Odendaal H J, Crockart I C, Du Plessis C, Brink L, Groenewald C A
Department of Obstetrics and Gynaecology, Stellenbosch University, South Africa.
Department of Mechanical and Mechatronic Engineering, Stellenbosch University, South Africa.
Glob J Pediatr Neonatal Care. 2021;3(5). Epub 2021 Oct 30.
To use machine learning to determine what information on Doppler velocimetry and maternal and fetal heart rates, collected at 20-24 weeks gestation, correlates best with fetal growth restriction according to the estimated fetal weight at 34-38 weeks.
Data of 4496 pregnant women, collected prospectively for the Safe Passage Study, from August 2007 to August 2016, were used for the present analysis. Doppler flow velocity of the uterine, umbilical, and middle cerebral arteries and transabdominally recorded maternal and fetal ECGs were collected at 20-24 weeks gestation and fetal biometry collected at 34-38 weeks from which the estimated fetal weight was calculated. Fetal growth restriction was defined as an estimated fetal weight below the 10th centile. Accelerations and decelerations of the fetal and maternal heart rates were quantified as gained or lost beats per hour of recording respectively. Machine learning with receiver operative characteristic curves were then used to determine which model gives the best performance.
The final model performed exceptionally well across all evaluation metrics, particularly so for the Stochastic Gradient Descent method: achieving a 93% average for Classification Accuracy, Recall, Precision and F1-Score to identify the fetus with an estimated weight below the 10th percentile at 34-38 weeks. Ranking determined that the most important standard feature was the umbilical artery pulsatility index. However, the excellent overall accuracy is likely due to the value added by the pre-processed features regarding fetal gained beats and accelerations.
Fetal movements, as characterized by gained beats as early as 20-24 weeks gestation, contribute to the value of the flow velocimetry of the umbilical artery at 34-38 weeks in identifying the growth restricted fetus.
运用机器学习来确定妊娠20 - 24周时收集的多普勒测速以及母体和胎儿心率的哪些信息,与根据34 - 38周估计胎儿体重得出的胎儿生长受限最为相关。
本分析使用了2007年8月至2016年8月期间为安全通道研究前瞻性收集的4496名孕妇的数据。在妊娠20 - 24周时收集子宫、脐动脉和大脑中动脉的多普勒流速以及经腹记录的母体和胎儿心电图,并在34 - 38周时收集胎儿生物测量数据,据此计算估计胎儿体重。胎儿生长受限定义为估计胎儿体重低于第10百分位数。胎儿和母体心率的加速和减速分别量化为每小时记录中增加或减少的心跳次数。然后使用带有受试者工作特征曲线的机器学习来确定哪种模型表现最佳。
最终模型在所有评估指标上表现出色,随机梯度下降法尤其如此:在识别34 - 38周时估计体重低于第10百分位数的胎儿方面,分类准确率、召回率、精确率和F1分数平均达到93%。排名确定最重要的标准特征是脐动脉搏动指数。然而,总体准确率高可能归因于预处理的关于胎儿心跳增加和加速的特征所增加的价值。
早在妊娠20 - 24周时以心跳增加为特征的胎儿活动,有助于34 - 38周时脐动脉流速测定在识别生长受限胎儿方面的价值。