Researchers are improving the prediction of preterm birth by studying electrical activity during pregnancy. A deep learning model developed by the team can predict preterm births as early as 31 weeks of gestation using electrohysterogram measurements and clinical data.
Preterm birth affects 10% of pregnancies worldwide, and its incidence is rising. Researchers at Washington University in St. Louis use deep learning to predict preterm births as early as 31 weeks of pregnancy. Their method analyzes electrohysterogram measurements and clinical data acquired around the 31st week of gestation, achieving performance comparable to clinical standards. By training a deep recurrent neural network on data from 159 pregnant women, the team outperforms other methods. Their findings pave the way for improved understanding and prediction of preterm birth.
Original article NeuroScienceNews
