Detecting hidden signs of anemia from the eye

In our latest work, "Detection of anemia from retinal fundus images via deep learning" published in "Nature Biomedical Engineering" we find that a deep learning model can quantify hemoglobin using de-identified photographs of the back of the eye and common metadata (e.g. Compared to just using metadata, deep learning improved the detection of anemia (as measured using the AUC), from 74 percent to 88 percent. To ensure these promising findings were not the result of chance or false correlations, other scientists helped to validate the model--which was initially developed on a dataset of primarily Caucasian ancestry--on a separate dataset from Asia. The performance of the model was similar on both datasets, suggesting the model could be useful in a variety of settings.