ArticleGraefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie2022
End-to-end diabetic retinopathy grading based on fundus fluorescein angiography images using deep learning.
Article in Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
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Who cites it
19 citing papers in PubMed, 38 citations in OpenAlex.
- Morphological and Functional Alterations in the Mouse Retina After Sodium Fluorescein Administration.Translational vision science & technology · 2026Article
- Intelligent Staging Performance of Diabetic Retinopathy Based on Fundus Fluorescein Angiography Images with Different Angiographic Phases.Bioengineering (Basel, Switzerland) · 2026Article
- Automated interpretation of fundus fluorescein angiography with multi-retinal vascular lesion segmentation.Frontiers in medicine · 2026Article
- [An adaptive multi-label classification model for diabetic retinopathy lesion recognition].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Article
- Generative artificial intelligence for fundus fluorescein angiography interpretation and human expert evaluation.NPJ digital medicine · 2025Article
- A Systematic Review of Advances in AI-Assisted Analysis of Fundus Fluorescein Angiography (FFA) Images: From Detection to Report Generation.Ophthalmology and therapy · 2025Review
- D-GET: Group-Enhanced Transformer for Diabetic Retinopathy Severity Classification in Fundus Fluorescein Angiography.Journal of medical systems · 2025Article
- Evaluation of Systemic Risk Factors in Patients with Diabetes Mellitus for Detecting Diabetic Retinopathy with Random Forest Classification Model.Diagnostics (Basel, Switzerland) · 2024Article
- Article
- FFA-GPT: an automated pipeline for fundus fluorescein angiography interpretation and question-answer.NPJ digital medicine · 2024Article
- Applications of deep learning for detecting ophthalmic diseases with ultrawide-field fundus images.International journal of ophthalmology · 2024Review
- Automatic Diagnosis of Infectious Keratitis Based on Slit Lamp Images Analysis.Journal of personalized medicine · 2023Article
- A Prospective Study on Diabetic Retinopathy Detection Based on Modify Convolutional Neural Network Using Fundus Images at Sindh Institute of Ophthalmology & Visual Sciences.Diagnostics (Basel, Switzerland) · 2023Article
- On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks.Archives of computational methods in engineering : state of the art reviews · 2023Article
- Assessment of a Novel Semi-Automated Algorithm for the Quantification of the Parafoveal Capillary Network.Clinical ophthalmology (Auckland, N.Z.) · 2023Article
- The Role of Medical Image Modalities and AI in the Early Detection, Diagnosis and Grading of Retinal Diseases: A Survey.Bioengineering (Basel, Switzerland) · 2022Review
- The Role of Different Retinal Imaging Modalities in Predicting Progression of Diabetic Retinopathy: A Survey.Sensors (Basel, Switzerland) · 2022Review
- Diabetic Retinopathy Grading by Deep Graph Correlation Network on Retinal Images Without Manual Annotations.Frontiers in medicine · 2022Article
- Artificial intelligence and deep learning in ophthalmology: Current status and future perspectives.Advances in ophthalmology practice and researchReview
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 1 country.
Funding
Abstract
purposeTo develop and validate a deep learning system for diabetic retinopathy (DR) grading based on fundus fluorescein angiography (FFA) images.
methodsA total of 11,214 FFA images from 705 patients were collected to form the internal dataset. Three convolutional neural networks, namely VGG16, RestNet50, and DenseNet, were trained using a nine-square grid input, and heat maps were generated. Subsequently, a comparison between human graders and the algorithm was performed. Lastly, the best model was tested on two external datasets (Xian dataset and Ningbo dataset).
resultsVGG16 performed the best, with a maximum accuracy of 94.17%, and had an AUC of 0.972, 0.922, and 0.994 for levels 1, 2, and 3, respectively. For Xian dataset, our model reached the accuracy of 82.47% and AUC of 0.910, 0.888, and 0.976 for levels 1, 2, and 3. As for Ningbo dataset, the network performed with the accuracy of 88.89% and AUC of 0.972, 0.756, and 0.945 for levels 1, 2, and 3.
conclusionsA deep learning system for DR staging was trained based on FFA images and evaluated through human-machine comparisons as well as external dataset testing. The proposed system will help clinical practitioners to diagnose and treat DR patients, and lay a foundation for future applications of other ophthalmic or general diseases.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.