Successful labor outcomes in expectant mothers using AI



Researchers at the Mayo Clinic have discovered that analyzing patterns of changes in laboring women using artificial intelligence (AI) algorithms can assist predict if a vaginal delivery will be successful and result in a healthy mother and child. The research results were released in PLOS ONE.

Abimbola Famuyide, M.D., a Mayo Clinic OB-GYN and senior author of the study, says, "This is the first step to applying algorithms in providing effective guidance to physicians and midwives as they make key decisions during the birthing process." "We anticipate that the algorithm will operate in real time, updating the risk of a negative outcome with each new piece of information entered during an expectant woman's labor once it has been verified by additional research. This could lower the number of cesarean deliveries as well as complications for both mothers and newborns."

Women who are in labor are aware of the value of routine cervical exams to monitor the progression of labor. This crucial phase enables obstetricians to forecast the possibility of a vaginal birth within a given time frame. The issue is that cervical dilatation during labor varies from person to person, and a number of significant factors can affect how labor progresses.

The multicenter Consortium on Safe Labor database from the Eunice Kennedy Shriver National Institute of Child Health and Human Development was used by researchers to build the prediction model in the study. More than 700 clinical and obstetric variables were assessed in 66,586 deliveries starting at the time of admission and continuing during labor.

The risk-prediction model was built using information that was available at the time of labor admission, such as baseline patient characteristics, the patient's most recent clinical evaluation, and cumulative labor progress since admission. According to the researchers, using the baseline and labor features of each patient, the models may offer an alternative to traditional labor charts and encourage the individualization of healthcare decisions.

According to Dr. Famuyide, "it is very customized to the person in labor." He continues by saying that this will be an effective tool for midwives and doctors working remotely since it will provide patients time to be transferred from rural or remote settings to the proper level of care.

According to Bijan Borah, Ph.D., the Robert D. and Patricia E. Kern Scientific Director for Health Services and Outcomes Research, "the AI algorithm's ability to predict individualized risks during the labor process will not only help reduce adverse birth outcomes but it can also reduce healthcare costs associated with maternal morbidity in the U.S., which have been estimated to be over $30 billion.

To evaluate the results of these models following their implementation in labor units, validation studies are still being conducted.

Researchers from the Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery collaborated on this study. There are no disclosed or actual conflicts of interest for the writers.

Mayo Clinic

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