Womb to World: Linking Prenatal Variability to Postnatal Outcomes using AI

Longitudinal monitoring of physiological characteristics and reproductive health can impact lifelong health, including menopause symptoms and timing, cardiovascular health and risk factors for cancer and other conditions.

Growing evidence supports the enduring role of fetal programming on predicting later child health. Specifically, just as monitoring during pregnancy can enable earlier identification of perinatal and newborn health, so too can heightened monitoring of reproductive health predict later risk for a range of health conditions.

Harmonizing reports of perinatal characteristics into the electronic health records of the fetus (growth measures, motor activity, and placental blood flow) and mother (hormonal, epigenetic and inflammatory biomarkers, endometrial features, nutrition/BMI) could lead to further identification of perinatal continuity and change.

This workshop unites researchers applying AI and machine learning techniques to biomedical imaging and genetics datasets, with clinicians and researchers focused on preeclampsia, gestational diabetes, assisted reproduction and newborn health.

If you have any questions about registration or about the workshop in general, please contact Christina Rozeik (coordinator@repro.cam.ac.uk).

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