BAOBAB stands for Biometry Automation in Obstetrics and Beyond. The World Health Organization recommends one routine ultrasound scan for all pregnant women. However, in sub-Saharan Africa, few women have access to high quality pregnancy ultrasound. The main barriers identified are the lack of affordable, robust ultrasound equipment; and a lack of trained / skilled ultrasound operators.
We aim to overcome these roadblocks in this project, undertaken in collaboration with our engineering colleagues at the Institute of Biomedical Engineering and the Bill & Melinda Gates Foundation. We investigate how recent developments in low cost ultrasound technology have meant that more affordable ultrasound equipment is now coming to the market; and how advances in real time image analysis mean that computer-assisted, partly (or eventually even fully-) automated obstetric ultrasound is becoming a promising possibility. We also aim to engage a wider field of machine learning research groups, to work on problems in this healthcare area through organising an international imaging challenge here https://a-afma.grand-challenge.org/
An important part of this project is to develop artificial intelligence networks for gestational age estimation. Knowing the gestational age is key to all management plans for an individual woman receiving maternity care; and essential for distinguishing pre-term from small for gestational age newborn babies. Many women, especially in low- income settings, first come into maternity care late in pregnancy, and gestational age (based on late measurement of the baby) is very inaccurate. The main problem with such late gestational age estimation is that this inherently underestimates gestation in small fetuses, and overestimates it in large fetuses. In order to overcome this we are employing the power of deep learning to use image characteristics (not measurements) - in other words the appearance of fetal structures - to enhance the accuracy of this gestational age estimation.
Funding: Bill & Melinda Gates Foundation