  Despite its high prevalence, anemia is often undetected due to the
invasiveness and cost of screening and diagnostic tests. Though some
non-invasive approaches have been developed, they are less accurate than
invasive methods, resulting in an unmet need for more accurate non-invasive
methods. Here, we show that deep learning-based algorithms can detect anemia
and quantify several related blood measurements using retinal fundus images
both in isolation and in combination with basic metadata such as patient
demographics. On a validation dataset of 11,388 patients from the UK Biobank,
our algorithms achieved a mean absolute error of 0.63 g/dL (95% confidence
interval (CI) 0.62-0.64) in quantifying hemoglobin concentration and an area
under receiver operating characteristic curve (AUC) of 0.88 (95% CI 0.86-0.89)
in detecting anemia. This work shows the potential of automated non-invasive
anemia screening based on fundus images, particularly in diabetic patients, who
may have regular retinal imaging and are at increased risk of further morbidity
and mortality from anemia.
