Fetal Anomalies

Obstetric ultrasound represents the standard of care for the screening of the fetal anomalies. However, its performance is dependent upon several parameters including type of anomaly, gestational age, maternal habitus and skills of the examiner. The use of Artificial Intelligence (AI) in medical diagnostics has been suggested not only to reduce the inter- and intra-operator variability, but also to compress the required time necessary to perform routine tasks, hence optimizing healthcare resources. Fetal brain abnormalities are among the most challenging fetal congenital anomalies in terms of ultrasound diagnosis, prenatal counseling and management. The access to new sources of technology, i.e. AI, has the potential to improve recognition, detection and localization of brain malformations. Therefore, we propose to develop an AI-based software, which would be capable to recognize the brain structures at antenatal ultrasound and discriminate between normal and abnormal fetal brain anatomy through fully automatic data processing.

The Biobank includes data and biological specimens of women from three original studies: 1) First-trimester Prediction of Preeclampsia (PREDICTION Study, NCT02189148), 2) Pre-Eclampsia And growth Retardation, an evaluative Longitudinal study (PEARL Study, NCT02379832), 3) Effect of Low Dose Aspirin on Birthweight in Twins: The GAP Trial (NCT02280031) and 4)PREDICTION2: Prediction of Preeclampsia and other Pregnancy Complications Following Combined Iterative Screening.