{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T14:32:04Z","timestamp":1781533924236,"version":"3.54.5"},"reference-count":69,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T00:00:00Z","timestamp":1779926400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001795","name":"University of Southern Queensland","doi-asserted-by":"publisher","award":["10 Research Training Program (RTP) Stipend"],"award-info":[{"award-number":["10 Research Training Program (RTP) Stipend"]}],"id":[{"id":"10.13039\/501100001795","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001795","name":"University of Southern Queensland","doi-asserted-by":"publisher","award":["USQ198 UniSQ International Fees Research Scholarship"],"award-info":[{"award-number":["USQ198 UniSQ International Fees Research Scholarship"]}],"id":[{"id":"10.13039\/501100001795","id-type":"DOI","asserted-by":"publisher"}]},{"award":["10 Research Training Program (RTP) Stipend"],"award-info":[{"award-number":["10 Research Training Program (RTP) Stipend"]}],"id":[{"id":"https:\/\/ror.org\/04sjbnx57","id-type":"ROR","asserted-by":"publisher"}]},{"award":["USQ198 UniSQ International Fees Research Scholarship"],"award-info":[{"award-number":["USQ198 UniSQ International Fees Research Scholarship"]}],"id":[{"id":"https:\/\/ror.org\/04sjbnx57","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Diabetic retinopathy (DR) and diabetic macular oedema (DME) are two of the most significant preventable contributors to blindness in the adult population worldwide, yet current automated screening systems typically address each condition in isolation and rely on a single imaging modality. In this study, we propose a deep learning model that simultaneously grades DR severity and detects DME by fusing paired colour fundus and optical coherence tomography (OCT) images acquired from the same eye during the same clinical visit. Our architecture employs two parallel EfficientNet-B0 backbones pre-trained on ImageNet, one for each modality, whose 1280-dimensional feature vectors are concatenated into a 2560-dimensional joint representation. This fused representation passes through a shared fully connected block before branching into a three-class DR classification head and a binary DME detection head. We train and evaluate the model on a private dataset of 425 paired fundus and OCT eye images (850 images). The proposed architecture adopts feature-level fusion, in which modality-specific deep features are independently extracted from fundus and OCT images using separate convolutional backbones and subsequently concatenated to form a joint representation for multi-task learning. On the held-out test set (n= 85), the fusion model achieves 82.4% DR accuracy (area under the receiver operating characteristic curve [AUC] = 0.929, macro sensitivity = 0.81, macro specificity = 0.905) and 97.6% DME accuracy (AUC = 0.999, sensitivity = 0.833, specificity = 1.000). The fusion model detects 10 of 12 DME-positive eyes compared with only 7 of 12 for either the fundus-only or OCT-only baselines, representing a 43% relative improvement in DME sensitivity. Stratified five-fold cross-validation (n = 425 aggregated predictions) corroborates these findings, with the fusion model reaching 87.1% DR accuracy (AUC = 0.978) and 99.1% DME accuracy (AUC = 1.000). Gradient-weighted class activation mapping visualisations confirm that the fundus branch attends to clinically relevant macular lesions, whereas the OCT branch highlights retinal layer disruptions and subretinal fluid, providing interpretability. To the best of our knowledge, the proposed MultiRetNet is the first lightweight, task-specific multimodal architecture to jointly grade DR severity and detect DME from paired same-eye, same-visit fundus and OCT images through explicit feature-level fusion within a single end-to-end multi-task framework, distinct from recent generalist ophthalmic foundation models, supporting the value of multimodal fusion for comprehensive diabetic eye screening pending external validation.<\/jats:p>","DOI":"10.3390\/jimaging12060236","type":"journal-article","created":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T15:57:18Z","timestamp":1780070238000},"page":"236","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MultiRetNet: A Lightweight Explainable AI Approach to Diabetic Retinopathy Grading and DME Detection Using Fundus\u2013OCT Fusion"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3578-8307","authenticated-orcid":false,"given":"Saad","family":"Islam","sequence":"first","affiliation":[{"name":"Artificial Intelligence Applications Laboratory, School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, QLD 4300, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2290-6749","authenticated-orcid":false,"given":"Ravinesh C.","family":"Deo","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Applications Laboratory, School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, QLD 4300, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2689-8552","authenticated-orcid":false,"given":"U. Rajendra","family":"Acharya","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Applications Laboratory, School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, QLD 4300, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5117-8333","authenticated-orcid":false,"given":"Prabal Datta","family":"Barua","sequence":"additional","affiliation":[{"name":"School of Business, University of Southern Queensland, Springfield, QLD 4300, Australia"},{"name":"Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4964-7556","authenticated-orcid":false,"given":"Jeffrey","family":"Soar","sequence":"additional","affiliation":[{"name":"School of Business, University of Southern Queensland, Springfield, QLD 4300, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/s13167-023-00314-8","article-title":"Diabetic retinopathy as the leading cause of blindness and early predictor of cascading complications\u2014risks and mitigation","volume":"14","author":"Kropp","year":"2023","journal-title":"Epma J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1608","DOI":"10.1016\/j.ophtha.2018.04.007","article-title":"Guidelines on diabetic eye care: The international council of ophthalmology recommendations for screening, follow-up, referral, and treatment based on resource settings","volume":"125","author":"Wong","year":"2018","journal-title":"Ophthalmology"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"412","DOI":"10.2337\/dc16-2641","article-title":"Diabetic retinopathy: A position statement by the American Diabetes Association","volume":"40","author":"Solomon","year":"2017","journal-title":"Diabetes Care"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/S2213-8587(16)30052-3","article-title":"Diabetic macular oedema","volume":"5","author":"Tan","year":"2017","journal-title":"Lancet Diabetes Endocrinol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/S2213-8587(19)30411-5","article-title":"Screening for diabetic retinopathy: New perspectives and challenges","volume":"8","author":"Vujosevic","year":"2020","journal-title":"Lancet Diabetes Endocrinol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2509","DOI":"10.2337\/dc18-0147","article-title":"An automated grading system for detection of vision-threatening referable diabetic retinopathy on the basis of color fundus photographs","volume":"41","author":"Li","year":"2018","journal-title":"Diabetes Care"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ahn, S.J., and Kim, Y.H. (2024). Clinical applications and future directions of smartphone fundus imaging. Diagnostics, 14.","DOI":"10.3390\/diagnostics14131395"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Nazir, T., Irtaza, A., Javed, A., Malik, H., Hussain, D., and Naqvi, R.A. (2020). Retinal image analysis for diabetes-based eye disease detection using deep learning. Appl. Sci., 10.","DOI":"10.3390\/app10186185"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"495","DOI":"10.13052\/jmm1550-4646.20210","article-title":"Deep dive into diabetic retinopathy identification: A deep learning approach with blood vessel segmentation and lesion detection","volume":"20","author":"Upreti","year":"2024","journal-title":"J. Mob. Multimed."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Xu, T., Zhang, J., Wang, M., He, H., and Zou, X. (2025). On the Limits of Uncertainty-Aware Fine-Tuning for Robust Diabetic Retinopathy Screening. Proceedings of the International Workshop on Ophthalmic Medical Image Analysis, Springer.","DOI":"10.1007\/978-3-032-10351-2_9"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1007\/s40123-026-01322-3","article-title":"Artificial Intelligence-Based Medical Devices for Diabetic Retinopathy Screening in the European Union","volume":"15","author":"Grzybowski","year":"2026","journal-title":"Ophthalmol. Ther."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"962","DOI":"10.1016\/j.ophtha.2017.02.008","article-title":"Automated identification of diabetic retinopathy using deep learning","volume":"124","author":"Gargeya","year":"2017","journal-title":"Ophthalmology"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1038\/s41746-018-0040-6","article-title":"Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices","volume":"1","author":"Lavin","year":"2018","journal-title":"npj Digit. Med."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1001\/jamaophthalmol.2016.5877","article-title":"Optical coherence tomographic angiography in type 2 diabetes and diabetic retinopathy","volume":"135","author":"Ting","year":"2017","journal-title":"JAMA Ophthalmol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"34387","DOI":"10.1109\/ACCESS.2020.2974158","article-title":"Accurate detection of non-proliferative diabetic retinopathy in optical coherence tomography images using convolutional neural networks","volume":"8","author":"Ghazal","year":"2020","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"920713","DOI":"10.1155\/2013\/920713","article-title":"Structural changes in individual retinal layers in diabetic macular edema","volume":"2013","author":"Murakami","year":"2013","journal-title":"J. Diabetes Res."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.1016\/j.cell.2018.02.010","article-title":"Identifying medical diagnoses and treatable diseases by image-based deep learning","volume":"172","author":"Kermany","year":"2018","journal-title":"Cell"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"5041","DOI":"10.1167\/iovs.08-2231","article-title":"A severity scale for diabetic macular edema developed from ETDRS data","volume":"49","author":"Gangnon","year":"2008","journal-title":"Investig. Ophthalmol. Vis. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"434560","DOI":"10.1155\/2013\/434560","article-title":"The diagnostic function of OCT in diabetic maculopathy","volume":"2013","author":"Sikorski","year":"2013","journal-title":"Mediat. Inflamm."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1167\/tvst.9.2.46","article-title":"An intelligent optical coherence tomography-based system for pathological retinal cases identification and urgent referrals","volume":"9","author":"Wang","year":"2020","journal-title":"Transl. Vis. Sci. Technol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"23024","DOI":"10.1038\/s41598-021-02479-6","article-title":"A deep learning model for identifying diabetic retinopathy using optical coherence tomography angiography","volume":"11","author":"Ryu","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1109\/MSP.2017.2738401","article-title":"Deep Multimodal Learning: A Survey on Recent Advances and Trends","volume":"34","author":"Ramachandram","year":"2017","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1602","DOI":"10.18517\/ijaseit.14.5.11677","article-title":"Deep Learning-based Method in Multimodal Data for Diabetic Retinopathy Detection","volume":"14","author":"Wardhani","year":"2024","journal-title":"Int. J. Adv. Sci. Eng. Inf. Technol."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Bodapati, J.D., Naralasetti, V., Shareef, S.N., Hakak, S., Bilal, M., Maddikunta, P.K.R., and Jo, O. (2020). Blended Multi-Modal Deep ConvNet Features for Diabetic Retinopathy Severity Prediction. Electronics, 9.","DOI":"10.3390\/electronics9060914"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1186\/s12938-022-01018-2","article-title":"Application of artificial intelligence-based dual-modality analysis combining fundus photography and optical coherence tomography in diabetic retinopathy screening in a community hospital","volume":"21","author":"Liu","year":"2022","journal-title":"Biomed. Eng. OnLine"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Islam, S., Deo, R.C., Barua, P.D., Soar, J., and Acharya, U.R. (2025). Novel Deep Learning Model for Glaucoma Detection Using Fusion of Fundus and Optical Coherence Tomography Images. Sensors, 25.","DOI":"10.3390\/s25144337"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1186\/s40662-015-0026-2","article-title":"Epidemiology of Diabetic Retinopathy, Diabetic Macular Edema and Related Vision Loss","volume":"2","author":"Lee","year":"2015","journal-title":"Eye Vis."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1734","DOI":"10.1016\/j.ophtha.2005.05.023","article-title":"Three-Dimensional Retinal Imaging with High-Speed Ultrahigh-Resolution Optical Coherence Tomography","volume":"112","author":"Wojtkowski","year":"2005","journal-title":"Ophthalmology"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1007\/s11517-018-1915-z","article-title":"The Possibility of the Combination of OCT and Fundus Images for Improving the Diagnostic Accuracy of Deep Learning for Age-Related Macular Degeneration: A Preliminary Experiment","volume":"57","author":"Yoo","year":"2019","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Aslam, M.A., Salik, M.N., Chughtai, F., Ali, N., Dar, S.H., and Khalil, T. (2019). Image classification based on mid-level feature fusion. Proceedings of the 2019 15th International Conference on Emerging Technologies (ICET), IEEE.","DOI":"10.1109\/ICET48972.2019.8994721"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Vairetti, C., Maldonado, S., Cuitino, L., and Urzua, C.A. (2024). Interpretable Multimodal Classification for Age-Related Macular Degeneration Diagnosis. PLoS ONE, 19.","DOI":"10.1371\/journal.pone.0311811"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1038\/s41586-023-06555-x","article-title":"A foundation model for generalizable disease detection from retinal images","volume":"622","author":"Zhou","year":"2023","journal-title":"Nature"},{"key":"ref_33","unstructured":"Qiu, J., Wu, J., Wei, H., Shi, P., Zhang, M., Sun, Y., Li, L., Liu, H., Liu, H., and Hou, S. (2023). Visionfm: A multi-modal multi-task vision foundation model for generalist ophthalmic artificial intelligence. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1038\/s41746-025-01772-2","article-title":"A multimodal visual\u2013language foundation model for computational ophthalmology","volume":"8","author":"Shi","year":"2025","journal-title":"npj Digit. Med."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","article-title":"Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization","volume":"128","author":"Selvaraju","year":"2020","journal-title":"Int. J. Comput. Vis."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2211","DOI":"10.1001\/jama.2017.18152","article-title":"Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes","volume":"318","author":"Ting","year":"2017","journal-title":"JAMA"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"176630","DOI":"10.1109\/ACCESS.2024.3477420","article-title":"Retinal Health Screening Using Artificial Intelligence with Digital Fundus Images: A Review of the Last Decade (2012\u20132023)","volume":"12","author":"Islam","year":"2024","journal-title":"IEEE Access"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Chow, J.C. (2025). Quantum computing and machine learning in medical decision-making: A comprehensive review. Algorithms, 18.","DOI":"10.3390\/a18030156"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1038\/s41746-025-01725-9","article-title":"Artificial intelligence should genuinely support clinical reasoning and decision making to bridge the translational gap","volume":"8","author":"Sokol","year":"2025","journal-title":"npj Digit. Med."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Oei, S., Bakkes, T., Mischi, M., Bouwman, R., Van Sloun, R., and Turco, S. (2025). Artificial intelligence in clinical decision support and the prediction of adverse events. Front. Digit. Health, 7.","DOI":"10.3389\/fdgth.2025.1403047"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Chow, J.C., and Li, K. (2026). From Dialogue Systems to Autonomous Agents: A Modeling Framework for Ethical Generative AI in Healthcare. Information, 17.","DOI":"10.3390\/info17040361"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Hasan, M. (2024). Deep learning for breast cancer detection: Comparative analysis of ConvNeXT and EfficientNet. Proceedings of the 2024 27th International Conference on Computer and Information Technology (ICCIT), IEEE.","DOI":"10.1109\/ICCIT64611.2024.11021905"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Li, F.-F. (2009). Imagenet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1299","DOI":"10.1109\/TMI.2016.2535302","article-title":"Convolutional neural networks for medical image analysis: Full training or fine tuning?","volume":"35","author":"Tajbakhsh","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_45","unstructured":"Raghu, M., Zhang, C., Kleinberg, J., and Bengio, S. (2019). Transfusion: Understanding transfer learning for medical imaging. Adv. Neural Inf. Process. Syst., 32."},{"key":"ref_46","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 6\u201311). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. Proceedings of the 32nd International Conference on Machine Learning (ICML), Lille, France."},{"key":"ref_47","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_48","unstructured":"Bengio, Y., Goodfellow, I., and Courville, A. (2017). Deep Learning, MIT Press."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_50","unstructured":"Loshchilov, I., and Hutter, F. (2019, January 6\u20139). Decoupled Weight Decay Regularization. Proceedings of the 7th International Conference on Learning Representations (ICLR), New Orleans, LA, USA."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/j.ipm.2009.03.002","article-title":"A systematic analysis of performance measures for classification tasks","volume":"45","author":"Sokolova","year":"2009","journal-title":"Inf. Process. Manag."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2283","DOI":"10.1002\/sim.1768","article-title":"Sample size and power for McNemar\u2019s test with clustered data","volume":"23","year":"2004","journal-title":"Stat. Med."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10489-025-07036-6","article-title":"PRANet: Pathological relationship perception and dual attention guided network for diabetic retinopathy grading","volume":"56","author":"Guo","year":"2026","journal-title":"Appl. Intell."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"100722","DOI":"10.1016\/j.xops.2025.100722","article-title":"Autonomous screening for diabetic macular edema using deep learning processing of retinal images","volume":"5","author":"Bressler","year":"2025","journal-title":"Ophthalmol. Sci."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Zhang, W., Belcheva, V., and Ermakova, T. (2025). Interpretable Deep Learning for Diabetic Retinopathy: A Comparative Study of CNN, ViT, and Hybrid Architectures. Computers, 14.","DOI":"10.3390\/computers14050187"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Emara, A.H.M., Alkhateeb, J.H., Atteia, G., Turani, A., Zraqou, J., Elsawaf, Z., and Jameel, A. (2025). Early prediction of diabetic retinopathy using a multimodal deep learning framework integrating fundus and OCT imaging. Front. Med., 12.","DOI":"10.3389\/fmed.2025.1741146"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Aghabeigi Alooghareh, M.M., Sheikhey, M.M., Sahafi, A., Pirnejad, H., and Naemi, A. (2025). Deep Learning for Comprehensive Analysis of Retinal Fundus Images: Detection of Systemic and Ocular Conditions. Bioengineering, 12.","DOI":"10.3390\/bioengineering12080840"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"100755","DOI":"10.1016\/j.xops.2025.100755","article-title":"Diabetic retinopathy assessment through multitask learning approach on heterogeneous fundus image datasets","volume":"5","author":"Wu","year":"2025","journal-title":"Ophthalmol. Sci."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1186\/s12967-024-05141-7","article-title":"Machine learning and optical coherence tomography-derived radiomics analysis to predict persistent diabetic macular edema in patients undergoing anti-VEGF intravitreal therapy","volume":"22","author":"Meng","year":"2024","journal-title":"J. Transl. Med."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"304","DOI":"10.2337\/dc23-0993","article-title":"Performance of Artificial Intelligence in Detecting Diabetic Macular Edema From Fundus Photography and Optical Coherence Tomography Images: A Systematic Review and Meta-analysis","volume":"47","author":"Lam","year":"2024","journal-title":"Diabetes Care"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"42361","DOI":"10.1109\/ACCESS.2023.3272228","article-title":"A lightweight robust deep learning model gained high accuracy in classifying a wide range of diabetic retinopathy images","volume":"11","author":"Raiaan","year":"2023","journal-title":"IEEE Access"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1433","DOI":"10.1038\/s41433-021-01552-8","article-title":"Deep learning-based automated detection for diabetic retinopathy and diabetic macular oedema in retinal fundus photographs","volume":"36","author":"Li","year":"2022","journal-title":"Eye"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"3242","DOI":"10.1038\/s41467-021-23458-5","article-title":"A deep learning system for detecting diabetic retinopathy across the disease spectrum","volume":"12","author":"Dai","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Hossen, M.S., Reza, A.A., and Mishu, M.C. (2020). An automated model using deep convolutional neural network for retinal image classification to detect diabetic retinopathy. Proceedings of the International Conference on Computing Advancements, ACM.","DOI":"10.1145\/3377049.3377067"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Jiang, H., Yang, K., Gao, M., Zhang, D., Ma, H., and Qian, W. (2019). An interpretable ensemble deep learning model for diabetic retinopathy disease classification. 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Ophthalmol."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1016\/j.ophtha.2017.10.031","article-title":"Fully Automated Detection and Quantification of Macular Fluid in OCT Using Deep Learning","volume":"125","author":"Schlegl","year":"2018","journal-title":"Ophthalmology"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1342","DOI":"10.1038\/s41591-018-0107-6","article-title":"Clinically Applicable Deep Learning for Diagnosis and Referral in Retinal Disease","volume":"24","author":"Ledsam","year":"2018","journal-title":"Nat. Med."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"2402","DOI":"10.1001\/jama.2016.17216","article-title":"Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs","volume":"316","author":"Gulshan","year":"2016","journal-title":"JAMA"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/6\/236\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T04:19:51Z","timestamp":1780546791000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/6\/236"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,28]]},"references-count":69,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["jimaging12060236"],"URL":"https:\/\/doi.org\/10.3390\/jimaging12060236","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,28]]}}}