化学
代谢组学
胎心
产前筛查
胎儿
色谱法
产前诊断
怀孕
生物
遗传学
作者
Bianting Sun,Yiwei Fang,Hui Ye,Mingming Fan,Chao He,Yun Zhao,Kai Zhao,Huiping Zhang
出处
期刊:Talanta
[Elsevier]
日期:2024-04-01
卷期号:: 126109-126109
标识
DOI:10.1016/j.talanta.2024.126109
摘要
To investigate the metabolic alterations in maternal individuals with fetal congenital heart disease (FCHD), establish the FCHD diagnostic models, and assess the performance of these models, we recruited two batches of pregnant women. By metabolomics analysis using Ultra High-performance Liquid Chromatography-Mass/Mass (UPLC-MS/MS), a total of 36 significantly altered metabolites (VIP >1.0) were identified between FCHD and non-FCHD groups. Two logistic regression models and four support vector machine (SVM) models exhibited strong performance and clinical utility in the training set (area under the curve (AUC) =1.00). The convolutional neural network (CNN) model also demonstrated commendable performance and clinical utility (AUC=0.89 in the training set). Notably, in the validation set, the performance of the CNN model (AUC=0.66, precision = 0.714) exhibited better robustness than the six models above (AUC≤0.50). In conclusion, the CNN model based on pseudo-MS images holds promise for real-world and clinical applications due to its better repeatability.
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