AI-guided meta-analysis of non-invasive prenatal testing platforms for trisomy 21 screening: comparative evaluation of cffDNA and fetal cell-based approaches
Abstract
Background: Trisomy 21 (Down syndrome) remains the most prevalent autosomal aneuploidy, necessitating accurate prenatal diagnosis. While cell-free fetal DNA (cffDNA)-based non-invasive prenatal testing (NIPT) has transformed screening, challenges persist in low fetal fraction cases and confined placental mosaicism. Integrating artificial intelligence (AI), multi-omics, and fetal cell-based approaches represents a paradigm shift toward comprehensive prenatal diagnostics.
Objective: This AI-assisted meta-analysis evaluates NIPT platforms for Trisomy 21 detection, comparing cffDNA-based, SNP-based, digital PCR, and emerging fetal cell-based approaches.
Methods: Following PRISMA guidelines, we systematically searched PubMed, Web of Science, and Scopus (2010-2023). Ten high-quality studies were analyzed for diagnostic accuracy metrics across multiple NIPT platforms.
Results: MPS-based cffDNA NIPT demonstrated pooled sensitivity of 99.3% (95% CI: 98.7-99.7%) and specificity of 99.9% (95% CI: 99.8-100%). Alternative platforms, including fetal cell-based NIPT, showed promising but moderately lower accuracy. AI-driven risk stratification models demonstrated enhanced diagnostic precision in complex cases.
Conclusion: MPS-based NIPT remains the gold standard for Trisomy 21 screening. Future integration of AI-enhanced fetal cell isolation and multi-omics profiling may expand diagnostic capabilities beyond aneuploidy detection.
How strong is the evidence?
Evidence Hub reads this as a meta-analysis, done in people. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
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