ArticlePloS one2018
Improving the calling of non-invasive prenatal testing on 13-/18-/21-trisomy by support vector machine discrimination.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 22 citations in OpenAlex.
- Towards deep phenotyping pregnancy: a systematic review on artificial intelligence and machine learning methods to improve pregnancy outcomes.Briefings in bioinformatics · 2021Pooled it
- MRI-based habitat, intra-, and peritumoral machine learning model for perineural invasion prediction in rectal cancer.Abdominal radiology (New York) · 2026Article
- A Hybrid Artificial Intelligence Approach for Down Syndrome Risk Prediction in First Trimester Screening.Diagnostics (Basel, Switzerland) · 2025Article
- Predictive Study of Machine Learning-Based Multiparametric MRI Radiomics Nomogram for Perineural Invasion in Rectal Cancer: A Pilot Study.Journal of imaging informatics in medicine · 2025Article
- Artificial intelligence and machine learning in cell-free-DNA-based diagnostics.Genome research · 2025Review
- Detection of Embryonic Trisomy 21 in the First Trimester Using Maternal Plasma Cell-Free RNA.Diagnostics (Basel, Switzerland) · 2022Article
- Development and performance evaluation of an artificial intelligence algorithm using cell-free DNA fragment distance for non-invasive prenatal testing (aiD-NIPT).Frontiers in genetics · 2022Article
- Intra- and Peritumoral Radiomics Model Based on Early DCE-MRI for Preoperative Prediction of Molecular Subtypes in Invasive Ductal Breast Carcinoma: A Multitask Machine Learning Study.Frontiers in oncology · 2022Article
- Noninvasive prenatal testing: from aneuploidy to single genes.Human genetics · 2020Review
- Diagnosis support systems for rare diseases: a scoping review.Orphanet journal of rare diseases · 2020Article
- A Novel Graphic-Aided Algorithm (gNIPT) Improves the Accuracy of Noninvasive Prenatal Testing.BioMed research international · 2020Article
- NIPTeR: an R package for fast and accurate trisomy prediction in non-invasive prenatal testing.BMC bioinformatics · 2018Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the advance of next-generation sequencing (NGS) technologies, non-invasive prenatal testing (NIPT) has been developed and employed in fetal aneuploidy screening on 13-/18-/21-trisomies through detecting cell-free fetal DNA (cffDNA) in maternal blood. Although Z-test is widely used in NIPT NGS data analysis, there is still necessity to improve its accuracy for reducing a) false negatives and false positives, and b) the ratio of unclassified data, so as to lower the potential harm to patients as well as the induced cost of retests. Combining the multiple Z-tests with indexes of clinical signs and quality control, features were collected from the known samples and scaled for model training using support vector machine (SVM). We trained SVM models from the qualified NIPT NGS data that Z-test can discriminate and tested the performance on the data that Z-test cannot discriminate. On screenings of 13-/18-/21-trisomies, the trained SVM models achieved 100% accuracies in both internal validations and unknown sample predictions. It is shown that other machine learning (ML) models can also achieve similar high accuracy, and SVM model is most robust in this study. Moreover, four false positives and four false negatives caused by Z-test were corrected by using the SVM models. To our knowledge, this is one of the earliest studies to employ SVM in NIPT NGS data analysis. It is expected to replace Z-test in clinical practice.
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