用AI从胎心监护预测胎儿生物年龄，判断未来妊娠风险。
Artificial Intelligence-derived Cardiotocography Age as a Digital Biomarker for Predicting Future Adverse Pregnancy Outcomes
- 用1D CNN+增强回归训练模型，从胎心图推算胎儿生物年龄。
- 年龄差超过21天的孕产妇，早产和妊娠糖尿病风险显著升高。
- 无需额外检查，适合资源有限地区的孕期风险筛查。
胎心监护（CTG）是一种低成本、无创的胎儿健康评估技术，全球广泛使用，尤其在欠发达地区。目前主要用于识别胎儿当前状态（如酸中毒或缺氧），其预测未来不良妊娠结局的潜力尚未充分挖掘。本研究旨在开发一种基于人工智能的模型，从CTG时序数据中预测胎儿生物年龄（命名为CTGage），计算该年龄与实际年龄的差距（称为CTGage-gap），并将其作为预测未来不良妊娠结局的新数字生物标志物。模型基于2018至2022年北京大学人民医院收集的61,140条记录（来自11,385名孕妇）训练而成，采用结构化设计的一维卷积神经网络，并结合分布对齐增强回归技术。将CTGage-gap分为五类：< -21天（低估组）、-21至-7天、-7至7天（正常组）、7至21天、> 21天（高估组）。进一步将低估组与高估组合并为高风险组。比较各组不良妊娠结局及母体疾病发生率。CTGage模型平均绝对误差为10.91天。与正常组相比，高估组早产儿发生率为5.33% vs. 1.42%（p < 0.05），妊娠期糖尿病发生率为31.93% vs. 20.86%（p < 0.05）。与正常组相比，低估组低出生体重发生率为0.17% vs. 0.15%（p < 0.05），贫血发生率为37.51% vs. 34.74%（p < 0.05）。人工智能推导的CTGage可预测未来妊娠风险，具有作为新型非侵入性、易获取数字生物标志物的潜力。
原文摘要 · Abstract (English)
Cardiotocography (CTG) is a low-cost, non-invasive fetal health assessment technique used globally, especially in underdeveloped countries. However, it is currently mainly used to identify the fetus's current status (e.g., fetal acidosis or hypoxia), and the potential of CTG in predicting future adverse pregnancy outcomes has not been fully explored. We aim to develop an AI-based model that predicts biological age from CTG time series (named CTGage), then calculate the age gap between CTGage and actual age (named CTGage-gap), and use this gap as a new digital biomarker for future adverse pregnancy outcomes. The CTGage model is developed using 61,140 records from 11,385 pregnant women, collected at Peking University People's Hospital between 2018 and 2022. For model training, a structurally designed 1D convolutional neural network is used, incorporating distribution-aligned augmented regression technology. The CTGage-gap is categorized into five groups: < -21 days (underestimation group), -21 to -7 days, -7 to 7 days (normal group), 7 to 21 days, and > 21 days (overestimation group). We further defined the underestimation group and overestimation group together as the high-risk group. We then compare the incidence of adverse outcomes and maternal diseases across these groups. The average absolute error of the CTGage model is 10.91 days. When comparing the overestimation group with the normal group, premature infants incidence is 5.33% vs. 1.42% (p < 0.05) and gestational diabetes mellitus (GDM) incidence is 31.93% vs. 20.86% (p < 0.05). When comparing the underestimation group with the normal group, low birth weight incidence is 0.17% vs. 0.15% (p < 0.05) and anaemia incidence is 37.51% vs. 34.74% (p < 0.05). Artificial intelligence-derived CTGage can predict the future risk of adverse pregnancy outcomes and hold potential as a novel, non-invasive, and easily accessible digital biomarker.
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