Araújo Daniella Castro, de Macedo Alexandre Afonso, Veloso Adriano Alonso, Alpoim Patricia Nessralla, Gomes Karina Braga, Carvalho Maria das Graças, Dusse Luci Maria SantAna
Huna, São Paulo, SP, Brazil.
Departamento de Ciência da Computação, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil.
BMC Pregnancy Childbirth. 2024 Oct 1;24(1):628. doi: 10.1186/s12884-024-06821-4.
This study introduces the complete blood count (CBC), a standard prenatal screening test, as a biomarker for diagnosing preeclampsia with severe features (sPE), employing machine learning models.
We used a boosting machine learning model fed with synthetic data generated through a new methodology called DAS (Data Augmentation and Smoothing). Using data from a Brazilian study including 132 pregnant women, we generated 3,552 synthetic samples for model training. To improve interpretability, we also provided a ridge regression model.
Our boosting model obtained an AUROC of 0.90±0.10, sensitivity of 0.95, and specificity of 0.79 to differentiate sPE and non-PE pregnant women, using CBC parameters of neutrophils count, mean corpuscular hemoglobin (MCH), and the aggregate index of systemic inflammation (AISI). In addition, we provided a ridge regression equation using the same three CBC parameters, which is fully interpretable and achieved an AUROC of 0.79±0.10 to differentiate the both groups. Moreover, we also showed that a monocyte count lower than yielded a sensitivity of 0.71 and specificity of 0.72.
Our study showed that ML-powered CBC could be used as a biomarker for sPE diagnosis support. In addition, we showed that a low monocyte count alone could be an indicator of sPE.
Although preeclampsia has been extensively studied, no laboratory biomarker with favorable cost-effectiveness has been proposed. Using artificial intelligence, we proposed to use the CBC, a low-cost, fast, and well-spread blood test, as a biomarker for sPE.
本研究引入全血细胞计数（CBC）这一标准产前筛查测试，将其作为一种生物标志物，利用机器学习模型诊断重度子痫前期（sPE）。
我们使用了一种通过名为DAS（数据增强与平滑）的新方法生成的合成数据来训练的增强型机器学习模型。利用一项巴西研究中的数据，该研究包括132名孕妇，我们生成了3552个合成样本用于模型训练。为了提高可解释性，我们还提供了一个岭回归模型。
我们的增强模型在使用中性粒细胞计数、平均红细胞血红蛋白含量（MCH）和全身炎症综合指数（AISI）这些CBC参数来区分sPE孕妇和非PE孕妇时，曲线下面积（AUROC）为0.90±0.10，灵敏度为0.95，特异性为0.79。此外，我们提供了一个使用相同三个CBC参数的岭回归方程，该方程完全可解释，区分两组的AUROC为0.79±0.10。而且，我们还表明单核细胞计数低于某个值时，灵敏度为0.71，特异性为0.72。
我们的研究表明，基于机器学习的CBC可作为支持sPE诊断的生物标志物。此外，我们表明单独的低单核细胞计数可能是sPE的一个指标。
尽管子痫前期已得到广泛研究，但尚未提出具有良好成本效益的实验室生物标志物。利用人工智能，我们提议将CBC这种低成本、快速且广泛应用的血液检测作为sPE的生物标志物。
