Deep learning model using continuous skin temperature data predicts labor onset

Abstract

Abstract Background Changes in body temperature anticipate labor onset in numerous mammals, yet this concept has not been explored in humans. We investigated if continuous body temperature exhibits similar changes in women and whether these changes may be linked to hormonal status. Finally, we developed a deep learning model using temperature patterning to provide a daily forecast of time to labor onset. Methods We evaluated patterns in continuous skin temperature data in 91 (n = 54 spontaneous labors) pregnant women using a wearable smart ring. In a subset of 28 pregnancies, we examined daily steroid hormone samples leading up to labor to analyze relationships among hormones and body temperature trajectory. Finally, we applied an autoencoder long short-term memory (AE-LSTM) deep learning model to provide a novel daily estimation of days until labor onset. Results Features of temperature change leading up to labor were associated with urinary hormones and labor type. Spontaneous labors exhibited greater estriol to α-pregnanediol ratio, as well as lower body temperature and more stable circadian rhythms compared to pregnancies that did not undergo spontaneous labor. Skin temperature data from 54 pregnancies that underwent spontaneous labor between 34 and 42 weeks of gestation were included in training the AE-LSTM model, and an additional 37 pregnancies that underwent artificial induction of labor or Cesarean without labor were used for further testing. The input to the pipeline was 5-min skin temperature data from a gestational age of 240 days until the day of labor onset. During cross-validation AE-LSTM average error (true – predicted) dropped below 2 days at 8 days before labor, independent of gestational age. Labor onset windows were calculated from the AE-LSTM output using a probabilistic distribution of model error. For these windows AE-LSTM correctly predicted labor start for 79% of the spontaneous labors within a 4.6-day window at 7 days before true labor, and 7.4-day window at 10 days before true labor. Conclusion Continuous skin temperature reflects progression toward labor and hormonal change during pregnancy. Deep learning using continuous temperature may provide clinically valuable tools for pregnancy care.

Keywords

• Pregnancy
• Signal processing
• Machine learning
• ML
• AI
• Parturition