Grant: Collaborative Health Research Projects

The placenta is a critical organ of pregnancy, responsible for the exchange of all gases andnutrients between the mother and fetus. When the development or function of the placenta_x000d_becomes compromised, common diseases of pregnancy can arise (e.g., hypertension in_x000d_pregnancy, fetal growth restriction). It is now known that these diseases, which result from adamaged 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_x000d_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_x000d_clinics are a wonderful approach to tackle CVD in women, however, they are very expensiveto run and are probably unnecessary for all women with placental disease, as ~50% of_x000d_women referred to these clinics are later deemed to be at low risk for CVD. Our previous workhas demonstrated that particular types of damage in the placenta, which can be seen under amicroscope, are strongly associated with CVD risk at follow up. Unfortunately, visual_x000d_screening by specialized pathologists is impractical as it is costly and unavailable in smallerhospitals and health care settings. The current research proposal will tackle this challenge,_x000d_using machine learning methods to enable a computer to learn how to automatically detect_x000d_these visual patterns in images of placentas from women who have high CVD risk. If_x000d_successful, the developed technology will help streamline the identification and referral of_x000d_high-priority women to cardiovascular screening and intervention programs in a cost- effectiveand timely fashion, ultimately improving the cardiovascular health of these women.