ArticleGraefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie2020
Multi-label classification of retinal lesions in diabetic retinopathy for automatic analysis of fundus fluorescein angiography based on deep learning.
Article in Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 84 citations in OpenAlex.
- Intelligent Staging Performance of Diabetic Retinopathy Based on Fundus Fluorescein Angiography Images with Different Angiographic Phases.Bioengineering (Basel, Switzerland) · 2026Article
- Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics.Briefings in bioinformatics · 2026Article
- Automated interpretation of fundus fluorescein angiography with multi-retinal vascular lesion segmentation.Frontiers in medicine · 2026Article
- Federated Deep Learning Approaches for Detecting Ocular Diseases in Medical Imaging: A Systematic Review.Current medical imaging · 2025Article
- Multi-class Classification of Retinal Eye Diseases from Ophthalmoscopy Images Using Transfer Learning-Based Vision Transformers.Journal of imaging informatics in medicine · 2025Article
- The role of artificial intelligence in the diagnosis of diabetic retinopathy through retinal lesion features: a narrative review.Quantitative imaging in medicine and surgery · 2025Review
- A Systematic Review of Advances in AI-Assisted Analysis of Fundus Fluorescein Angiography (FFA) Images: From Detection to Report Generation.Ophthalmology and therapy · 2025Review
- Bibliometric analysis of research on the application of deep learning to ophthalmology.Quantitative imaging in medicine and surgery · 2025Article
- ChatFFA: An ophthalmic chat system for unified vision-language understanding and question answering for fundus fluorescein angiography.iScience · 2024Article
- Autonomous screening for laser photocoagulation in fundus images using deep learning.The British journal of ophthalmology · 2024Article
- FFA-GPT: an automated pipeline for fundus fluorescein angiography interpretation and question-answer.NPJ digital medicine · 2024Article
- Automatic Detection of 30 Fundus Diseases Using Ultra-Widefield Fluorescein Angiography with Deep Experts Aggregation.Ophthalmology and therapy · 2024Article
- Uncovering Language Disparity of ChatGPT on Retinal Vascular Disease Classification: Cross-Sectional Study.Journal of medical Internet research · 2024Article
- Article
- An artificial intelligence system for the whole process from diagnosis to treatment suggestion of ischemic retinal diseases.Cell reports. Medicine · 2023Article
- RDS-DR: An Improved Deep Learning Model for Classifying Severity Levels of Diabetic Retinopathy.Diagnostics (Basel, Switzerland) · 2023Article
- Multivendor fully automatic uncertainty management approaches for the intuitive representation of DME fluid accumulations in OCT images.Medical & biological engineering & computing · 2023Article
- Automatic Multilabel Classification of Multiple Fundus Diseases Based on Convolutional Neural Network With Squeeze-and-Excitation Attention.Translational vision science & technology · 2023Article
- Deep Learning for Diabetic Retinopathy Analysis: A Review, Research Challenges, and Future Directions.Sensors (Basel, Switzerland) · 2022Review
- End-to-end diabetic retinopathy grading based on fundus fluorescein angiography images using deep learning.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2022Article
Corrections and comments
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Authors and funding
12 authors at 2 institutions in 1 country.
Funding
Abstract
purposeTo automatically detect and classify the lesions of diabetic retinopathy (DR) in fundus fluorescein angiography (FFA) images using deep learning algorithm through comparing 3 convolutional neural networks (CNNs).
methodsA total of 4067 FFA images from Eye Center at the Second Affiliated Hospital of Zhejiang University School of Medicine were annotated with 4 kinds of lesions of DR, including non-perfusion regions (NP), microaneurysms, leakages, and laser scars. Three CNNs including DenseNet, ResNet50, and VGG16 were trained to achieve multi-label classification, which means the algorithms could identify 4 retinal lesions above at the same time.
resultsThe area under the curve (AUC) of DenseNet reached 0.8703, 0.9435, 0.9647, and 0.9653 for detecting NP, microaneurysms, leakages, and laser scars, respectively. For ResNet50, AUC was 0.8140 for NP, 0.9097 for microaneurysms, 0.9585 for leakages, and 0.9115 for laser scars. And for VGG16, AUC was 0.7125 for NP, 0.5569 for microaneurysms, 0.9177 for leakages, and 0.8537 for laser scars.
conclusionsExperimental results demonstrate that DenseNet is a suitable model to automatically detect and distinguish retinal lesions in the FFA images with multi-label classification, which lies the foundation of automatic analysis for FFA images and comprehensive diagnosis and treatment decision-making for DR.
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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.