Researchers use AI to improve pregnancy care, identifying unseen risk patterns

iNDICA NEWS BUREAU-

A new AI-based model, unveiled recently, has uncovered previously unrecognized combinations of risk factors associated with serious pregnancy complications, including stillbirth.

The study, led by researchers from the Universities of Utah and Brown, analyzed data from nearly 10,000 pregnancies across the United States.

The analysis examined a range of factors, from social support and blood pressure to medical history, fetal weight, and pregnancy outcomes. The findings revealed that risk levels for infants could vary up to tenfold, even for those receiving the same clinical care.

Factors such as fetal sex, the presence of pre-existing diabetes, and fetal anomalies (like heart defects) were found to play a significant role in determining risk.

The AI model identified some surprising combinations of factors that could signal higher risk, said Nathan Blue from the University of Utah’s Department of Obstetrics and Gynecology. This model could help develop more personalized risk assessments and improve pregnancy care, he added.

Notably, the study found that female fetuses might be at higher risk than males if the mother has pre-existing diabetes, which contrasts with the usual trend where female fetuses tend to be at lower risk for complications.

The researchers focused on improving risk predictions for fetuses in the bottom 10% for weight, who are often monitored closely despite being large enough to generally be healthy. Current clinical guidelines suggest intensive monitoring for all pregnancies in this category, but the new findings revealed that risk varies widely within this group.

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