Grant: Collaborative Health Research Projects (NSERC partnered)

The placenta is a critical organ of pregnancy, responsible for the exchange of all gases and nutrients between the mother and fetus. When the development or function of the placenta becomes compromised, common diseases of pregnancy can arise (e.g., hypertension in pregnancy, fetal growth restriction). It is now known that these diseases, which result from a damaged placenta, are significant risk factors for the development of cardiovascular disease (CVD) later in life. CVD is a leading cause of death for women. As such, several hospitals now run specialized follow up clinics for women following a pregnancy with placental disease, where their cardiovascular health is screened and prevention programs are offered. These clinics are a wonderful approach to tackle CVD in women, however, they are very expensive to run and are probably unnecessary for all women with placental disease, as ~50% of women referred to these clinics are later deemed to be at low risk for CVD. Our previous work has demonstrated that particular types of damage in the placenta, which can be seen under a microscope, are strongly associated with CVD risk at follow up. Unfortunately, visual screening by specialized pathologists is impractical as it is costly and unavailable in smaller hospitals and health care settings. The current research proposal will tackle this challenge, using machine learning methods to enable a computer to learn how to automatically detect these visual patterns in images of placentas from women who have high CVD risk. If successful, the developed technology will help streamline the identification and referral of high-priority women to cardiovascular screening and intervention programs in a cost- effective and timely fashion, ultimately improving the cardiovascular health of these women.