{"version":1,"type":"story","url":"https://digestai.news/story/why-teaching-ai-about-symmetry-could-make-prenatal-diagnosis-more-reli","json":"https://digestai.news/story/why-teaching-ai-about-symmetry-could-make-prenatal-diagnosis-more-reli.json","markdown":"https://digestai.news/story/why-teaching-ai-about-symmetry-could-make-prenatal-diagnosis-more-reli.md","slug":"why-teaching-ai-about-symmetry-could-make-prenatal-diagnosis-more-reli","headline":"Why teaching AI about symmetry could make prenatal diagnosis more reliable","summary":"Researchers at MIT, Boston Children’s Hospital and Inria introduced a deep‑learning model called E(3)-Pose that improves fetal brain MRI by tracking head motion and producing high‑resolution 3D images. The model embeds left‑right symmetry of the fetal head into its architecture, allowing it to align 2D slices taken while the fetus moves and reduce gaps in the final scan.  \n\nFirst author Ramya Muthukrishnan, an MIT PhD student in Polina Golland’s lab, built the system with help from radiology assistant professor Borjan Gagoski and research assistant Aryn Lee, who acquired the MRI data.  The work was presented at the 2026 European Conference on Computer Vision.  The team says E(3)-Pose could speed up scans, make them more comfortable for pregnant mothers, and provide radiologists with clearer images for early detection of brain abnormalities.  Future plans include adapting the model for second‑trimester fetuses, which move more and are currently harder to image.","keyPoints":["E(3)-Pose embeds left‑right symmetry to align fetal MRI slices despite motion","Model developed by MIT, Boston Children’s Hospital and Inria; presented at ECCV 2026","Goal: faster, higher‑quality fetal brain scans to aid early diagnosis"],"whyItMatters":"Accurate, motion‑robust fetal MRI could enable earlier detection of brain disorders, improving treatment options and reducing scan time for mothers.","category":{"slug":"research","name":"Research","url":"https://digestai.news/category/research"},"entities":{"companies":["MIT","Boston Children’s Hospital","Inria"],"models":["E(3)-Pose"],"people":["Ramya Muthukrishnan","Polina Golland","Borjan Gagoski","Ellen Grant","Elfar Adalsteinsson","Benjamin Billot"]},"firstPublishedAt":"2026-10-19T18:20:00Z","updatedAt":"2026-10-19T18:20:00Z","sourceCount":1,"hasPrimarySource":true,"sources":[{"outlet":"MIT News on AI","title":"Why teaching AI about symmetry could make prenatal diagnosis more reliable","url":"https://news.mit.edu/2026/teaching-ai-about-symmetry-could-make-prenatal-diagnosis-more-reliable-1019","publishedAt":"2026-10-19T18:20:00Z","type":"primary","primary":true,"lead":true}],"sourceNotes":null,"discussions":[],"thread":null,"cite":{"text":"Digest AI, \"Why teaching AI about symmetry could make prenatal diagnosis more reliable\", 19 October 2026, https://digestai.news/story/why-teaching-ai-about-symmetry-could-make-prenatal-diagnosis-more-reli","publisher":"Digest AI","title":"Why teaching AI about symmetry could make prenatal diagnosis more reliable","datePublished":"2026-10-19T18:20:00Z","url":"https://digestai.news/story/why-teaching-ai-about-symmetry-could-make-prenatal-diagnosis-more-reli"},"generatedBy":"Written by Digest AI's editorial model from the linked sources; the sources are the record.","license":"Headlines, digests and key points are written by Digest AI and may be quoted with a link to the story page. Linked articles belong to their publishers. Terms: https://digestai.news/terms#reuse"}