Could Artificial Intelligence Prevent Stillbirths?

A new AI-driven model revealed previously unknown risk factor combinations associated with severe pregnancy complications, including stillbirth (1^✔ ✔Trusted Source
AI-based analysis of fetal growth restriction in a prospective obstetric cohort quantifies compound risks for perinatal morbidity and mortality and identifies previously unrecognized high risk clinical scenarios

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AI-Powered Pregnancy Analysis Reveals 10-Fold Risk Difference

A team of US researchers including from the Universities of Utah and Brown conducted an AI-based analysis of almost 10,000 pregnancies in the country. It included information on social and physical characteristics ranging from pregnant people’s level of social support to their blood pressure, medical history, and foetal weight, as well as the outcome of each pregnancy. There may be up to a 10-fold difference in risk for infants who are currently treated identically under clinical guidelines, revealed the results, published in the journal BMC Pregnancy and Childbirth.

Foetal sex, presence or absence of pre-existing diabetes and the presence or absence of a foetal anomaly such as a heart defect could determine the risk. The AI model helped identify a “really unexpected” combination of factors that revealed higher risk, said Nathan Blue, from Utah’s Department of Obstetrics and Gynecology.

The model can progress toward making “more personalised risk assessment and pregnancy care”, he added. The findings showed that female babies could be at higher risk than males if the mother has pre-existing diabetes. Usually, female foetuses are at slightly lower risk for complications than male foetuses -- a small but well-established effect.

The researchers were especially interested in developing better risk estimates for foetuses in the bottom 10 percent for weight, but not the bottom 3 percent. These babies are small enough to be concerning, but large enough that they are usually perfectly healthy.

While current clinical guidelines advise intensive medical monitoring for all such pregnancies, the team found that within this foetal weight class, the risk of an unhealthy pregnancy outcome varied widely.

Reference:

1. AI-based analysis of fetal growth restriction in a prospective obstetric cohort quantifies compound risks for perinatal morbidity and mortality and identifies previously unrecognized high risk clinical scenarios - (https://bmcpregnancychildbirth.biomedcentral.com/articles/10.1186/s12884-024-07095-6)

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