Development and performance evaluation of an artificial intelligence algorithm using cell-free DNA fragment distance for non-invasive prenatal testing (aiD-NIPT)

Junnam Lee; Sae-Mi Lee; Jin Mo Ahn; Tae-Rim Lee; Wan Kim; Eun-Hae Cho; Chang-Seok Ki

doi:10.3389/fgene.2022.999587

Development and performance evaluation of an artificial intelligence algorithm using cell-free DNA fragment distance for non-invasive prenatal testing (aiD-NIPT)

Front Genet. 2022 Nov 29:13:999587. doi: 10.3389/fgene.2022.999587. eCollection 2022.

Authors

Junnam Lee^{1

2}, Sae-Mi Lee¹, Jin Mo Ahn¹, Tae-Rim Lee¹, Wan Kim¹, Eun-Hae Cho¹, Chang-Seok Ki¹

Affiliations

¹ Genome Research Center, GC Genome, Yongin, South Korea.
² Department of Bioinformatics, Soongsil University, Seoul, South Korea.

Abstract

With advances in next-generation sequencing technology, non-invasive prenatal testing (NIPT) has been widely implemented to detect fetal aneuploidies, including trisomy 21, 18, and 13 (T21, T18, and T13). Most NIPT methods use cell-free DNA (cfDNA) fragment count (FC) in maternal blood. In this study, we developed a novel NIPT method using cfDNA fragment distance (FD) and convolutional neural network-based artificial intelligence algorithm (aiD-NIPT). Four types of aiD-NIPT algorithm (mean, median, interquartile range, and its ensemble) were developed using 2,215 samples. In an analysis of 17,678 clinical samples, all algorithms showed >99.40% accuracy for T21/T18/T13, and the ensemble algorithm showed the best performance (sensitivity: 99.07%, positive predictive value (PPV): 88.43%); the FC-based conventional Z-score and normalized chromosomal value showed 98.15% sensitivity, with 40.77% and 36.81% PPV, respectively. In conclusion, FD-based aiD-NIPT was successfully developed, and it showed better performance than FC-based NIPT methods.

Keywords: artificial intelligence algorithm (AI); cfDNA fragment distance; convolutional neural network (CNN); fetal chromosomal aneuploidy; non-invasive prenatal testing (NIPT).