Machine-learning tool gives doctors a more detailed 3D picture of fetal health

MIT CSAIL researchers developed a tool that can model the shape and movements of fetuses in 3D, potentially assisting doctors in finding abnormalities and making diagnoses.

Originally published by Alex Shipps | MIT CSAIL in MT News, on September 15, 2025

Fetal SMPL was trained on 20,000 MRI volumes to predict the location and size of a fetus and create sculpture-like 3D representations. The approach could enable doctors to precisely measure things like the size of a baby’s head and compare these metrics with healthy fetuses at the same age.

Credits: Image: Alex Shipps and Yingcheng Liu/MIT CSAIL 

 

For pregnant women, ultrasounds are an informative (and sometimes necessary) procedure. 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. If your doctor wants a closer look, they may use magnetic resonance imaging (MRI), which uses magnetic fields to capture images that can be combined to create a 3D view of the fetus.

MRIs aren’t a catch-all, though; the 3D scans are difficult for doctors to interpret well enough to diagnose problems because our visual system is not accustomed to processing 3D volumetric scans (in other words, a wrap-around look that also shows us the inner structures of a subject). Enter machine learning, which could help model a fetus’s development more clearly and accurately from data — although no such algorithm has been able to model their somewhat random movements and various body shapes.

That is, until a new approach called “Fetal SMPL” from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), Boston Children’s Hospital (BCH), and Harvard Medical School presented clinicians with a more detailed picture of fetal health. It was adapted from “SMPL” (Skinned Multi-Person Linear model), a 3D model developed in computer graphics to capture adult body shapes and poses, as a way to represent fetal body shapes and poses accurately. Fetal SMPL was then trained on 20,000 MRI volumes to predict the location and size of a fetus and create sculpture-like 3D representations. Inside each model is a skeleton with 23 articulated joints called a “kinematic tree,” which the system uses to pose and move like the fetuses it saw during training.

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