Department of Computer Science, The University of Arizona, Tucson, AZ, USA.
People Science, Inc., Los Angeles, CA, USA.
BMC Pregnancy Childbirth. 2024 Nov 25;24(1):777. doi: 10.1186/s12884-024-06862-9.
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.
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.
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.
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.
许多哺乳动物的体温变化预示着分娩的开始，但这一概念尚未在人类中得到探索。我们研究了女性的连续体温是否表现出类似的变化，以及这些变化是否与激素状态有关。最后，我们使用温度模式开发了一种深度学习模型，以提供分娩开始时间的每日预测。
我们使用可穿戴智能戒指评估了 91 名（n=54 例自然分娩）孕妇连续皮肤温度数据的模式。在 28 例妊娠的一个亚组中，我们检查了分娩前每日类固醇激素样本，以分析激素和体温轨迹之间的关系。最后，我们应用了一种自动编码器长短期记忆（AE-LSTM）深度学习模型，以提供一种新的分娩开始时间的每日估计。
分娩前体温变化的特征与尿激素和分娩类型有关。与未经历自然分娩的妊娠相比，自发性分娩表现出更高的雌三醇与α-孕二烯二醇比值，以及更低的体温和更稳定的昼夜节律。纳入训练 AE-LSTM 模型的是 54 例在 34 至 42 孕周之间经历自发性分娩的妊娠的皮肤温度数据，另外 37 例经历人工引产或无分娩剖宫产的妊娠用于进一步测试。该管道的输入是从妊娠 240 天到分娩当天的 5 分钟皮肤温度数据。在交叉验证中，AE-LSTM 的平均误差（真实值-预测值）在分娩前 8 天低于 2 天，与胎龄无关。使用模型误差的概率分布从 AE-LSTM 输出中计算分娩开始窗口。对于这些窗口，AE-LSTM 在分娩前 7 天的 4.6 天窗口内正确预测了 79%的自发性分娩开始，在分娩前 10 天的 7.4 天窗口内正确预测了 79%的自发性分娩开始。
连续皮肤温度反映了妊娠期间分娩的进展和激素变化。使用连续温度的深度学习可能为妊娠护理提供有临床价值的工具。
