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

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.

Keywords

• usm
• nipt
• chromosome
• fetus
• machine learning
• data mining