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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 [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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Evaluation of ChatGPT-4 in detecting referable diabetic retinopathy using single fundus images
Aftab, O., Khan, H., VanderBeek, B. L., Scoles, D., Kim, B. J., & Tsui, J. C. (2025) · AJO International · doi:10.1016/j.ajoint.2025.100111 · Cited by 10
A Wrapped Approach Using Unlabeled Data for Diabetic Retinopathy Diagnosis
Zhang, X., Kim, Y.-S., Chung, Y., et al. (2023) · Applied Sciences · doi:10.3390/app13031901 · Cited by 9
VascX Models: Deep Ensembles for Retinal Vascular Analysis From Color Fundus Images
Quiros, J. V., Liefers, B. J., van Garderen, K. A., Vermeulen, J. P., & Klaver, C. C. W. (2025) · Translational Vision Science & Technology · doi:10.1167/tvst.14.7.19 · Cited by 14
Leeman, S., Wang, L., Johnson, B. A., et al. (2022) · Telemedicine Journal and e-Health · doi:10.1089/tmj.2021.0064 · Cited by 9
Nakayama, L., Ribeiro, L. Z., Malerbi, F., & Regatieri, C. (2022) · Frontiers in Ophthalmology · doi:10.3389/fopht.2022.898181 · Cited by 8
“In the eyePACS labels, there is a description of patient sex, age, quality control, social aspects, or ethnicity (7).”
BackgroundTowards population-independent, multi-disease detection in fundus photographs
Matta, S., Lamard, M., Conze, P.-H., Guilcher, A. L., Lecat, C., Carette, R., Basset, F., Massin, P., Rottier, J.-B., Cochener, B., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-38610-y · Cited by 12
Parsa, S., & Khatibi, T. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-18968-5 · Cited by 10
“Initially, the neural networks are pre-trained using BYOL’s proposed method on the public EyePACS dataset [37], where image labels are not utilized.”
Ogunyemi, O. I., Moran, E., Daskivich, L. P., et al. (2013) · Telemedicine Journal and e-Health · doi:10.1089/tmj.2012.0191 · Cited by 9
Diabetic Retinopathy Detection using ensemble deep Learning and Individual Channel Training
Harihanth, K., & Karthikeyan, B. (2020) · International Conference on Intelligent Sustainable Systems (ICISS) · doi:10.1109/iciss49785.2020.9315991 · Cited by 9
“Fundus Retinal images available in Kaggle is provided by EyePACS [18],[25].”
MethodInfluentialDeep Red Lesion Classification for Early Screening of Diabetic Retinopathy
Ashraf, M. N., Hussain, M., & Habib, Z. (2022) · Mathematics · doi:10.3390/math10050686 · Cited by 9
Litvin, T. V., Weissenberg, C. R., Daskivich, L. P., et al. (2015) · Journal of Diabetes Science and Technology · doi:10.1177/1932296815617281 · Cited by 9
“Three-field nonmydriatic digital fundus photographs were obtained using a CR6-45NM fundus camera (Canon Inc, Tokyo, Japan) according to EyePACS protocol.(15) Macular thickness optical coherence tomography (OCT) scans were obtained using iVUE OCT (Optovue, Inc, Fremont, CA) Retina Map scanning protocol.”
MethodSelf-citationAn Active Learning Classifier for Further Reducing Diabetic Retinopathy Screening System Cost
Zhang, Y., & An, M. (2016) · Computational and Mathematical Methods in Medicine · doi:10.1155/2016/4345936 · Cited by 9
“This challenging problem causes a demand of a better computer-aided DR screening system [7, 8].”
BackgroundSafi, S., Ahmadieh, H., Katibeh, M., Yaseri, M., Nikkhah, H., Karimi, S., Nourinia, R., Tivay, A., Zareinejad, M., Azarmina, M., et al. (2019) · Journal of Ophthalmology · doi:10.1155/2019/2073679 · Cited by 9
“In terms of sex distribution, most of our screened subjects were women, which was in accordance with the study by Jani et al. study, performed in the United States [21].”
BackgroundMethodInfluentialUnsupervised Learning of Local Discriminative Representation for Medical Images
Chen, H., Li, J., Wang, R., et al. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-78191-0_29 · Cited by 9
Starren, J. B., Nesbitt, T. S., & Chiang, M. F. (2013) · Springer eBooks · doi:10.1007/978-1-4471-4474-8_18 · Cited by 9
Explainable AI for Diabetic Retinopathy: Utilizing YOLO Model on a Novel Dataset
Mutawa, A. M., Sabti, K. A., Raizada, S., & Sruthi, S. (2025) · AI · doi:10.3390/ai6120301 · Cited by 9
“We utilized random pictures from the EyePACS dataset [73] to evaluate the model (Figure 20).”
Bondala, A. K., & Lella, K. K. (2024) · The European Physical Journal Special Topics · doi:10.1140/epjs/s11734-024-01334-9 · Cited by 8
“We also explored various CNN models in this work to enhance our analysis and improve performance [21–28].”
A Novel Un-Supervised GAN for Fundus Image Enhancement with Classification Prior Loss
Chen, S., Zhou, Q., & Zou, H. (2022) · Electronics · doi:10.3390/electronics11071000 · Cited by 8
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. (2017) · Canadian Journal of Ophthalmology · doi:10.1016/j.jcjo.2017.09.025 · Cited by 8
Jena, M., Mishra, D., Mishra, S. P., & Mallick, P. K. (2022) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-022-07057-0 · Cited by 8
“This original dataset [47] The images in this dataset come from different models and types of cameras and feature very mixed quality.”
BackgroundSoftware-Assisted Analysis During Ocular Health Screening
Kolomeyer, A. M., Szirth, B. C., Shahid, K. S., et al. (2012) · Telemedicine Journal and e-Health · doi:10.1089/tmj.2012.0070 · Cited by 8
“17 In addition to the support garnered from the abovementioned studies, the capacity of monochromatic filters to identify pathology to a distinct layer in the posterior segment has been clinically validated by the use of automated software detection of DR by red or bright lesion detection 18 and by…”
Comparison of Cysts in Red and Green Images for Diabetic Macular Edema
Alhamami, M. A., Elsner, A. E., Malinovsky, V. E., et al. (2016) · Optometry and Vision Science · doi:10.1097/opx.0000000000001010 · Cited by 8
“The color fundus images were graded by EyePACS-certified graders, according to the International Classification for Diabetic Retinopathy.(31,32) The criterion for diagnosis with clinically significant macular edema is the presence of hard exudates in the central 1500 Km of the fovea,(32,33) a method validated by a dil…”
MethodInfluentialSelf-citationHybrid Deep Learning Models for Diabetic Retinopathy Classification
Mikram, M., Moujahdi, C., Rhanoui, M., et al. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-031-07969-6_13 · Cited by 8
Luo, S., Lock, L. J., Xing, B., Wingelaar, M. J., Channa, R., & Liu, Y. (2022) · Telemedicine Journal and e-Health · doi:10.1089/tmj.2022.0391 · Cited by 7
Addressing Imbalance for Class Incremental Learning in Medical Image Classification
Hao, X., Ni, W., Jiang, X., Tan, W., & Yan, B. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3680719 · Cited by 8
“Taking the EyePACS dataset [8] as an example, the model is initially trained to classify three classes ( i.e. , No DR, Mild, and Moderate).”
BackgroundMethodWelikala, R. A., Fajtl, J., Johnson, G., et al. (2024) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) · doi:10.1109/bhi62660.2024.10913687 · Cited by 7
“The adapted U-Net underwent pretraining via self-supervised learning using the EyePACS dataset from Kaggle [31], consisting of 88,702 retinal images.”
Domain Adaptation for Medical Image Classification without Source Data
Zhou, C., Zhang, W., Chen, H., & Chen, L. (2022) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm55620.2022.9995395 · Cited by 8
“They are EyePACS [39], MESSIDOR [40] and IDRiD [41] .”
Unsupervised Local Discrimination for Medical Images
Chen, H., Wang, R., Wang, X., Li, J., Fang, Q., Li, H., Bai, J., Peng, Q., Meng, D., & Wang, L. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3299038 · Cited by 7
“Color fundus Kaggle DR1[54] 35k CHASEDB (CHASE)2[55] vessel segmentation 20 8 HRF3[56] vessel segmentation 25 20 RITE4[57] vessel segmentation 20 20 STARE5[58], [59] vessel segmentation 10 10 DRISHTI-GS1(Cup)6[60], [61] optic cup segmentation 50 51 DRISHTI-GS1(OD)6[60], [61] optic disc segmentation 50 51 IDRiD(OD)7[62…”
MethodSSVT: Self-Supervised Vision Transformer For Eye Disease Diagnosis Based On Fundus Images
Wang, J., Kang, M., Liu, Y., et al. (2024) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi56570.2024.10635531 · Cited by 7
“101,064 color fundus photographs from six publicly available datasets (EyePACS [16], Messidor [17], Messidor-2 [18], and APTOS-2019 [19] for DR, Ichallenge-AMD [20] for AMD, REFUGE for GON, and Ichallenge-PM [21] for PM) and two self-collected external datasets from Beijing Tongren Hospital (a GON…”
MethodLorenzo, E., O’Neal, A., Garcia, L. C., Mendoza, K., & Lee, R. E. (2023) · Diabetes Spectrum · doi:10.2337/ds22-0083 · Cited by 7
Marginean, A. N., Groza, A. P., Nicoară, S., Muntean, G. A., Slăvescu, R. R., & Leţia, I. A. (2019) · IEEE International Conference on Intelligent Computer Communication and Processing (ICCP) · doi:10.1109/iccp48234.2019.8959714 · Cited by 7
“Through data augmentation (rotation at 90 ◦ , 180 [18] 4,000 400 2004 Eyepacs 3 [19] 35,126 ZhangLabdata 4 [9] 207,130 4,686 2018 confidence interval.”
BackgroundDisentangling representations of retinal images with generative models
Müller, S., Koch, L. M., Lensch, H. P. A., & Berens, P. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103628 · Cited by 8
“We trained our models on retinal fundus images provided by EyePACS Inc. (Cuadros and Bresnick, 2009; Eye, 2008), an adaptable telemedicine system for diabetic retinopathy screening from California.”
Distributional Shifts In Automated Diabetic Retinopathy Screening
Nandy, J., Hsu, W., & Lee, M. L. (2021) · Proceedings - International Conference on Image Processing · doi:10.1109/icip42928.2021.9506635 · Cited by 6
“This is a public dataset with 35,126 retina images [22].”
BackgroundTowards a Device Agnostic AI for Diabetic Retinopathy Screening: An External Validation Study
Rao, D. P., Sindal, M. D., Sengupta, S., Baskaran, P., Venkatesh, R., Sivaraman, A., & Savoy, F. M. (2022) · Clinical ophthalmology · doi:10.2147/opth.s369675 · Cited by 7
“The training set consisted of 52,894 images of which 34,278 images originated from the Eye Picture Archive Communication System tele-medicine program (EyePACS LLC, Santa Cruz, California).(14) This dataset contained images from multiple ethnicities and desktop-based cameras.”
MethodPseudo-Labeling for Small Lesion Detection on Diabetic Retinopathy Images
Chen, Q., Liu, P., Ni, J., Cao, Y., Liu, B., & Zhang, H. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9207193 · Cited by 6
“EyePACS [24] is a well-known large scale dataset for DR Diagnosis in ICDRDMEDSS standard.”
MethodPreventive Systems for the Late Complications of Diabetes
Ładyżyński, P., Wojcicki, J. M., & Foltyński, P. (2011) · Journal of Applied Biomedicine · doi:10.1016/s0208-5216(11)70007-9 · Cited by 6
“TOSCA [46], eyePACS [47], the Spanish CESGA-CHUS [48] or the Alberta teleophthalmology program [49].”
MethodExploiting Uncertainties from Ensemble Learners to Improve Decision-Making in Healthcare AIPreprint
Tan, Y., Jin, B., Yue, X., Chen, Y., & Sangiovanni-Vincentelli, A. L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.06063 · Cited by 8
“3, we used two popular collections of diabetic retinopathy image data, the Kaggle Diabetic Retinopathy dataset [22] (hereafter referred to as “Kaggle-DR”) and the Messidor-2 dataset [23], each respectively consisting of 88702 and 1748 high resolution images.”
MethodBiswas, S., Khan, M. A. A., Ali, M. H., Rohdin, J., Pramanik, S., Khan, M. I. A., Chakravarty, S. K., & Pramanik, B. K. (2025) · Life · doi:10.3390/life15091473 · Cited by 5
“For training and testing ADRCs, we used three publicly available datasets: Kaggle EyePACS [71,72], IDRiD-DR [73], and Messidor-2 [74].”
Shanthala, K. V., & Kundur, N. C. (2026) · Engineering Technology & Applied Science Research · doi:10.48084/etasr.15311 · Cited by 5
“The APTOS 2019 [13] dataset contains 5,590 images with refined annotations from expert ophthalmologists, while Messidor-2 [14] provides 1,748 images with detailed quality assessments and standardized grading protocols.”
Kumar, S. V., Gidijala, K., Karanam, S. R., & Sagar, N. S. S. (2026) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-026-10564-0 · Cited by 5
“In this context, the large number of well-annotated datasets (e.g., EyePACS [7], Messidor [8], DIARETDB1 [9], and APTOS 2019 [10]) enables rapid progress on new research lines, with a relatively standardized evaluation code across studies.”
Rao, D. P., Savoy, F. M., Sivaraman, A., et al. (2024) · Indian Journal of Ophthalmology · doi:10.4103/ijo.ijo_2151_23 · Cited by 5
“[18,19] This could partly explain the absence of bias in this algorithm when used on both dark and lightly pigmented fundi.”
MethodRepurposing the Image Generative Potential: Exploiting GANs to Grade Diabetic Retinopathy
Poles, I., D’Arnese, E., Cellamare, L. G., Santambrogio, M. D., & Yi, D. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00236 · Cited by 5
“Unfortunately, it typically assumes labeled and highly balanced source retinal data, which may not always be the case [8].”
Peris‐Martínez, C., Shaha, A., Clarida, W., Amelon, R. E., Hernáez-Ortega, M. C., Návea, A., Morales-Olivas, J., Dolz‐Marco, R., Pérez-Jordá, P., Verbraak, F. D., et al. (2020) · Archivos de la Sociedad Española de Oftalmología · doi:10.1016/j.oftal.2020.08.007 · Cited by 5
ANALYSIS OF TRANSFER LEARNING FOR SELECT RETINAL DISEASE CLASSIFICATION
Gelman, R., & Fernandez‐Granda, C. (2021) · Retina · doi:10.1097/iae.0000000000003282 · Cited by 5
Multi-scale multi-attention network for diabetic retinopathy grading
Xia, H., Long, J., Song, S., & Tan, Y. (2023) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ad111d · Cited by 5
Bhatta, S. (2023) · Journal of Electronics Electromedical Engineering and Medical Informatics · doi:10.35882/jeeemi.v5i4.326 · Cited by 5
An Integrated Image Processing and Deep Learning Approach for Diabetic Retinopathy Classification
Choudhury, A. R., Bhattacharya, D., Debnath, A., et al. (2020) · Communications in computer and information science · doi:10.1007/978-981-15-3666-3_1 · Cited by 5
Ishihara, K., & Matsumoto, K. (2023) · Bioengineering · doi:10.3390/bioengineering10121383 · Cited by 6
“In this study, we used the retinal fundus image dataset EyePACS [24] to diagnose DR.”
MethodDragan, C.-M., Saad, M. M., Rehmani, M. H., & O’Reilly, R. (2023) · Studies in computational intelligence · doi:10.1007/978-3-031-46341-9_4 · Cited by 6
Small Scale Feature Propagation Using Deep Residual Learning for Diabetic Retinopathy Classification
Metan, A. C., Lambert, A. J., & Pickering, M. R. (2019) · International Conference on Image, Vision and Computing (ICIVC) · doi:10.1109/icivc47709.2019.8981096 · Cited by 5
Correcting Automatic Cataract Diagnosis Systems Against Noisy/Blur Environment
Pratap, T., & Kokil, P. (2020) · National Conference on Communications · doi:10.1109/ncc48643.2020.9055998 · Cited by 5
“The fundus retinal images are collected from the open access datasets such as high resolution fundus (HRF) image database [17], e-ophtha: a color fundus image database [18], Indian diabetic retinopathy image dataset (IDRiD) [19], digital retinal images for optic nerve segmentation database (DRIONS-DB) [20], fundus ima…”
BackgroundDiagnosing of Diabetic Retinopathy with Image Dehazing and Capsule Network
Köse, U., Deperlioğlu, Ö., Alzubi, J. A. A., et al. (2020) · Studies in computational intelligence · doi:10.1007/978-981-15-6325-6_9 · Cited by 5
mBRSET: A Portable Retina Fundus Photos Benchmark Dataset for Clinical and Demographic PredictionPreprint
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. (2024) · medRxiv · doi:10.1101/2024.07.11.24310293 · Cited by 5
Delavari, P., Özturan, G., Navajas, E. V., Yilmaz, O., & Oruç, İ. (2025) · PLoS ONE · doi:10.1371/journal.pone.0327305 · Cited by 4
Token labeling-guided multi-scale medical image classification
Yan, F., Yan, B., Liang, W. G., & Pei, M. (2023) · Pattern Recognition Letters · doi:10.1016/j.patrec.2023.12.018 · Cited by 4
Diabetic retinopathy identification based on multi-source-free domain adaptation
Zhang, G., Zhang, Z., Sun, B., Yang, W., & Zhang, S.-C. (2024) · International Journal of Ophthalmology · doi:10.18240/ijo.2024.07.03 · Cited by 5
Arunga, S., Morley, K., Kwaga, T., Morley, M., Nakayama, L. F., Mwavu, R., Kaggwa, F., Ssempiira, J., Celi, L. A., Haberer, J. E., et al. (2024) · JMIR Formative Research · doi:10.2196/59914 · Cited by 4
“We also examined the labeling protocols for the following large open-source fundus photo data sets-Messidor [31], BRSET [28], Eye Pacs [32], and IDRiD [33] and did not find documentation indicating whether metadata was used or not used in the labeling process.”
MethodA comprehensive review of retinal disease diagnosis and open access datasets: Fundus and OCT images
Fatima, Z., Dhaliwal, P., & Gupta, D. K. (2024) · Intelligent Decision Technologies · doi:10.3233/idt-241007 · Cited by 4
Artificial Intelligence-Based Screening System for Diabetic Retinopathy in Primary Care
Baget-Bernaldiz, M., Fontoba-Poveda, B., Romero-Aroca, P., Navarro-Gil, R., Hernando-Comerma, A., Bautista-Pérez, Á., Llagostera-Serra, M., Morente-Lorenzo, C., Vizcarro, M., & Mira-Puerto, A. (2024) · Diagnostics · doi:10.3390/diagnostics14171992 · Cited by 6
“Previously, we had used two different retinographies datasets to build and train the AIRS, with a total of 103,815 images: our sample of 15,123 tagged fundus images (different patients from the validation and testing phases) and 88,692 retinal images extracted from EyePACS [30].”
MethodKhandekar, R. B., Zaman, B., Shahwan, S. A., et al. (2014) · Middle East African Journal of Ophthalmology · doi:10.4103/0974-9233.129773 · Cited by 4
“To the best of our knowledge, such a web‐based registry has been created for diabetic patients with data on 10,000 cases.(16)”
BackgroundRobotic Process Automation-Based Glaucoma Screening System: A Framework
Thainimit, S., Chaipayom, P., Gansawat, D., et al. (2021) · Transactions on computational science and computational intelligence · doi:10.1007/978-3-030-71051-4_44 · Cited by 4
Grading Diabetic Retinopathy Severity Using Modern Convolution Neural Networks (CNN)
Lee, A., Khushi, M., Hao, P., et al. (2021) · IEEE International Conference on Digital Health (ICDH) · doi:10.1109/icdh52753.2021.00014 · Cited by 4
Automatic Screening for Ocular Anomalies Using Fundus Photographs
Matta, S. C., Lamard, M., Conze, P.-H., Guilcher, A. L., Ricquebourg, V., Benyoussef, A., Massin, P., Rottier, J.-B., Cochener, B., & Quellec, G. (2021) · Optometry and Vision Science · doi:10.1097/opx.0000000000001845 · Cited by 4
“Alteration 6 (17) 2 (8) 1 (3) 3 (6) 1 (2)”
InfluentialImpacto da Resolução na Detecção de Retinopatia Diabética com uso de Deep Learning
Moreira, F. B., Schaan, B. D., Schneiders, J., et al. (2020) · doi:10.5753/sbcas.2020.11546 · Cited by 4
“Para a validação, eles usaram imagens adicionais do EyePACS e o conjunto de dados Messidor-2 [Decencière et al. 2014].”
BackgroundInfluentialDahlan, K., Suman, P., Rubaltelli, D., Shrivastava, A., Chuck, R. S. H., & Mian, U. K. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20075349 · Cited by 5
“Remote telemedicine screening methods have been validated in multiple studies to be effective and relatively inexpensive methods for evaluating diabetic retinopathy, with both a high sensitivity and specificity for detecting retinopathy [8].”
MethodComputer-aided Cataract Diagnosis with Fundus Retinal Images under Noisy Conditions
Pratap, T., Dhulipalla, V. R., & Kokil, P. (2024) · IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) · doi:10.1109/iatmsi60426.2024.10502479 · Cited by 4
“The fundus retinal images used in this study are collected from the EyePACS dataset [30] under the supervision of experienced ophthalmologists.”
MethodScreening of the Retina in Diabetes Patients by Morphological Means
Xiao, D., & Kanagasingam, Y. (2014) · Springer eBooks · doi:10.1007/978-3-662-44975-2_2 · Cited by 4
Memory efficient Multi-Scale Line Detector architecture for retinal blood vessel segmentation
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“[2] J. Cuadros and G. Bresnick, "EyePACS: an adaptable telemedicine system for diabetic retinopathy screening," 20100210 DCOM- 20100803.”
MethodInfluentialGaze-and-Machine Dual-Driven Attention Fusion Network for Medical Image Classification
Peng, Q., Zhu, S., Su, Y. R., et al. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-0036-9_34 · Cited by 3
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The role of deep learning-based algorithms in retinal image processing: a review
Celik, C., & Yücedağ, İ. (2026) · Multimedia Tools and Applications · doi:10.1007/s11042-026-21181-1 · Cited by 3
Efficient labeling of retinal fundus photographs using deep active learning
Paul, S. K., Pan, I., & Sobol, W. (2022) · Journal of Medical Imaging · doi:10.1117/1.jmi.9.6.064001 · Cited by 3
Basreddy, V., & Veerashetty, S. S. (2023) · International Conference on Integrated Intelligence and Communication Systems (ICIICS) · doi:10.1109/iciics59993.2023.10421217 · Cited by 3
“For evaluating their model, they used the IDRiD dataset, LIQ-EyePACs dataset [36], MESSIDOR-1 [37], and MESSIDOR-2.”
MethodMIDC: Medical image dataset cleaning framework based on deep learning
Yi, S., & Chen, Z. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e38910 · Cited by 3
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Peris‐Martínez, C., Shaha, A., Clarida, W., Amelon, R. E., Hernáez-Ortega, M. C., Návea, A., Morales-Olivas, J., Dolz‐Marco, R., Pérez-Jordá, P., Verbraak, F. D., et al. (2020) · Archivos de la Sociedad Española de Oftalmología (English Edition) · doi:10.1016/j.oftale.2020.08.002 · Cited by 4
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Classification of Diabetic Retinopathy Based on B-ResNet
Zhang, Y. (2022) · IEEE International Conference on Data Science in Cyberspace · doi:10.1109/dsc55868.2022.00085 · Cited by 3
Transformer for medical image analysis
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Parimi, V., Elsner, A. E., Gast, T. J., Chen, Z., Baskaran, K., Alhamami, M. A., Litvin, T. V., Ozawa, G. Y., & Cuadros, J. A. (2024) · Optometry and Vision Science · doi:10.1097/opx.0000000000002096 · Cited by 3
Self-citation", S., Невська, А. О., Pohosian, O. A., Goncharuk, K. O., Sofyna, D. F., Chernenko, O. O., Enterprise, M. P., Tronko, К. M., Kozhan, N. I., & Korol, A. R. (2024) · Oftalmologicheskii Zhurnal · doi:10.31288/oftalmolzh202412731 · Cited by 5
ACE: Anatomically Consistent Embeddings in Composition and Decomposition
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“We evaluate our ACE on chest X-rays and fundus photography, pretraining on ChestX-ray14 [18] and Eye-PACS [4] datasets respectively.”
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“However, extensive works are being conducted around the globe to build blindness prevention programs in clinical settings [3] whereas mTEH has been developed following Action Research methodology to serve in community settings.”
BackgroundHsu, J., Phene, S., Mitani, A., Luo, J., Hammel, N., Krause, J., & Sayres, R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.10858 · Cited by 5
“EyePACS: an adaptable telemedicine system for diabetic retinopathy screening.”
MethodInfluentialDeep Learning from Small Amount of Medical Data with Noisy Labels: A Meta-Learning ApproachPreprint
Algan, G., Ulusoy, İ., Gönül, Ş., Turgut, B. Ö., & Bakbak, B. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.06939 · Cited by 3
“We used diabetic retinopathy (DR) dataset that has 35k high-resolution retina images [10].”
MethodSemi-weakly Supervised Contrastive Representation Learning for Retinal Fundus ImagesPreprint
Yap, B. P., & Ng, B. K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.02122 · Cited by 5
“We train a semi-supervised binary CNN classifier using OIA-ODIR [14] as the labeled dataset and images from Kaggle-EyePACS [15] as unlabeled dataset.”
Diabetic Retinopathy during pregnancy in Hispanic women with latent Toxoplasma gondii infection
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Self-citationA low-light fundus image enhancement method based on GAN
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“The EyePACS [12] fundus image dataset has 35,126 fundus images in the training set and 53,576 fundus images in the test set, and each image has DR grading annotation.”
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Genetic analysis of imaging-derived phenotypes
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A Study of Bilateral Symmetry in Color Fundus Photographs
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“, [48]–[55]), segmentation of anatomical structures such as optic disc, macula, CRBVs (e.”
BackgroundMethodComputer-aided Cataract Grading Under Adversarial Environment
Pratap, T., & Kokil, P. (2022) · International Conference on Signal Processing and Communications (SPCOM) · doi:10.1109/spcom55316.2022.9840821 · Cited by 3
Elsner, A. E. (2023) · Optometry and Vision Science · doi:10.1097/opx.0000000000002029 · Cited by 2
Gao, Y., Ma, C., Guo, L., Zhang, X., & Ji, X. (2023) · Bioengineering · doi:10.3390/bioengineering10080971 · Cited by 2
“In order to maximize the utilization of the generalization bias learned from the fundus image dataset and expedite the model training process, our study employed a large fundus image classification dataset, specifically Kaggle [14], to pre-train the proposed MIL-CT model.”
BackgroundMethodInfluentialAdvances in Teleophthalmology Screening for Diabetic Retinopathy
Song, A., & Borkar, D. S. (2023) · International Ophthalmology Clinics · doi:10.1097/iio.0000000000000505 · Cited by 2
“High-quality OCT image acquisition can be challenging as it requires familiarity with the device, an understanding of retinal anatomy, and skills to direct the patient for precise alignment.”
BackgroundAmerican Society of Retina Specialists Artificial Intelligence Task Force Report
Talcott, K. E., Baxter, S. L., Chen, D. K., Korot, E., Lee, A., Kim, J. E., Modi, Y. S., Moshfeghi, D. M., & Singh, R. P. (2024) · Journal of VitreoRetinal Diseases · doi:10.1177/24741264241247602 · Cited by 2
“21 The macular OCT dataset from Kermany et al 22 also provides 84 000 OCT B-scans for a 4-class classification problem.”
Rusu, A. C., Chistol, R. O., Damian, S.-I., Brînzaniuc, K., & Horvath, K. U. (2024) · BRAIN BROAD RESEARCH IN ARTIFICIAL INTELLIGENCE AND NEUROSCIENCE · doi:10.18662/brain/15.2/576 · Cited by 2
“EyePACS Database (2009) – 88702 fundus images of multiple conditions with grading (Cuadros et al., 2009); 4.”
Background
Metadata
- 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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