Synthetic data-driven AI approach for fetal chromosomal aneuploidies detection.

ABSTRACT:

Motivation

A major limitation in the development of fetal chromosomal aneuploidy detection technologies lies in the scarcity of real positive data. To address this issue, we propose a novel methodology to generate virtually unlimited synthetic negative and positive datasets with >99.9% similarity to real data, enabling accurate detection of both autosomal chromosome aneuploidies (ACA) and sex chromosome aneuploidies (SCA). In terms of methods, blood samples from 15 999 pregnant women were analyzed, including 186 clinically confirmed positive cases. Using 701 high-confidence negatives as a reference, we designed algorithms for synthetic data generation. For negatives, multiple real FASTQ files were randomly merged, and fetal fraction (FF) was recalculated to reflect biological varia

SUBMITTER: Hwang C 

PROVIDER: S-EPMC12557104 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature