ArticleNature communications2021
Automatic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 119 papers, 2 of them syntheses that pooled it.
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Who cites it
119 citing papers in PubMed, 2 syntheses or guidelines pooled it, 320 citations in OpenAlex.
- Performance of artificial intelligence for the detection of pathological myopia from colour fundus images: a systematic review and meta-analysis.Eye (London, England) · 2024Pooled it
- Test accuracy of artificial intelligence-based grading of fundus images in diabetic retinopathy screening: A systematic review.Journal of medical screening · 2023Pooled it
- Automated Retinal Vascular Analysis Reveals Response to Acetazolamide in Idiopathic Intracranial Hypertension.Translational vision science & technology · 2025Trial
- RetinoMamba-AnatomyQuery: Anatomy-Guided Query Learning for Efficient Vision Impairment Screening from Retinal Fundus Images.Life (Basel, Switzerland) · 2026Article
- Genetic discovery using deep learning-derived optic nerve integrity phenotypes.medRxiv : the preprint server for health sciences · 2026Article
- Machine Learning-Based Classification of Retinitis Pigmentosa from Color Fundus Images: A Reproducible Benchmark and Screening-Oriented Pipeline.Vision (Basel, Switzerland) · 2026Article
- An explainable biomedical foundation model via large-scale concept-enhanced vision-language pretraining.Nature biomedical engineering · 2026Article
- Swin-DRNet: A robust transformer framework for diabetic retinopathy screening under heterogeneous imaging conditions.Scientific reports · 2026Article
- A generalist biomedical vision-language model via multi-CLIP knowledge distillation.Nature communications · 2026Article
- Fundus to fluorescein angiography video generation as a retinal generative foundation model.NPJ digital medicine · 2026Article
- RetCond: A Conditional Diffusion Model for Self-Explanatory Multi-Class Fundus Image Classification.Journal of medical systems · 2026Article
- Impact of artificial intelligence on the availability, accessibility, acceptability and quality of ophthalmic disease screening services: a scoping review.The British journal of ophthalmology · 2026Article
- Towards generalizable AI in medicine via Generalist-Specialist Collaboration.Nature biomedical engineering · 2026Article
- Key concept learning for medical vision language model with reasoning capabilities.NPJ digital medicine · 2026Article
- Early retinal disease detection from fundus images using deep neural networks.Scientific reports · 2026Article
- Deep learning detection of retinitis pigmentosa inheritance forms through synthetic data expansion of a rare disease dataset.Scientific reports · 2026Article
- Boosting foundation models for rare eye disease diagnosis via a multimodal text-to-image generative framework.NPJ digital medicine · 2026Article
- Automated detection and segmentation of Weiss ring in fundus photography images using deep learning.Scientific reports · 2026Article
- Macular hole detection and segmentation on fundus photography using large multimodal generative models for synthetic augmentation.BMC ophthalmology · 2026Article
- A test-time clinically adaptive framework for detecting multiple fundus diseases harnessing ophthalmic foundation models.NPJ digital medicine · 2026Article
59 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
32 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Retinal fundus diseases can lead to irreversible visual impairment without timely diagnoses and appropriate treatments. Single disease-based deep learning algorithms had been developed for the detection of diabetic retinopathy, age-related macular degeneration, and glaucoma. Here, we developed a deep learning platform (DLP) capable of detecting multiple common referable fundus diseases and conditions (39 classes) by using 249,620 fundus images marked with 275,543 labels from heterogenous sources. Our DLP achieved a frequency-weighted average F1 score of 0.923, sensitivity of 0.978, specificity of 0.996 and area under the receiver operating characteristic curve (AUC) of 0.9984 for multi-label classification in the primary test dataset and reached the average level of retina specialists. External multihospital test, public data test and tele-reading application also showed high efficiency for multiple retinal diseases and conditions detection. These results indicate that our DLP can be applied for retinal fundus disease triage, especially in remote areas around the world.
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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.