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基于机器学习的 USM 联合 NIPT 在胎儿染色体异常诊断中的应用价值评估。

Machine learning-based evaluation of application value of the USM combined with NIPT in the diagnosis of fetal chromosomal abnormalities.

机构信息

Department of Reproductive Medicine, Shenzhen Second People's Hospital, Guangdong Province, 518035, China.

Department of Obstetrics and Gynecology, Shenzhen Second People's Hospital, Guangdong Province, 518035, China.

出版信息

Math Biosci Eng. 2022 Feb 25;19(4):4260-4276. doi: 10.3934/mbe.2022197.

DOI:10.3934/mbe.2022197
PMID:35341297
Abstract

OBJECTIVE

To explore the soft ultrasound marker (USM) combined with non-invasive prenatal testing (NIPT) in diagnosing fetal chromosomal abnormalities based on machine learning and data mining techniques.

METHODS

To analyze the data of ultrasonic examination from 856 cases with high-risk single pregnancy during early and middle pregnancy stage. NIPT was applied in 642 patients. All 856 patients accepted amniocentesis and chromosome karyotype analysis to determine the efficacy of USM, Down's syndrome screening, and NIPT in detecting fetal chromosomal abnormalities.

RESULTS

Among the 856 fetuses, 129 fetuses (15.07%) with single positive USM and 36 fetuses (4.21%) with two or more positive USM. There were 81 fetuses (9.46%) with chromosomal abnormalities. In the group with multiple USM, chromosomal abnormalities were found in 36.11% of them. It was higher than the group without USM, which was 6.22% (P < 0.01), and the group with just a single USM (19.38%, P < 0.05). The sensitivity, specificity and accuracy were 96.72%, 98.45% and 98.29% when the combination of USM, Down's syndrome screening and NIPT was used to diagnose fetal chromosomal abnormalities further evaluating the accuracy and effectiveness of the above diagnostic criteria and methods with mainstream Classifiers based evaluation indicators of accuracy, f1 score, AUC.

CONCLUSIONS

The combination of USM, Down's syndrome screening and NIPT is valuable for the diagnosis of fetal chromosomal abnormalities.

摘要

目的

探讨基于机器学习和数据挖掘技术的软超声标记物(USM)联合无创产前检测(NIPT)在诊断胎儿染色体异常中的应用。

方法

分析 856 例早中期高危单胎妊娠的超声检查数据。642 例患者进行了 NIPT 检查。所有 856 例患者均接受羊膜穿刺术和染色体核型分析,以确定 USM、唐氏综合征筛查和 NIPT 检测胎儿染色体异常的效果。

结果

在 856 例胎儿中,129 例胎儿(15.07%)USM 单项阳性,36 例胎儿(4.21%)两项或两项以上 USM 阳性。81 例胎儿(9.46%)染色体异常。在多项 USM 阳性的胎儿中,发现染色体异常的比例为 36.11%,高于无 USM 阳性的胎儿(6.22%)(P<0.01),也高于仅单项 USM 阳性的胎儿(19.38%)(P<0.05)。USM、唐氏综合征筛查和 NIPT 联合应用进一步诊断胎儿染色体异常的灵敏度、特异度和准确率分别为 96.72%、98.45%和 98.29%。使用主流分类器基于准确率、f1 得分、AUC 等评价指标对上述诊断标准和方法进行评估,验证其准确性和有效性。

结论

USM、唐氏综合征筛查和 NIPT 的联合应用对胎儿染色体异常的诊断具有一定价值。

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Math Biosci Eng. 2022 Feb 25;19(4):4260-4276. doi: 10.3934/mbe.2022197.
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Advancements in machine learning and biomarker integration for prenatal Down syndrome screening.用于产前唐氏综合征筛查的机器学习与生物标志物整合的进展。
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Prenatal chromosomal microarray analysis in a large Chinese cohort of fetuses with congenital heart defects: a single center study.产前染色体微阵列分析在先天性心脏病胎儿的大型中国队列中的应用:一项单中心研究。
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