Mount Sinai specialists debut AI and machine learning models targeting prediction and screening for severe pregnancy complications using imaging and EMR data at SMFM 2026.
Key Details
- 1AI-assisted tools for diagnosis of severe congenital heart defects from fetal scans presented.
- 2Machine learning models using EMR data can preconceptionally predict placenta accreta spectrum with high sensitivity and specificity.
- 3Novel risk factors for placenta accreta, including anemia, identified by the AI models.
- 4AI is being tested for real-world impact on completion rates of fetal cardiac screening at a prenatal ultrasound center.
- 5Research highlights the strong early evidence and potential clinical value of these AI approaches in maternal-fetal medicine.
Why It Matters
These advances suggest AI and machine learning could become critical in early identification and risk stratification of pregnancy complications, potentially improving prenatal care through better screening and management. This intersection between AI, imaging, and obstetric care exemplifies the growing clinical utility of radiology/AI collaboration.
Source
EurekAlert
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