Performance of a Deep-Learning Algorithm vs Manual Grading for Detecting Diabetic Retinopathy in India

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Deep Learning-Assisted Physical Education and Resource Allocation Management in Higher Education

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Overview of Artificial Intelligence Systems in Ophthalmology

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AIM and Business Models of Healthcare

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Deep Learning based Diabetic Retinopathy Detection using Image Processing

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Diabetic retinopathy screening and treatment

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Empowering Future Engineers: Educational Journey from AI Fundamentals to Healthcare Innovations

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Hybrid CNN and Vision Transformer Model for Diabetic Retinopathy Detection

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Deep Learning-Driven Framework for Diabetic Retinopathy Diagnosis and Virtual Ophthalmic Support

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DMAP: A Deep-Model Driven Multi-Agent Framework for Reliable Diagnosis Prediction

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A clinically interpretable deep learning pipeline for diabetic retinopathy classification using EfficientNet, advanced data augmentation and GradCAM

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Artificial Intelligence for Diabetes-related Complications: The Eye as a Window to the Systemic Health

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Diabetic Retinal Overlap Lesion Segmentation Network

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Detection and Classification of Diabetic Retinopathy Using Image Processing and Machine Learning Techniques

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Is it time to consider teleophthalmology as a game-changer in the management of diabetic retinopathy?

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Deep Learning-Based Eye Disease Classification Using Convolutional Neural Networks

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A Health Diagnosis System based on Transfer Learning and Multi-scale Dilated Convolution from Binocular Fundus Image Pairs

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A multi-center prospective evaluation of THEIA to detect diabetic retinopathy (DR) and diabetic macular edema (DME) in the New Zealand screening programPreprint

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A Holistic Perspective in Caring for People with Diabetic Retinopathy

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AIM and Business Models of Healthcare

Dee, E. C., Yu, R. C., Celi, L. A., & Nehal, U. S. (2021) · Springer eBooks · doi:10.1007/978-3-030-58080-3_247-1

AIM in Endocrinology

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Parallel Network - A Deep Learning Approach for Blood Vessel Segmentation in Retinal fundus Images

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Ophthalmic Telemedicine in the Pandemic–Endemic World: Present and Future Perspectives

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Beyond Predictions: Explainability and Learning from Machine Learning

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Performance of Artificial Intelligence in Detecting Diabetic Macular Edema from Fundus Photographs and Optical Coherence Tomography Images: A Systematic Review and Meta-analysis

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Medical Practice and Litigation in the Global South - Towards Achieving Health for All in Nigeria

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OPHTHALMOLOGICAL COMPLICATIONS OF TYPE 2 DIABETES MELLITUS

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Recent Development and Application in Deep Learning for Diabetic Retinopathy Image Classification

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Digital Pathology and Artificial Intelligence for Early Diagnosis of Pediatric Solid Tumors: Implication for Improved Healthcare Strategies

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A Reliable Approach for Automatic Grading of Diabetic Retinopathy from Fundus Images using Feature Extraction and Fusion

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Diagnosis of Diabetic Retinopathy Using Hierarchical Feature Learning and Modified CNN with ACAF

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Advancing Healthcare AI Education Through Cloud Computing: Benchmarking AWS vs. GCP

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Diabetic Retinopathy Detection and Severity Grading Using Deep Learning

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Medical Computer Vision

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Next-Generation Ophthalmology: How Artificial Intelligence Is Shaping the Future of Eye Care

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Artificial Intelligence in Diabetic Retinopathy

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Threats to the Validity of the Predictive System Performance for Ophthalmology

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Overview of Artificial Intelligence Systems in Ophthalmology

Kaothanthong, N., Arjkongharn, N., Vongsa, N., Ruamviboonsuk, V., & Ruamviboonsuk, P. (2025) · Springer eBooks · doi:10.1007/978-3-031-83756-2_3

Enhanced Diabetic Retinopathy Detection and Classification Model Using SqueezeNet with Whale Optimization Algorithm

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Diabetic Retinopathy Stages Classification Using Advance Deep Learning Models

Sirisha, B. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-4226-7_21

Investigating the effectiveness of convolutional neural network on retinal disease diagnosis using MURED dataset

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Enhancing Early Diabetic Retinopathy Detection: Explainable AI for Microaneurysm Identification

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Feasibility and Equity of AI-Driven Models for Diabetic Retinopathy Screening in Sub-Saharan Africa: A Systematic Review and Meta-Analysis

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Additional file 3 of Advancing healthcare with artificial intelligence: diagnostic accuracy of machine learning algorithm in diagnosis of diabetic retinopathy in the Brazilian population

dos Reis, M. A., Künas, C. A., da Silva Araújo, T., Schneiders, J., de Azevedo, P. B., Nakayama, L. F., Rados, D. R. V., Umpierre, R. N., Berwanger, O. B., Lavinsky, D., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26944017

Additional file 1 of Advancing healthcare with artificial intelligence: diagnostic accuracy of machine learning algorithm in diagnosis of diabetic retinopathy in the Brazilian population

dos Reis, M. A., Künas, C. A., da Silva Araújo, T., Schneiders, J., de Azevedo, P. B., Nakayama, L. F., Rados, D. R. V., Umpierre, R. N., Berwanger, O. B., Lavinsky, D., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26944011

Additional file 4 of Advancing healthcare with artificial intelligence: diagnostic accuracy of machine learning algorithm in diagnosis of diabetic retinopathy in the Brazilian population

dos Reis, M. A., Künas, C. A., da Silva Araújo, T., Schneiders, J., de Azevedo, P. B., Nakayama, L. F., Rados, D. R. V., Umpierre, R. N., Berwanger, O., Lavinsky, D., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26944020

Additional file 2 of Advancing healthcare with artificial intelligence: diagnostic accuracy of machine learning algorithm in diagnosis of diabetic retinopathy in the Brazilian population

dos Reis, M. A., Künas, C. A., da Silva Araújo, T., Schneiders, J., de Azevedo, P. B., Nakayama, L. F., Rados, D. R. V., Umpierre, R. N., Berwanger, O. B., Lavinsky, D., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26944014

Additional file 5 of Advancing healthcare with artificial intelligence: diagnostic accuracy of machine learning algorithm in diagnosis of diabetic retinopathy in the Brazilian population

dos Reis, M. A., Künas, C. A., da Silva Araújo, T., Schneiders, J., de Azevedo, P. B., Nakayama, L. F., Rados, D. R. V., Umpierre, R. N., Berwanger, O., Lavinsky, D., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26944023

Additional file 1 of Automatic diagnosis of keratitis using object localization combined with cost-sensitive deep attention convolutional neural network

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Deep Learning-based Retinal Image Analysis for the Detection of Diabetic Retinopathy, Cataract, and Glaucoma

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AI-Based Innovative Activation Function for Deep Learning-Based DR Detection in Ophthalmology

Veluri, S., Jadala, V. C., & Goel, S. (2025) · International Conference on Applied Artificial Intelligence and Computing (ICAAIC) · doi:10.1109/icaaic64647.2025.11330760

AI and Diabetic Retinopathy: Methods and Results

Esserkassi, B., Eddarouich, S., & Bourouhou, A. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-14430-0_12

A cognitive learning approach for severity classification of diabetic retinopathy using voting-based selection of deep models

Ahmed, F. A., Ullah, M. S., Khan, M. A., Brahim, G. B., & Shabaz, M. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-40379-8.00013-2

AI-Driven Diagnostics vs. Clinician Assessment in Diabetic Retinopathy: A Comparative Analysis at a Secondary Eye Care Centre

Ravindran, R. S., Khadar, S. M. A., Ramasamy, K., Khan, A., Gopalakrishnan, N., Uduman, M. S., Balakrishnan, H., Ravindran, M., & Ramakrishnan, R. (2026) · Ophthalmic Epidemiology · doi:10.1080/09286586.2026.2641114

Data & AI-driven disruption : identification of data-driven solution business model patterns in the context of business model innovation

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Classification of retinopathy images using augmentation and transfer learning

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Revolutionizing Ophthalmology with Internet of Medical Things Applications

Espaillat, A. (2026) · Springer eBooks · doi:10.1007/978-3-032-19336-0_15

Cross-Dataset Binary and Ordinal Diabetic Retinopathy Classification with Deep Learning and Statistical Evaluation

Presitha, M., & Somasundaram, K. (2026) · International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET) · doi:10.1109/wispnet69615.2026.11489401

Diagnosis of Diabetic Retinopathy Using Transfer Learning and Graph Neural Networks with Fundus Imaging

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