Could AI Imaging Spot Pregnancy Health Risks Earlier?

2026-02-16 · Source: AI Magazine · Field: Health & Wellbeing — Medical Devices & Health Technology, Clinical Care & Medical Practice, Healthcare Systems & Policy · Depth: Intermediate, quick

Summary

Mount Sinai is developing and testing AI tools to identify high-risk pregnancies earlier, focusing on placenta accreta spectrum (PAS) before conception and congenital heart defects (CHD) during mid-trimester scans. A case-control study of 118,890 deliveries revealed PAS in 0.23% of cases, with AI identifying preconception anemia as a new risk factor alongside known ones. Machine learning models trained on EMR data achieved an XGBoost AUC of 0.86 for PAS prediction, outperforming logistic regression (0.76), while Random Forest showed 91% sensitivity. For CHD, AI-assisted fetal ultrasound screening using BrightHeart software increased detection to over 97%, reduced reading time by 18%, and boosted reader confidence by 19% across a multicenter study of 200 ultrasounds. These initiatives aim to enable earlier counseling, targeted surveillance, and planned deliveries.

Key takeaway

For AI Scientists developing clinical decision support systems, you should prioritize rigorous validation on diverse populations and continuous bias monitoring. Your deployment strategy must include clear clinical sponsorship with metrics tied to morbidity, cost, and workflow, ensuring a deliberate plan to scale from pilots to system-wide integration for tangible gains in accuracy and efficiency.

Key insights

AI can significantly enhance early detection of high-risk pregnancy conditions like PAS and CHD, improving care pathways.

Principles

• Preconception data can predict pregnancy risks.
• AI models offer trade-offs between sensitivity and specificity.

Method

Machine learning models, including XGBoost and Random Forest, are trained on EMR data for PAS prediction. AI-assisted software enhances fetal ultrasound screening for CHD.

In practice

• Integrate preconception anemia screening into risk assessment.
• Deploy AI-assisted ultrasound for fetal cardiac screening.

Topics

• Pregnancy Risk Prediction
• Fetal Imaging AI
• Machine Learning Models
• Placenta Accreta Spectrum
• Congenital Heart Defects

Best for: Computer Vision Engineer, AI Scientist, Research Scientist, AI Data Scientist, MLOps Engineer, AI Product Manager

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI Magazine.