| Title: | Detection of anaemia from retinal fundus images via deep learning |
| Journal: | Nature Biomedical Engineering |
| Published: | 23 Dec 2019 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/31873211/ |
| DOI: | https://doi.org/10.1038/s41551-019-0487-z |
| URL: | https://arxiv.org/pdf/1904.06435 |
| Citations: | 160 (62 in last 2 years) as of 8 Aug 2024 |
Publication 3657
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Abstract
Owing to the invasiveness of diagnostic tests for anaemia and the costs</p>
associated with screening for it, the condition is often undetected. Here, we show</p>
that anaemia can be detected via machine-learning algorithms trained using retinal</p>
fundus images, study participant metadata (including race or ethnicity, age, sex and</p>
blood pressure) or the combination of both data types (images and study participant</p>
metadata). In a validation dataset of 11,388 study participants from the UK Biobank,</p>
the metadata-only, fundus-image-only and combined models predicted haemoglobin</p>
concentration (in g dl-1) with mean absolute error values</p>
of 0.73 (95% confidence interval: 0.72-0.74), 0.67 (0.66-0.68) and 0.63 (0.62-0.64),</p>
respectively, and with areas under the receiver operating characteristic curve (AUC)</p>
values of 0.74 (0.71-0.76), 0.87 (0.85-0.89) and 0.88 (0.86-0.89), respectively. For</p>
539 study participants with self-reported diabetes, the combined model predicted</p>
haemoglobin concentration with a mean absolute error of 0.73 (0.68-0.78) and anaemia</p>
an AUC of 0.89 (0.85-0.93). Automated anaemia screening on the basis of fundus</p>
images could particularly aid patients with diabetes undergoing regular retinal</p>
imaging and for whom anaemia can increase morbidity and mortality risks.</p>
9 Authors
- Akinori Mitani
- Abigail Huang
- Subhashini Venugopalan
- Greg S. Corrado
- Lily Peng
- Dale R. Webster
- Naama Hammel
- Yun Liu
- Avinash V. Varadarajan
1 Application
| Application ID | Title |
|---|---|
| 17643 | Automated Detection of Ocular & Systemic Disease Via Retinal Fundus Imaging |
Enabling scientific discoveries that improve human health