Evidence map›Paper›PMID 39587525›Full record

ArticleBMC pregnancy and childbirth2024

Deep learning model using continuous skin temperature data predicts labor onset.

Chinmai Basavaraj, Azure D Grant, Shravan G Aras, Elise N Erickson

Abstract read
In one paragraph

Article in BMC pregnancy and childbirth, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Smart Ring in Clinical Medicine: A Systematic Review.Biomimetics (Basel, Switzerland) · 2025
    Review
  2. Observational
  3. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Chinmai Basavaraj *Department of Computer Science, The University of Arizona, Tucson, AZ, USA.
Azure D Grant *People Science, Inc., Los Angeles, CA, USA.
Shravan G Aras *Center for Biomedical Informatics and Biostatistics, The University of Arizona, Tucson, AZ, USA.
Elise N Erickson *College of Nursing, The University of Arizona, Tucson, AZ, USA. eliseerickson@arizona.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundChanges 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.

methodsWe 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.

resultsFeatures 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.

conclusionContinuous skin temperature reflects progression toward labor and hormonal change during pregnancy. Deep learning using continuous temperature may provide clinically valuable tools for pregnancy care.

Indexed as

Deep LearningLabor OnsetSkin TemperatureAdultFemaleHumansLabor, ObstetricPregnancyWearable Electronic DevicesAIBiological rhythmsEstrogenMachine learningMaternityMLParturitionPregnancyProgesteroneSignal processingThermoregulation

Identifiers

PMID39587525
PMCPMC11587739

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.