The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Automatic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362
pROC: an open-source package for R and S+ to analyze and compare ROC curves
Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J.-C., & Müller, M. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-77 · Cited by 13,697
High-performance medicine: the convergence of human and artificial intelligence
Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450
Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., et al. (2016) · JAMA · doi:10.1001/jama.2016.17216 · Cited by 7,697
Wong, W. L., Su, X., Li, X., Cheung, C. M. G., Klein, R., Cheng, C.-Y., & Wong, T. Y. (2014) · The Lancet Global Health · doi:10.1016/s2214-109x(13)70145-1 · Cited by 5,331
Saito, T., & Rehmsmeier, M. (2015) · PLOS ONE · doi:10.1371/journal.pone.0118432 · Cited by 5,309
Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning
Kermany, D. S., Goldbaum, M., Cai, W., Valentim, C. C. S., Liang, H., Baxter, S. L., McKeown, A., Yang, G., Wu, X., Yan, F., et al. (2018) · Cell · doi:10.1016/j.cell.2018.02.010 · Cited by 4,842
A Review on Multi-Label Learning Algorithms
Zhang, M.-L., & Zhou, Z.-H. (2014) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2013.39 · Cited by 3,321
Self-citationEhteshami Bejnordi, B., Veta, M., Johannes van Diest, P., van Ginneken, B., Karssemeijer, N., Litjens, G., van der Laak, J. A. W. M., and the CAMELYON16 Consortium, Hermsen, M., Manson, Q. F., et al. (2017) · JAMA · doi:10.1001/jama.2017.14585 · Cited by 3,293
Self-citationHannun, A. Y., Rajpurkar, P., Haghpanahi, M., Tison, G. H., Bourn, C., Turakhia, M. P., & Ng, A. Y. (2019) · Nature Medicine · doi:10.1038/s41591-018-0268-3 · Cited by 3,148
Cited by
A foundation model for generalizable disease detection from retinal images
Zhou, Y., Chia, M. A., Wagner, S. K., Ayhan, M. S., Williamson, D. J., Struyven, R. R., Liu, T., Xu, M., Lozano, M. G., Woodward-Court, P., et al. (2023) · Nature · doi:10.1038/s41586-023-06555-x · Cited by 1,014
Algorithmic fairness in artificial intelligence for medicine and healthcare
Chen, R. J., Wang, J. J., Williamson, D. F. K., Chen, T. Y., Lipkova, J., Lu, M. Y., Sahai, S., & Mahmood, F. (2023) · Nature Biomedical Engineering · doi:10.1038/s41551-023-01056-8 · Cited by 750
Gkintoni, E., Antonopoulou, H., Sortwell, A., & Halkiopoulos, C. (2025) · Brain Sciences · doi:10.3390/brainsci15020203 · Cited by 309
Ruamviboonsuk, P., Tiwari, R., Sayres, R., Nganthavee, V., Hemarat, K., Kongprayoon, A., Raman, R., Levinstein, B., Liu, Y., Schaekermann, M., et al. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(22)00017-6 · Cited by 250
Self-citationGao, Z., Pan, X., Shao, J., Jiang, X., Su, Z., Jin, K., & Ye, J. (2023) · British Journal of Ophthalmology · doi:10.1136/bjo-2022-321472 · Cited by 93
Metadata
- Authors
- Cen, L.-P., Ji, J., Lin, J.-W., Ju, S.-T., Lin, H.-J., Li, T.-P., Wang, Y., Yang, J.-F., Liu, Y.-F., Tan, S., Tan, L., Li, D., Wang, Y., Zheng, D., Xiong, Y., Wu, H., Jiang, J., Wu, Z., Huang, D., Shi, T., Chen, B., Yang, J., Zhang, X., Luo, L., Huang, C., Zhang, G., Huang, Y., Ng, T. K., Chen, H., Chen, W., Pang, C. P., & Zhang, M.
- Venue
- Nature Communications
- Published
- 2021-08-10
- Volume
- 12
- Issue
- 1
- Pages
- 4828-4828
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W3191934798
- Cited by
- 346
- References
- 44
- FWCI
- 23.81
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Automatic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks. https://app.cochat.ai/papers/10.1038/s41467-021-25138-w
- The paper
- Cen, L.-P., Ji, J., Lin, J.-W., Ju, S.-T., Lin, H.-J., Li, T.-P., Wang, Y., Yang, J.-F., Liu, Y.-F., Tan, S., Tan, L., Li, D., Wang, Y., Zheng, D., Xiong, Y., Wu, H., Jiang, J., Wu, Z., Huang, D., . . . Zhang, M. (2021). Automatic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks. Nature Communications. https://doi.org/10.1038/s41467-021-25138-w
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.