Journal article
Deep-learning models for the detection and incidence prediction of chronic kidney disease and type 2 diabetes from retinal fundus images
- Abstract:
-
Regular screening for the early detection of common chronic diseases might benefit from the use of deep-learning approaches, particularly in resource-poor or remote settings. Here we show that deep-learning models can be used to identify chronic kidney disease and type 2 diabetes solely from fundus images or in combination with clinical metadata (age, sex, height, weight, body-mass index and blood pressure) with areas under the receiver operating characteristic curve of 0.85–0.93. The models ...
Expand abstract
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Access Document
- Files:
-
-
(Preview, Accepted manuscript, 2.0MB, Terms of use)
-
- Publisher copy:
- 10.1038/s41551-021-00745-6
Authors
Bibliographic Details
- Publisher:
- Springer Nature
- Journal:
- Nature Biomedical Engineering More from this journal
- Volume:
- 5
- Pages:
- 533-545
- Publication date:
- 2021-06-15
- Acceptance date:
- 2021-05-12
- DOI:
- EISSN:
-
2157-846X
Item Description
- Language:
-
English
- Keywords:
- Pubs id:
-
1182249
- Local pid:
-
pubs:1182249
- Deposit date:
-
2021-06-16
Terms of use
- Copyright holder:
- Zhang et al.
- Copyright date:
- 2021
- Rights statement:
- Copyright © 2021, The Author(s), under exclusive licence to Springer Nature Limited
- Notes:
-
This is the accepted manuscript version of the article. The final version is available from Springer Nature at https://doi.org/10.1038/s41551-021-00745-6
Metrics
If you are the owner of this record, you can report an update to it here: Report update to this record