Smart Ultrasound in Obstetrics and Gynecology - SUOG

Ultrasound screening in obstetrics and gynecology is a complex task, with a major cognitive load and with significant medical impact both in the first trimester (emergency care, consequences on fertility) and later in pregnancy (fetal disorder detection, placental disorder diagnosis). With over 5M births per year in Europe, 130K cases of congenital anomalies and around 50K cases of ectopic pregnancy, the need for efficient support of the ultrasound screening has become critical. After a routine screening scan, when an unusual feature is identified (fetal disorder or growth anomaly) ultrasound operators must request an expert scan to establish a prognosis and to organize adequate prenatal care and anticipate the postnatal care as well. The need for experts for this second scan is an issue in many regions in Europe. The ultrasound screening in obstetrics and gynecology problem is twofold: 1) to collect all relevant images during the scan 2) to analyze these images to achieve a diagnosis The SUOG assistant develops the next-generation of intelligent assistant for ultrasound support during pregnancy and provides the ultrasound operator, in real-time and during the scan, with relevant information when he faces unusual features. The SUOG assistant provides iterative intelligent guidance and display relevant expert-validated ultrasound images to enforce the provided guidance. The SUOG assistant is the first intelligent assistant of its kind. It is unique by design: it relies on a composite AI (semantic reasoning and image recognition) and its decision processes remain human-readable (no black-box effect, thanks to the use of ontologies). SUOG integrates a knowledge base designed by international OB/GYN and fetal medicine experts. At the end of the project, in 2022, the fully functional and clinically validated SUOG assistant will be developed. This project is funded for 3 years by the EIT-Health Innovation program, selected as part of the bp2020.

• Employees10
• Founded2020