Detecting Anemia from Retinal Fundus Images

TL;DR

This study demonstrates that deep learning algorithms can accurately detect anemia and measure hemoglobin levels from retinal fundus images, offering a promising non-invasive screening method especially useful for diabetic patients.

Contribution

Introduces a deep learning approach that accurately detects anemia and quantifies hemoglobin from retinal images, outperforming existing non-invasive methods.

Findings

Mean absolute error of 0.63 g/dL in hemoglobin measurement

AUC of 0.88 for anemia detection

Effective especially in diabetic patients

Abstract

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.