Research · doi:10.1186/1472-6963-11-250 · Cited by 50 · see citing works
“Most commercial telemedicine systems in developed countries are proprietary in that the end users (primary care centers) must use licensed software built into the imaging system for storage and transmission of images [8].”
BackgroundMethodInfluentialSellergren, A., Kazemzadeh, S., Jaroensri, T., Kiraly, A., Traverse, M., Kohlberger, T., Xu, S., Jamil, F., Hughes, C., Lau, C., et al. (2025) · arXiv · doi:10.48550/arxiv.2507.05201 · Cited by 49 · see citing works
Diabetic Retinopathy Detection using Ensemble Machine Learning
Odeh, I., Alkasassbeh, M. S., & Alauthman, M. (2021) · doi:10.1109/icit52682.2021.9491645 · Cited by 47 · see citing works
“Gargeya and Leng’s [13] proposed algorithm is mainly constructed of Deep convolutional neural networks (DCNNs), which consist of several filtering layers, each one produces the sum of both its outcome and the previous layer’s results.”
BackgroundEDR-Net: Lightweight Deep Neural Network Architecture for Detecting Referable Diabetic Retinopathy
Aujih, A. B., Shapiai, M. I., Mériaudeau, F., & Tang, T. B. (2022) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2022.3182907 · Cited by 47 · see citing works
Outpatient services and primary care: scoping review, substudies and international comparisons
Winpenny, E. M., Miani, C., Pitchforth, E., Ball, S. L., Nolte, E., King, S., Greenhalgh, J., & Roland, M. O. (2016) · Health Services and Delivery Research · doi:10.3310/hsdr04150 · Cited by 47 · see citing works
Retinal Image Enhancement Using Cycle-Constraint Adversarial Network
Wan, C., Zhou, X., You, Q., Sun, J., Shen, J., Zhu, S., Jiang, Q. L., & Yang, W. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2021.793726 · Cited by 48 · see citing works
Owsley, C. J., Rhodes, L. A., McGwin, G., Mennemeyer, S. T., Bregantini, M., Patel, N., Wiley, D. M., LaRussa, F., Box, D., SAADDINE, J. B., et al. (2015) · International Journal for Equity in Health · doi:10.1186/s12939-015-0213-8 · Cited by 46 · see citing works
The promise of explainable deep learning for omics data analysis: Adding new discovery tools to AI
Santorsola, M., & Lescai, F. (2023) · New Biotechnology · doi:10.1016/j.nbt.2023.06.002 · Cited by 44 · see citing works
transferGWAS: GWAS of images using deep transfer learning
Kirchler, M., Konigorski, S., Norden, M., Meltendorf, C., Kloft, M., Schurmann, C., & Lippert, C. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac369 · Cited by 40 · see citing works
Telemedicine for diabetes care: An Indian perspective - feasibility and efficacy
Kesavadev, J., Saboo, B., Shankar, A., Krishnan, G., & Jothydev, S. (2015) · Indian journal of endocrinology and metabolism · doi:10.4103/2230-8210.167560 · Cited by 33 · see citing works
Pieczyński, J., & Grzybowski, A. E. (2015) · European Ophthalmic Review · doi:10.17925/eor.2015.09.01.49 · Cited by 42 · see citing works
“This programme covers all patients with diabetes 12 years of age or over, with patient identification sent to the programme by the GP. Trained readers grade photographs.15 US Cuadros et al. presented a study on the EyePACS (picture archive and communication system) programme developed in California.24 They used a lice…”
MethodShah, A., Lynch, S. K., Niemeijer, M., Amelon, R. E., Clarida, W., Folk, J. C., Russell, S. R., Wu, X., & Abràmoff, M. D. (2018) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2018.8363846 · Cited by 42 · see citing works
“504,906 images were obtained from the Eyepacs dataset [13] for training the CNNs used in the experiments.”
MethodUllah, W., Pathan, S. K., Panchal, A., Anandan, S., Saleem, K., Sattar, Y., Ahmad, E., Mukhtar, M., & Nawaz, H. (2020) · Medicine · doi:10.1097/md.0000000000020306 · Cited by 42 · see citing works
Enhancing Ophthalmic Diagnosis and Treatment with Artificial Intelligence
Olawade, D. B., Weerasinghe, K., Mathugamage, M. D. D. E., Odetayo, A., Aderinto, N., Teke, J., & Boussios, S. (2025) · Medicina · doi:10.3390/medicina61030433 · Cited by 50 · see citing works
A Competition for the Diagnosis of Myopic Maculopathy by Artificial Intelligence Algorithms
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WITHDRAWN: Gabor filter and machine learning based diabetic retinopathy analysis and detection
Vijayan, T. B., Sangeetha, M. K., Kumaravel, A., et al. (2020) · Microprocessors and Microsystems · doi:10.1016/j.micpro.2020.103353 · Cited by 40 · see citing works
RetractedLemij, H. G., de Vente, C., Sánchez, C. I., & Vermeer, K. A. (2023) · Ophthalmology Science · doi:10.1016/j.xops.2023.100300 · Cited by 40 · see citing works
“57 They provided a large data set that was labeled as “ random ” and a small set labeled as “ glaucoma suspects.”
BackgroundBouskill, K., Smith-Morris, C. M., Bresnick, G. H., Cuadros, J. A., & Pedersen, E. R. (2018) · BMC Health Services Research · doi:10.1186/s12913-018-3427-9 · Cited by 40 · see citing works
Self-citationArtificial intelligence in ophthalmopathy and ultra-wide field image: A survey
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“This study builds on existing telemedicine studies and supports the notion that telemedicine solutions for diabetic retinopathy screenings are feasible in the primary care setting [12, 17, 18].”
BackgroundMultimodal image encoding pre-training for diabetic retinopathy grading
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“The training samples and validation samples that we use were released by EyePACS, LLC [27].”
MethodPandey, P. U., Ballios, B. G., Christakis, P. G., Kaplan, A., Mathew, D. J., Tone, S. O., Wan, M. J., Micieli, J. A., & Wong, J. C. Y. (2023) · British Journal of Ophthalmology · doi:10.1136/bjo-2022-322183 · Cited by 36 · see citing works
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DiaNet v2 deep learning based method for diabetes diagnosis using retinal images
Al-Absi, H. R. H., Pai, A., Naeem, U., Mohamed, F. K., Arya, S., Sbeit, R. A., Bashir, M., Shafei, M. M. E., Hajj, N. E., & Alam, T. (2024) · Scientific Reports · doi:10.1038/s41598-023-49677-y · Cited by 32 · see citing works
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- Authors
- Cuadros, J., & Bresnick, G.
- Affiliations
- University of California, Berkeley (US)
- Corresponding author
- Cuadros, Jorge
- 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)
- 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
- 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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