Why teaching AI about symmetry could make prenatal diagnosis more reliable

MIT News reports that during a routine prenatal ultrasound, the sonographer will flag possible brain abnormalities for additional magnetic resonance imaging (MRI). But imperfect imaging can result in missed or incorrect diagnoses.[1]According to MIT News, researchers at MIT, Boston Children's Hospital, and Inria have developed and trained a deep learning model called E(3)-Pose[1] that images the fetal head while it moves. MIT News says a paper about the work was presented at the 2026 European Conference on Computer Vision.[1]MIT News reports that left-right symmetry of the fetal head is embedded in the model architecture, making it easier for the model to accurately identify the brain's orientation.[1]Superpower Daily reports that the model estimates the head's position and orientation from a quick low-resolution volume, so the scanner can automatically adjust where it captures the next slice as the fetus moves[2]. The outlet adds that "the goal is more complete scans—not an AI diagnosis"[2] and that the method's "clinical promise remains unproven, particularly for younger fetuses."[2]First author Ramya Muthukrishnan told MIT News that "one of the problems with having the baby move is that the slices are all over the place."[1]Ellen Grant of Boston Children's told MIT News that the work "will automate fetal brain MRI, replacing today's slow, manual process with faster and more comfortable scans for mothers"[1].According to daily.dev, future work aims to extend the model to second-trimester fetuses, which move more and are harder to image.[3]