New York, May 31 (IANS) Researchers have developed a new model using deep learning that can predict preterm births as early as 31 weeks of pregnancy.
Preterm birth, which occurs when a baby is born before 37 weeks of gestation, affects nearly 10 per cent of pregnancies worldwide, and rates are on the rise.
The team at the Washington University in St. Louis developed the model by analysing electrical activity during pregnancy.
“Our method predicts preterm births using electrohysterogram measurements and clinical information acquired around the 31st week of gestation with a performance comparable to the clinical standards used to detect imminent labour in women with symptoms of preterm labour,” said Arye Nehorai, Professor of Electrical Engineering at Preston M. Green Department of Electrical & Systems Engineering, .
To design their method, the team used measurements from electrohysterograms (EHG) — a noninvasive technique that detects uterine electrical activity through electrodes placed on the abdomen, as well as clinical information from two public databases, such as age, gestational age, weight, and bleeding in the first or second trimester.
They trained a deep learning model on data from 30-minute EHGs performed on a total of 159 pregnant women who were at least 26 weeks’ gestation.
Some recordings were obtained during regular check-ups while others were recorded from mothers who were hospitalised with symptoms of preterm labour. Of all the women, nearly 19 per cent delivered preterm, according to the results of the research published in the journal PLoS One.
In their research, the team found that various components of the EHG measurements contributed to their model’s predictions. Higher frequency components of the EHG measurements were more predictive of preterm births.
They also found that their 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.
“Preterm birth is an abnormal physiological condition, not just a pregnancy that happened to end early,” Nehorai said.
“Therefore, we can expect that physiological measurements, such as EHG recordings, may show a stronger dichotomy between pregnancies that end with either preterm or term deliveries than is shown in continuous characteristics correlated with gestational age at delivery.”
Going forward, the researchers plan to develop a device to record EHG measurements and to collect data from a larger cohort of pregnant women to improve their method and validate results.