"Digital phenotyping of placenta histology". (Dr Christoffer Nellåker, NDWRH).

Digital phenotyping is an approach to use data collected from patients with new machine learning approaches extract biological information. I am working to explore how analysis of placenta histology data can predict adverse health outcomes for infants and detect developmental indicators of increased risks earlier than currently possible. We show that deep learning approaches can delineate placental cell phenotypes from histology images. The aim is to extract phenotype signatures from placental histology to capture larger scale tissue and organ level information.
Our vision is to use non-invasive data sources to help diagnose developmental impairments and predict health outcomes in early life.