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超声和人工智能在胎儿染色体数目异常预测中的应用

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  • 1.中南大学湘雅基础医学院生殖与干细胞工程研究所长沙 410000
    2.中信湘雅生殖与遗传专科医院长沙 410000
唐琳瑶,硕士研究生,专业方向:生殖医学。E-mail: tanglinyaotly@163.com
林戈,博士,研究员,研究方向:生殖医学。E-mail: linggf@hotmail.com

收稿日期: 2025-03-04

  修回日期: 2025-05-30

  网络出版日期: 2025-06-04

Application of ultrasound and artificial intelligence in the prediction of fetal chromosomal numerical abnormalities

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  • 1. Institute of Reproductive and Stem Cell Engineering, Xiangya School of Basic Medical Science, Central South University, Changsha 410000, China
    2. Reproductive and Genetic Hospital of CITIC-Xiangya, Changsha 410000, China

Received date: 2025-03-04

  Revised date: 2025-05-30

  Online published: 2025-06-04

摘要

胎儿染色体数目异常是导致妊娠丢失及出生缺陷的重要原因,超声因实时性、可重复性、安全性等特点成为胎儿染色体异常筛查的重要手段,但其临床应用仍受限于操作者经验差异和超声图像质量不一。随着人工智能技术引入传统超声,其预测胎儿染色体数目异常的人工智能模型突破传统筛查瓶颈且预测性能显著优于传统方法,可同步预警罕见染色体异常。本文介绍了近年来超声与人工智能在胎儿染色体数目异常预测中的协同应用,对比分析传统预测模型与人工智能预测模型各自的技术优势与局限性,探讨了多中心数据标准化、模型可解释性等挑战,为无创精准产前筛查提供新方向。

本文引用格式

唐琳瑶, 吴婧柔子, 林戈 . 超声和人工智能在胎儿染色体数目异常预测中的应用[J]. 遗传, 2025 , 47(12) : 1326 -1339 . DOI: 10.16288/j.yczz.25-070

Abstract

Fetal chromosomal numerical abnormalities is a significant cause of pregnancy loss and birth defects. Ultrasound has emerged as a critical modality for fetal chromosomal anomaly screening due to its real-time capability, repeatability, and safety. However, its clinical application remains constrained by operator expertise variability and inconsistent image quality. The integration of artificial intelligence (AI) into conventional ultrasound has enabled the development of AI-based predictive models that overcome traditional screening limitations. These models demonstrate superior predictive performance compared to conventional methods while enabling simultaneous detection of rare chromosomal abnormalities. This review summarizes recent advances in synergistic applications of ultrasound and AI for fetal chromosomal aneuploidy prediction, comparatively analyzes the technical strengths and limitations of traditional versus AI-based predictive models, and discusses challenges including multicenter data standardization and model interpretability. These advancements provide novel directions for non-invasive precision prenatal screening.

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