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).
The prevalence of congenital anomalies is 255/10,000 births in Europe, according to Eurocat for 2012-2016. In the same period, there were up to 5.11M to 5.23M births per year in Europe. Thus, this would represent around 130K cases of congenital anomalies and around 50K cases of ectopic pregnancy. Orphanet counts >1.2K different congenital disorders with 13K phenotypes.
The SUOG assistant 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 (what is relevant to look for, based on the previous normal and abnormal findings, and diagnosis hypothesis) 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.
As a result, at the very first scan, non-expert operators collect high quality image sets, close to what expert operators would do. Consequently, they can request second expert opinion based on these images and, if necessary, refer women to adequate expert centers.
This project is funded for 3 years by the EIT-Health Innovation program, selected as part of the bp2020.
EIT-Health innovation program is a major accelerator and game changer for the SUOG project: EIT partners network is gathering all the actors to ensure a world leader industry-level technology development for the tool with GE Healthcare, a business guidance and IP management with SATT Lutech, and a high-level expertise in Medicine (APHP, UCLH, …), in Academics and in Research (Sorbonne University and INSERM). This setting is essential to our ambitious AI-based solution for medical imaging. Moreover, the significant funding allows to encompass real-life implementation with adequate rise in the Techology Readiness Level of SUOG, including next-generation nested IA methods. Finally, it offers a unique opportunity to design international validation studies.
At the end of the project, in 2022, the fully functional and clinically validated SUOG assistant will be developed.
My main skills field are Neurosciences and behavioral sciences which were studied at Caen Normandy University. I was graduated with honor from Montpellier University where I studied health device engineering. I wishes to put my skills at the service of the development of new technologies applied to the field of health, to ensure better care for patients and diseases.
To this end, I integrated SUOG team as project manager and master contact with Ferdinand Dhombres. I am in charge of the project coordination and follow-up between the different partners in order to guide the SUOG project in the best conditions.