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Detection of anaemia from retinal fundus images via deep learning

An Author Correction to this article was published on 12 February 2020

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Owing to the invasiveness of diagnostic tests for anaemia and the costs associated with screening for it, the condition is often undetected. Here, we show that anaemia can be detected via machine-learning algorithms trained using retinal fundus images, study participant metadata (including race or ethnicity, age, sex and blood pressure) or the combination of both data types (images and study participant metadata). In a validation dataset of 11,388 study participants from the UK Biobank, the metadata-only, fundus-image-only and combined models predicted haemoglobin concentration (in g dl–1) with mean absolute error values of 0.73 (95% confidence interval: 0.72–0.74), 0.67 (0.66–0.68) and 0.63 (0.62–0.64), respectively, and with areas under the receiver operating characteristic curve (AUC) values of 0.74 (0.71–0.76), 0.87 (0.85–0.89) and 0.88 (0.86–0.89), respectively. For 539 study participants with self-reported diabetes, the combined model predicted haemoglobin concentration with a mean absolute error of 0.73 (0.68–0.78) and anaemia an AUC of 0.89 (0.85–0.93). Automated anaemia screening on the basis of fundus images could particularly aid patients with diabetes undergoing regular retinal imaging and for whom anaemia can increase morbidity and mortality risks.

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Fig. 1: Bland–Altman plot for predicted and measured Hb.
Fig. 2: Prediction of anaemia classifications.
Fig. 3: Effects of masking parts of the image on the prediction of anaemia and moderate anaemia.
Fig. 4: Effects of removing high-frequency information using Gaussian blur on the prediction of anaemia and moderate anaemia.
Fig. 5: Examples applying different explanation techniques to generate saliency maps highlighting the regions the model focuses on when predicting anaemia.

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Data availability

The data supporting the findings of this study are available, with restrictions, from the UK Biobank24.

Code availability

The machine-learning models were developed by using standard model libraries and scripts in TensorFlow56. Custom code was specific to our computing infrastructure and mainly used for data input/output and parallelization across computers.

Change history


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This research was conducted using the UK Biobank Resource under application number 17643. The work of A.H. was done via Advanced Clinical, San Francisco, USA. The authors thank C. Angermueller from Google Research for his engineering contributions and A. Zaidi, A. Narayanaswamy, C. Chen, J. Krause and R. Sayres from Google Research for their advice and assistance with reviewing the manuscript.

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Authors and Affiliations



A.M., G.S.C., L.P., D.R.W., N.H. and A.V.V. designed the research. A.M., L.P. and A.V.V. acquired data from the UK Biobank. A.M. executed the research and analysed the data. S.V. conducted the model explanation analysis. A.M., Y.L. and A.V.V. interpreted the results. A.M., A.H., Y.L. and N.H. prepared the manuscript. All authors contributed to manuscript revision and approved the submitted version.

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Correspondence to Akinori Mitani.

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The authors are employees of Google and own Alphabet stock or are working at Google. A.M., A.V.V., L.P. and D.R.W. are inventors on a patent applied by Google related to this work (current status: pending).

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Mitani, A., Huang, A., Venugopalan, S. et al. Detection of anaemia from retinal fundus images via deep learning. Nat Biomed Eng 4, 18–27 (2020).

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