Abstract
Traditionally, medical discoveries are made by observing associations, making hypotheses from them and then designing and running experiments to test the hypotheses. However, with medical images, observing and quantifying associations can often be difficult because of the wide variety of features, patterns, colours, values and shapes that are present in real data. Here, we show that deep learning can extract new knowledge from retinal fundus images. Using deep-learning models trained on data from 284,335 patients and validated on two independent datasets of 12,026 and 999 patients, we predicted cardiovascular risk factors not previously thought to be present or quantifiable in retinal images, such as age (mean absolute error within 3.26 years), gender (area under the receiver operating characteristic curve (AUC) = 0.97), smoking status (AUC = 0.71), systolic blood pressure (mean absolute error within 11.23 mmHg) and major adverse cardiac events (AUC = 0.70). We also show that the trained deep-learning models used anatomical features, such as the optic disc or blood vessels, to generate each prediction.
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Acknowledgements
We thank C. Angermueller, A. Narayanaswamy, A. Rajkomar, A. Taly and P. Nelson from Google Research for their advice and assistance with reviewing the manuscript, and J. Cuadros for access to and clarifications on the EyePACS dataset and for thoughtful discussions. We used the UK Biobank Resource under application number 17643 for this work.
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R.P., A.V.V., Y.L., G.S.C., L.P. and D.R.W. designed the research; R.P., A.V.V., K.B., Y.L. and L.P. acquired data and/or executed the research; R.P., A.V.V., K.B., Y.L., M.V.M., L.P. and D.R.W. analysed and/or interpreted the data; R.P., A.V.V., K.B., Y.L., M.V.M., G.S.C., L.P. and D.R.W. prepared the manuscript.
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Poplin, R., Varadarajan, A.V., Blumer, K. et al. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat Biomed Eng 2, 158–164 (2018). https://doi.org/10.1038/s41551-018-0195-0
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DOI: https://doi.org/10.1038/s41551-018-0195-0
