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.<\/p>\n
MRIs aren\u2019t 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\u2019s development more clearly and accurately from data \u2014 although no such algorithm has been able to model their somewhat random movements and various body shapes.<\/p>\n
That is, until a new approach called \u201cFetal SMPL\u201d from MIT\u2019s Computer Science and Artificial Intelligence Laboratory (CSAIL), Boston Children\u2019s Hospital (BCH), and Harvard Medical School presented clinicians with a more detailed picture of fetal health. It was adapted from \u201cSMPL\u201d (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 \u201ckinematic tree,\u201d which the system uses to pose and move like the fetuses it saw during training.<\/p>\n
The extensive, real-world scans that Fetal SMPL learned from helped it develop pinpoint accuracy. Imagine stepping into a stranger\u2019s footprint while blindfolded, and not only does it fit perfectly, but you correctly guess what shoe they wore \u2014 similarly, the tool closely matched the position and size of fetuses in MRI frames it hadn\u2019t seen before. Fetal SMPL was only misaligned by an average of about 3.1 millimeters, a gap smaller than a single grain of rice.<\/p>\n
The approach could enable doctors to precisely measure things like the size of a baby\u2019s head or abdomen and compare these metrics with healthy fetuses at the same age. Fetal SMPL has demonstrated its clinical potential in early tests, where it achieved accurate alignment results on a small group of real-world scans.<\/p>\n
\u201cIt can be challenging to estimate the shape and pose of a fetus because they\u2019re crammed into the tight confines of the uterus,\u201d says lead author, MIT PhD student, and CSAIL researcher Yingcheng Liu SM \u201921. \u201cOur approach overcomes this challenge using a system of interconnected bones under the surface of the 3D model, which represent the fetal body and its motions realistically. Then, it relies on a coordinate descent algorithm to make a prediction, essentially alternating between guessing pose and shape from tricky data until it finds a reliable estimate.\u201d<\/p>\n