Basavaraj Chinmai, Grant Azure D, Aras Shravan G, Erickson Elise N
Department of Computer Science, The University of Arizona, Tucson, AZ, USA.
People Science, Inc. LA, CA, USA.
medRxiv. 2024 Feb 27:2024.02.25.24303344. doi: 10.1101/2024.02.25.24303344.
Changes in body temperature anticipate labor onset in numerous mammals, yet this concept has not been explored in humans.
We evaluated patterns in continuous skin temperature data in 91 pregnant women using a wearable smart ring. Additionally, we collected daily steroid hormone samples leading up to labor in a subset of 28 pregnancies and analyzed relationships among hormones and body temperature trajectory. Finally, we developed a novel autoencoder long-short-term-memory (AE-LSTM) deep learning model to provide a 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 40 pregnancies that underwent artificial induction of labor or Cesarean without labor were used for further testing. The model was trained only on aggregate 5-minute skin temperature data starting at a gestational age of 240 until 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 status during pregnancy. Deep learning using continuous temperature may provide clinically valuable tools for pregnancy care.
体温变化预示着许多哺乳动物即将分娩,但这一概念尚未在人类中得到探索。
我们使用可穿戴智能指环评估了91名孕妇连续的皮肤温度数据模式。此外,我们在28例妊娠的一个子集中收集了临产前每日的类固醇激素样本,并分析了激素与体温轨迹之间的关系。最后,我们开发了一种新型的自动编码器长短期记忆(AE-LSTM)深度学习模型,以提供距分娩发作天数的每日估计。
临产前体温变化特征与尿激素及分娩类型相关。与未自然分娩的妊娠相比,自然分娩的雌三醇与α-孕二醇比值更高,体温更低,昼夜节律更稳定。来自54例在妊娠34至42周之间自然分娩的孕妇的皮肤温度数据被纳入AE-LSTM模型的训练,另外40例接受人工引产或剖宫产未临产的孕妇用于进一步测试。该模型仅在妊娠240周龄开始直至分娩发作的总计5分钟皮肤温度数据上进行训练。在交叉验证期间,AE-LSTM平均误差(实际值-预测值)在分娩前8天降至2天以下,与孕周无关。根据AE-LSTM输出,使用模型误差的概率分布计算分娩发作窗口。对于这些窗口,AE-LSTM在实际分娩前7天的4.6天窗口内以及实际分娩前10天的7.4天窗口内,正确预测了79%自然分娩的开始时间。
连续的皮肤温度反映了孕期向分娩的进展和激素状态。使用连续温度的深度学习可为孕期护理提供具有临床价值的工具。