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.
Automated Detection of Ocular & Systemic Disease Via Retinal Fundus Imaging
Retinal imaging in the form of fundus photography and OCT are well-established diagnostic tools for eye diseases such as diabetic retinopathy, glaucoma and age-related macular degeneration. It has also been suggested as a prognostic tool for the severity of systemic disease such as diabetes, stroke, and dementia. Our work centers around using machine learning and computer vision to (1) automate the detection of eye diseases which are currently diagnosed via retinal imaging and (2) identify novel features in retinal imaging that may be predictors or early signs of eye disease as well as systemic disease.
If successful, this work will help improve the detection of eye diseases and potentially other systemic diseases. Automated detection also has the potential of increasing efficiency and reducing costs. Using labeled fundus and OCT images as the main inputs, we will train computer algorithms to automatically predict image labels using machine learning and computer vision. We are requesting data from all patients that have had the retinal imaging performed. Per the UK Biobank look-up tool, this consists of 67,711 patients that make up the collection of 68,151 paired colour retinal photographs and optical coherence tomography (OCT) scans.
|Lead investigator:||Mr Philip Nelson|
|Lead institution:||Google LLC|
3 related Returns
|Return ID||App ID||Description||Archive Date|
|3272||17643||Deep Learning and Glaucoma Specialists: The Relative Importance of Optic Disc Features to Predict Glaucoma Referral in Fundus Photographs||1 Apr 2021|
|3656||17643||Detection of anaemia from retinal fundus images via deep learning||19 Jul 2021|
|3657||17643||Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning||19 Jul 2021|
|3656||Deep Learning for Predicting Refractive Error From Retinal Fundus Images||Varadarajan et al.,||2018||Investigative Ophthalmology and Visual Science (2018)|