Now, researchers from Washington University in St. Louis, Missouri, are looking to improve those odds through the use of artificial intelligence.
They developed a deep learning model that can predict preterm births by analyzing electrical activity in the woman’s uterus during pregnancy — then they tested the model in a study that was published in the medical journal PLOS One.
Researchers from Washington University in St. Louis, Missouri, have developed a deep learning model that can predict preterm births using electrical activity.During the study, the researchers performed electrohysterograms , which use electrodes on the abdomen to record electrical activity in the uterus.
“The advantage of our approach is that is inexpensive to build,” said Nehorai of the new research. “Our model was effective in prediction with shorter EHG recordings, which could make the model easier to use, more cost-effective in a clinical setting and possibly usable in a home setting.” “There are some EHG measuring devices already in the market — however, it has been challenging to predict preterm births from the EHG data,” said Uri Goldsztejn, a PhD candidate in the department of biomedical engineering working under the supervision of Professor Nehorai at Washington University.
Dr. Suzy Lipinski, a board-certified OB/GYN at Pediatrix Medical Group in Denver, Colorado, was not involved in the study but shared her input on whether the deep learning technology could help solve the problem of preterm birth in the U.S. If EHG becomes the way to evaluate for preterm birth, then hospitals, birth centers and offices would all have to purchase new equipment, which could delay adoption in low-resource areas such as rural and inner cities, Lipinski said.
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