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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“The images were provided by the Eye Picture Archive Communication System (EyePACS), which is a free platform for retinopathy screening and made available by the California Healthcare Foundation [23].”
Progress of artificial intelligence in diabetic retinopathy screening
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Which Color Channel Is Better for Diagnosing Retinal Diseases Automatically in Color Fundus Photographs?
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Background
Suitability of a Low-Cost, Handheld, Nonmydriatic Retinograph for Diabetic Retinopathy Diagnosis
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Systematic Comparison of Heatmapping Techniques in Deep Learning in the Context of Diabetic Retinopathy Lesion Detection
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A Comprehensive Review of Diabetic Retinopathy Detection and Grading Based on Deep Learning and Metaheuristic Optimization Techniques
Dayana, A. M., & Emmanuel, W. R. S. (2023) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-023-09946-5 · Cited by 28
“Kaggle EyePACS [48] 88,702 65,343 23,359 45o Varying size JPEG DR grading On registration https:// www.”
Background
Accuracy of Detection and Grading of Diabetic Retinopathy and Diabetic Macular Edema Using Teleretinal Screening
Date, R. C., Shen, K. L., Shah, B. M., et al. (2018) · Ophthalmology Retina · doi:10.1016/j.oret.2018.12.003 · Cited by 28
Teleretinal screening for diabetic retinopathy in six Los Angeles urban safety-net clinics: final study results.
Ogunyemi, O. I., George, S., Patty, L., Teklehaimanot, S., & Baker, R. S. (2013) · Europe PMC (PubMed Central) · Cited by 27
Expert Discussions Improve Comprehension of Difficult Cases in Medical Image Assessment
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“The labeling workﬂow we use here is similar to that used in telemedicine enterprises, including screening for eye disease [9, 5].”
Method
Screening Diabetic Retinopathy Using an Automated Retinal Image Analysis System in Independent and Assistive Use Cases in Mexico: Randomized Controlled Trial
Noriega-Campero, A., Meizner, D., Camacho‐García‐Formentí, D., Enciso, J., Quiroz-Mercado, H., Morales-Cantón, V., Almaatouq, A., & Pentland, A. (2021) · JMIR Formative Research · doi:10.2196/25290 · Cited by 23
“The model was trained on an international dataset, of which most images were taken in primary care clinics in California, United States [35].”
Method
Telemedicine model to prevent blindness from familial glaucoma
Staffieri, S. E., Ruddle, J. B., Kearns, L. S., Barbour, J. M., Edwards, T. L., Paul, P., & Mackey, D. A. (2011) · Clinical and Experimental Ophthalmology · doi:10.1111/j.1442-9071.2011.02556.x · Cited by 25
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“Some examples of fundus images with different DR stages in the Kaggle dataset[15].”
BackgroundMethod
Mini Lesions Detection on Diabetic Retinopathy Images via Large Scale CNN Features
Chen, Q., Sun, X., Zhang, N., Cao, Y., & Liu, B. (2019) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai.2019.00056 · Cited by 25
“EyePACS [17] is a well-known large scale dataset for DR Diagnosis in ICDRDMEDSS standard.”
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Elangovan, K., Lim, G., & Ting, D. S. W. (2024) · Scientific Reports · doi:10.1038/s41598-024-60429-4 · Cited by 30
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Teleretinal screening for diabetic retinopathy in six Los Angeles urban safety-net clinics: initial findings.
Ogunyemi, O. I., Terrien, E., Eccles, A. M., Patty, L., George, S., Fish, A. E., Teklehaimanot, S., Ilapakurthi, R., Aimiuwu, O., & Baker, R. S. (2011) · PubMed Central · Cited by 24
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“They collected the image from the Eyepacs dataset by Cuadros and Bresnick [34].”
Method
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Background
Diabetic Retinopathy and the Cascade into Vision Loss
Smith-Morris, C. M., Bresnick, G. H., Cuadros, J. A., Bouskill, K., & Pedersen, E. R. (2018) · Medical Anthropology · doi:10.1080/01459740.2018.1425839 · Cited by 23
“Telemedical screening programs (Cuadros and Bresnick 2009; Walker et al. 2008), which enable cost-effective retinal screening within primary care settings, have helped address these needs (Kirkizlar et al. 2013; Nguyen et al. 2016; Taylor et al. 2007).”
BackgroundSelf-citation
Fundus Imaging-Based Healthcare: Present and Future
Kumar, V., & Paul, K. (2023) · ACM Transactions on Computing for Healthcare · doi:10.1145/3586580 · Cited by 21
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Litvin, T. V., Ozawa, G. Y., Bresnick, G. H., et al. (2014) · Optometry and Vision Science · doi:10.1097/opx.0000000000000205 · Cited by 21
“) according to the EyePACS grading protocol.(22) A presumptive diagnosis of clinically significant macular edema (CSME) was made when HEs were noted at or within one disc diameter of the foveola.”
MethodSelf-citation
Identification of factors to increase efficacy of telemedicine screening for diabetic retinopathy in endocrinology practices using the Intelligent Retinal Imaging System (IRIS) platform
Naik, S., Wykoff, C. C., Ou, W. C., et al. (2018) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2018.04.011 · Cited by 21
A survey of Alabama eye care providers in 2010–2011
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“Telemedicine is well suited for vision and eye disease screening services and also monitoring of disease through imaging and other specialized tests because of the low invasiveness of testing, wide spread availability and affordability of imaging technologies, high levels of diagnostic reliability [37], and ease of tr…”
Background
A portable retina fundus photos dataset for clinical, demographic, and diabetic retinopathy prediction
Wu, C., Restrepo, D. S., Nakayama, L. F., Ribeiro, L. Z., Shuai, Z., Barboza, N. S., Sousa, M. L. V., Fiterman, R. D., Pereira, A. D. A., Regatieri, C. V., et al. (2025) · Scientific Data · doi:10.1038/s41597-025-04627-3 · Cited by 21
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“EyePACS, Avenue Sync and EyerCloud are examples of technological infrastructures that can integrate with healthcare information system.”
BackgroundInfluential
High-fidelity diabetic retina fundus image synthesis from freestyle lesion maps
Hou, B. (2022) · Biomedical Optics Express · doi:10.1364/boe.477906 · Cited by 19
Self-supervised equivariant regularization reconciles multiple-instance learning: joint referable diabetic retinopathy classification and lesion segmentation
Zhu, W., Qiu, P., Leporé, N., Dumitrascu Oana, M., & Wang, Y. (2023) · International Symposium on Medical Information Processing and Analysis (SIPAIM) · doi:10.1117/12.2669772 · Cited by 20
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“EyePACS uses an image evaluation protocol, the EyePACS Retinopathy Grading System (ERGS), developed by Bresnick [6] and based on the Early Treatment Diabetic Retinopathy Study (ETDRS) [8].”
BackgroundMethodSelf-citation
Analysis of reasons for noncompliance with laser treatment in patients of diabetic retinopathy
Hua, W. H., Cao, S., Cui, J. Z., Maberley, D., & Matsubara, J. A. (2013) · Canadian Journal of Ophthalmology · doi:10.1016/j.jcjo.2012.09.012 · Cited by 19
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“(a) Dataset launched by the EyePACS Medical Foundation [25]: the dataset contains approximately 80,000 fundus images that were captured by different fundus cameras.”
Method
Predictive Models for Diabetic Retinopathy from Non-Image Teleretinal Screening Data.
Ogunyemi, O. I., Gandhi, M., & Tayek, C. (2019) · PubMed Central · Cited by 19
A Revised Approach for the Detection of Sight-Threatening Diabetic Macular Edema
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Self-citation
DrishtiCare: A Telescreening Platform for Diabetic Retinopathy Powered with Fundus Image Analysis
Joshi, G. D., & Sivaswamy, J. (2011) · Journal of Diabetes Science and Technology · doi:10.1177/193229681100500104 · Cited by 19
A frequency-domain approach with learnable filters for image classification
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Wei, Q., Li, X., Wang, H., et al. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20870-7_12 · Cited by 18
Deep learning generalization for diabetic retinopathy staging from fundus images
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“To implement the MSD approach we use a leave-one-domain-out strategy, with an exception of the EYEPACS-train dataset which was included in all fine-tuning steps (the largest dataset at our disposal).”
Influential
Deep attentive convolutional neural network for automatic grading of imbalanced diabetic retinopathy in retinal fundus images
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Use of Telehealth Screening to Detect Diabetic Retinopathy and Other Ocular Findings in Primary Care Settings
Gao, X., Park, C. H., Dedrick, K., et al. (2018) · Telemedicine Journal and e-Health · doi:10.1089/tmj.2018.0016 · Cited by 17
“Telemedicine programs implemented in primary care settings can both significantly increase adherence to retinal screenings and effectively detect DR. 12 In this large urban primary care practice, nearly 10,000 patients with diabetes participated in this telemedicine screening program, and 76.7% were eligible for inclu…”
Influential
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Wang, V. Y., Lo, M., Chen, T., Huang, C.-H., Huang, A., & Wang, P. (2024) · Scientific Reports · doi:10.1038/s41598-024-82884-9 · Cited by 15
“Thus, EyePACS data corresponding to 5108 (80%), 639 (10%), and 637 (10”
Influential
Code-Free Deep Learning Glaucoma Detection on Color Fundus Images
Milad, D., Antaki, F., Mikhail, D., et al. (2025) · Ophthalmology Science · doi:10.1016/j.xops.2025.100721 · Cited by 15
Central Macular Thickness in Diabetic Patients: A Sex‐based Analysis
Arthur, E., Young, S. B., Elsner, A. E., Baskaran, K., Papay, J. A., Muller, M. S., Gast, T. J., Haggerty, B. P., Clark, C. A., Malinovsky, V. E., et al. (2019) · Optometry and Vision Science · doi:10.1097/opx.0000000000001363 · Cited by 17
Self-citation
Prevalence of Diabetic Eye Diseases in American Indians and Alaska Natives (AI/AN) as Identified by the Indian Health Service’s National Teleophthalmology Program Using Ultrawide Field Imaging (UWFI)
Fonda, S. J., Bursell, S., Lewis, D. G., Clary, D., Shahon, D., & Silva, P. S. (2021) · Ophthalmic Epidemiology · doi:10.1080/09286586.2021.1996611 · Cited by 14
“30 Moreover, California has several non-IHS DR surveillance programs [e.g., EyePACS (Eye Picture Archive Communication System) 31 and no California clinics have enrolled in the IHS teleophthalmology program yet.”
Hybrid CNN+Transformer for Diabetic Retinopathy Recognition and Grading
Sadeghzadeh, A., Junayed, M. S., Aydın, T., & Islam, M. B. (2023) · Innovations in Intelligent Systems and Applications Conference (ASYU) · doi:10.1109/asyu58738.2023.10296789 · Cited by 14
“The experiments are conducted on five publicly available benchmarks DR datasets: EyePACS [19], [20], APTOS [21], DDR [22], Messidor-1 [23], and Messidor-2 [23]–[25].”
Method
Autonomous Screening for Diabetic Macular Edema Using Deep Learning Processing of Retinal Images
Bressler, I., Aviv, R., Margalit, D., Cohen, G. Y., Ianchulev, T., Savant, S. V., Ramsey, D. J., & Dvey-Aharon, Z. (2025) · Ophthalmology Science · doi:10.1016/j.xops.2025.100722 · Cited by 14
“The EyePACS dataset contained up to 6 images per patient visit: 1 macula-centered image, 1 disk-centered image, and 1 centered image per eye (in which a central ﬁ xation image is ﬁ xated on the middle of a line connecting the foveola and the optic disc).”
Influential
Diabetes Awareness, Treatment, and Control among Mexico City Residents
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SatFormer: Saliency-Guided Abnormality-Aware Transformer for Retinal Disease Classification in Fundus Image
Jiang, Y., Xu, K., Wang, X., et al. (2022) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/138 · Cited by 14
The assessment of fundus image quality labeling reliability among graders with different backgrounds
Laurik, K. L., Sapahia, R., DeBuc, D. C., et al. (2022) · PLoS ONE · doi:10.1371/journal.pone.0271156 · Cited by 14
IDGI: A Framework to Eliminate Explanation Noise from Integrated Gradients
Yang, R., Wang, B., & Bilgic, M. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.02272 · Cited by 17
“With the deployment of deep neural network (DNN) models for safety-critical applications such as autonomous driving [5–7] and medical diagnostics [10, 24], explaining the decisions of DNNs has become a critical concern.”
Method
DMU-TransNet: Dense multi-scale U-shape transformer network for anomaly detection
Zhou, W., Wu, S., Wang, Y., Zuo, L., Yi, Y., & Cui, W. (2024) · Measurement · doi:10.1016/j.measurement.2024.114216 · Cited by 14
Frequency learning for image classification
Stuchi, J. A., Boccato, L., & Attux, R. (2020) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · doi:10.48550/arxiv.2006.15476 · Cited by 20
“d as a ﬁlter for machine learning algorithms that automatically analyze retina images in order to detect diseases like DR and glaucoma. An interesting dataset in this ﬁeld is based on EyePACS project [21]. The EyePACS project provides an excellent dataset for DR detection and it is available on Kaggle platform [22]. I…”
Method
Diabetic retinopathy classification based on dense connectivity and asymmetric convolutional neural network
Cao, J., Chen, J., Zhang, X., et al. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07952-5 · Cited by 13
“Contributions of our work are as follows: • We perform data enhancement on the EyePACS dataset [25] to solve the data imbalance problem, so that the training set and the test set maintain the balance of the number of samples in all categories.”
Diabetic Retinopathy Screening Using Smartphone-Based Fundus Photography and Deep-Learning Artificial Intelligence in the Yucatan Peninsula: A Field Study
Wroblewski, J. J., Sánchez-Buenfil, E., Inciarte, M., Berdia, J., Blake, L. R., Wroblewski, S., Patti, A., Suter, G., & Sanborn, G. E. (2023) · Journal of Diabetes Science and Technology · doi:10.1177/19322968231194644 · Cited by 18
“…This is made more remarkable because DR phenotypes differ by region and ethnicity, 3 and the two AI algorithms used in this analysis were trained on different international DR data sets of patients of Indian descent 14,20,27 for Medios and of American and Northern Mexican descent 13,33 for EyeArt.”
Influential
Advancing diabetic retinopathy classification using ensemble deep learning approaches
Biswas, A., & Banik, R. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.107804 · Cited by 15
A smartphone-based test and predictive models for rapid, non-invasive, and point-of-care monitoring of ocular and cardiovascular complications related to diabetes
Chakravadhanula, K. (2020) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2020.100485 · Cited by 13
“The UCI Heart Disease Dataset does not include retinal fundus images, so an image from the EyePACS dataset was inputted based on the D.R. classification of severity in the UCI collection.”
BackgroundInfluential
The effect of the COVID‐19 pandemic on working practices of UK primary care optometrists
Nagra, M., Allen, P. M., Norgett, Y., et al. (2021) · Ophthalmic and Physiological Optics · doi:10.1111/opo.12786 · Cited by 13
Prior Guided Fundus Image Quality Enhancement Via Contrastive Learning
Cheng, P., Lin, L., Huang, Y., et al. (2021) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi48211.2021.9434005 · Cited by 13
“We present qualitative results from both EyePACS [10] and clinical practice.”
Method
Deep Learning for Retinal Image Quality Assessment of Optic Nerve Head Disorders
Chan, E. J. J., Najjar, R. P., Tang, Z., & Miléa, D. (2021) · Asia-Pacific Journal of Ophthalmology · doi:10.1097/apo.0000000000000404 · Cited by 13
“Currently, there are 2 DR quality-labeled fundus dataset: Kaggle EYEPACS 25 and Eye-Quality dataset 41 .”
BackgroundMethod
Computational intelligence in eye disease diagnosis: a comparative study
Kumar, S. V. M., & Gunasundari, R. (2023) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-022-02737-3 · Cited by 13
“A non-proprietary tel-emedicine system “EyePacs” [127] was created with a large dataset (35,126 retinal eye images) collected from various diabetic screening programs conducted in California.”
Method
Workflow concerns and workarounds of readers in an urban safety net teleretinal screening study.
Fish, A. E., George, S., Terrien, E., Eccles, A. M., Baker, R. S., & Ogunyemi, O. I. (2011) · PubMed Central · Cited by 13
Screening for macular disorders: the optometrist's perspective
Elsner, A. E., & King, B. J. (2015) · Clinical Optometry · doi:10.2147/opto.s52482 · Cited by 16
“13 There are a large number of potential variables in different retinal imaging protocols: mydriatic versus nonmydriatic, full color versus only red-free (blue and/or green light) retinal images are acquired or graded, wide-field (30 ° –45 ° or more) versus ultrawide-field images (100 ° or 200 ° ),…”
Understanding the Knowledge Gap Experienced by U.S. Safety Net Patients in Teleretinal Screening.
George, S. M., Hayes, E. M., Fish, A. E., Daskivich, L. P., & Ogunyemi, O. I. (2016) · PubMed Central · Cited by 13
Improving diabetic retinopathy screening using artificial intelligence: design, evaluation and before-and-after study of a custom development
Pinto, I., Olazarán, Á., Jurío, D., de la Osa, B., Sainz, M., Oscoz, A., Ballaz, J., Gorricho, J., Galar, M., & Andonegui, J. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1547045 · Cited by 14
Color Fundus Photography and Deep Learning Applications in Alzheimer Disease
Dumitrascu, O. M., Li, X., Zhu, W., Woodruff, B. K., Nikolova, S., Sobczak, J. M., Youssef, A., Saxena, S., Andreev, J., Caselli, R. J., et al. (2024) · Mayo Clinic Proceedings Digital Health · doi:10.1016/j.mcpdig.2024.08.005 · Cited by 18
“39 After image quality control, the ADVAS model included 318 binary vessel segmentation images from 113 patients with AD and 338 original vessel segmentation images from 116 patients with AD.”
Content-based retrieval of retinal images for maculopathy
Deepak, K. S., Joshi, G. D., & Sivaswamy, J. (2010) · International Health Informatics Symposium · doi:10.1145/1882992.1883013 · Cited by 12
“Recently, there has been significant effort towards building screening solutions for DR [5] using color fundus images (CFI) mainly due to the value they offer such as low-cost and wider reach.”
Background
Factors Associated with Ophthalmology Referral and Adherence in a Teleretinal Screening Program: Insights from a Federally Qualified Health Center
Song, A., Johnson, N. A., Mirzania, D., et al. (2022) · Clinical ophthalmology · doi:10.2147/opth.s380629 · Cited by 12
Glaucoma Diagnosis Based on Retinal Fundus Image: A Review
Mahdi, H., & Abbadi, N. K. E. (2022) · Iraqi Journal of Science · doi:10.24996/ijs.2022.63.9.32 · Cited by 10
“DRISHTI-GS1 [23] 101 70 31 ● Classification images ● Soft matt segmenting cup and disc ●CDR values and Disc center http://cvit.iiit.ac.in/projects/mip/dri shti-gs/mip-dataset2/enter.php HRF [24] 45 15 15 ● Classification images ● Segmentation Vessel and FOV ●Center and Radius for OD https://www5.cs.fau.de/research/da…”
Background
Image analysis in modern ophthalmology: from acquisition to computer assisted diagnosis and telemedicine
Marrugo, A. G., Millán, M. S., Cristóbal, G., Gabarda, S., Šorel, M., & Šroubek, F. (2012) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.921299 · Cited by 11
“11”
Background
Artificial Intelligence in the assessment of diabetic retinopathy from fundus photographs
Gilbert, M. J., & Sun, J. K. (2020) · Seminars in Ophthalmology · doi:10.1080/08820538.2020.1855358 · Cited by 13
Application of Deep Learning Methods in a Moroccan Ophthalmic Center: Analysis and Discussion
Farahat, Z., Zrira, N., Souissi, N., Benamar, S., Belmekki, M., Ngote, M. N., & Megdiche, K. (2023) · Diagnostics · doi:10.3390/diagnostics13101694 · Cited by 12
“The authors also used image level datasets such as Messidor-1 [27], Messidor-2, LIQ-EyePACs [28], and other private datasets.”
Method
Leveraging Old Knowledge to Continually Learn New Classes in Medical Images
Chee, E. Y. L., Lee, M. L., & Hsu, W. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i12.26659 · Cited by 12
“• EyePACS (Kaggle and EyePacs 2015; Cuadros and Bresnick 2009): This Kaggle dataset consists of 35125 retinal images that have been graded for the severity of diabetic retinopathy (DR).”
Method
Enhancing Diabetic Retinopathy Diagnosis with Inception v4: A Deep Learning Approach
Athilakshmi, R., Jansi, R., & Upadhyay, R. (2023) · International Conference on Inventive Research in Computing Applications (ICIRCA) · doi:10.1109/icirca57980.2023.10220890 · Cited by 11
“Several studies in the literature [9] have investigated the effectiveness of telemedicine-based DR screening.”
BackgroundMethodInfluential
Diabetic retinopathy grading based on Lesion correlation graph
Luo, D., & Kamata, S. (2020) · International Conference on Informatics, Electronics & Vision (ICIEV) · doi:10.1109/icievicivpr48672.2020.9306664 · Cited by 11
“EyePACS [21] is the dataset that describes retinal images, especially diabetic retinopathy, which provided by California Healthcare Foundation.”
Background
Telemedicine and Telehealth
Chiang, M. F., Starren, J. B., & Demiris, G. (2021) · Springer eBooks · doi:10.1007/978-3-030-58721-5_20 · Cited by 14
Advances in Classifying the Stages of Diabetic Retinopathy Using Convolutional Neural Networks in Low Memory Edge Devices
Paul, A. J. (2021) · IEEE Madras Section Conference (MASCON) · doi:10.1109/mascon51689.2021.9563584 · Cited by 13
Topological Data Analysis for Eye Fundus Image Quality Assessment
Avilés-Rodríguez, G. J., Hipólito, J. I. N., Cosío-León, M. D. L. Á., et al. (2021) · Diagnostics · doi:10.3390/diagnostics11081322 · Cited by 11
“…of Digital Medical Images This Considering the need to use mathematical objects to represent 2D digital images, cubical Considering the need to use mathematical objects to represent 2D digital images, cubical complexes arise as a natural representation for this domain [32] given that…”
BackgroundMethod
Addressing Artificial Intelligence Bias in Retinal Disease DiagnosticsPreprint
Burlina, P., Joshi, N., Paul, W., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.13515 · Cited by 11
Fully Exploiting Every Real Sample: SuperPixel Sample Gradient Model Stealing
Zhao, Y., Deng, X., Liu, Y., Pei, X., Xia, J., & Chen, W. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.02295 · Cited by 10
“We employ four datasets used in Knock-off for our experimentation: Caltech256 (256 classes) [16], CUB-200-2011 (200 classes) [43], Indoor Scenes (67 classes) [31], and Diabetic Retinopathy (5 classes) [7].”
Method
Proposed measures to be taken by ophthalmologists during the coronavirus disease 2019 pandemic: Experience from Chang Gung Memorial Hospital, Linkou, Taiwan
Wu, W.-C., Lin, J.-Y., Kang, E.-C., Yeh, P., Ling, X., Chen, H., Chen, K., Hwang, Y., & Lai, C. (2020) · Taiwan Journal of Ophthalmology · doi:10.4103/tjo.tjo_21_20 · Cited by 10
Artificial Intelligence Software for Diabetic Eye Screening: Diagnostic Performance and Impact of Stratification
Peeters, F., Rommes, S., Elen, B., Gerrits, N., Stalmans, I., Jacob, J., & Boever, P. D. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12041408 · Cited by 11
“Note that the disease gradings originate from the telemedicine platform without regrading [26].”
Background
Nuclear Cataract Database for Biomedical and Machine Learning Applications
Cruz-Vega, I., Morales-Lopez, H. I., Ramírez-Cortés, J. M., et al. (2023) · IEEE Access · doi:10.1109/access.2023.3312616 · Cited by 10
Content Design and System Implementation of a Teleophthalmology System for Eye Disease Diagnosis and Treatment and Its Preliminary Practice in Guangdong, China
Xiao, D., Vignarajan, J., Chen, T., et al. (2017) · Telemedicine Journal and e-Health · doi:10.1089/tmj.2016.0266 · Cited by 10

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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