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Machine-learning tool gives doctors a more detailed 3D picture of fetal health
MIT CSAIL researchers developed Fetal SMPL, a novel tool that can accurately model the shape and movements of fetuses in 3D, potentially assisting doctors in finding abnormalities and making diagnoses.
They typically produce two-dimensional black-and-white scans of fetuses that can reveal key insights, including biological sex, approximate size, and abnormalities like heart issues or cleft lip. Imagine stepping into a stranger’s footprint while blindfolded, and not only does it fit perfectly, but you correctly guess what shoe they wore — similarly, the tool closely matched the position and size of fetuses in MRI frames it hadn’t seen before. “It can be challenging to estimate the shape and pose of a fetus because they’re crammed into the tight confines of the uterus,” says lead author, MIT PhD student, and CSAIL researcher Yingcheng Liu SM ’21.
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