在糖尿病和非糖尿病怀孕中,基于机器学习的预测孕期大大的新生儿
Ohad Houri1,2,3, Asaf Romano1,2, Asnat Walfisch1,4
1Helen Schneider Hospital for Women, Rabin Medical Center, Petach Tikva, Israel.
概括
与传统公式相比,一种新的机器学习模型显著改善了对妊娠年龄大 (LGA) 新生儿的预测. 这种先进的工具将超声波测量与孕产妇健康数据相结合,使所有怀孕,包括糖尿病怀孕的怀孕更准确.
科学领域:
- 孕产妇和胎儿的医学
- 医疗人工智能的人工智能
- 生物医学数据科学是生物医学数据科学.
背景情况:
- 准确预测孕期大 (LGA) 新生儿对于优化围产期结果至关重要.
- 现有的方法,如哈德洛克估计胎儿体重 (EFW) 公式,在预测准确性方面存在局限性.
研究的目的:
- 评估一个机器学习模型,将超声波生物识别与孕产妇临床参数相结合,以改善LGA预测.
- 为了将这个新型模型的性能与哈德洛克的EFW公式进行比较.
主要方法:
- 单胎活产 (怀孕≥32周) 的回顾性队列研究.
- 预测因素包括胎儿生物识别 (双双体直径,腹周,股骨长度) 和母亲数据 (人口统计,病史,并发症,葡萄糖水平).
- 开发了一个CatBoost梯度增强模型,并与Hadlock的EFW进行了比较,使用AUC和10%的FPR检测率.
主要成果:
- 与Hadlock的EFW (0.867;P=0.01) 相比,机器学习模型实现了明显更高的AUC (0.946).
- 该模型显示,LGA新生儿的检测率优于LGA新生儿的检测率 (79%对比63%在10%的FPR).
- 在糖尿病怀孕中,表现仍然很高 (Hadlock EFW的AUC为0.890对比0.820;P=0.02).
结论:
- 开发的机器学习算法集成了超声波和非超声波特征,在预测LGA新生儿时提供了卓越的准确性.
- 这种增强的预测能力扩展到一般的产科人口和因糖尿病而复杂的怀孕.
- 这些发现支持先进的人工智能模型的临床实用性,用于改善胎儿生长评估.
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