超声和人工智能在胎儿染色体数目异常预测中的应用

doi:10.16288/j.yczz.25-070

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Hereditas(Beijing) ›› 2025, Vol. 47 ›› Issue (12): 1326-1339.doi: 10.16288/j.yczz.25-070

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

Linyao Tang¹(), Jingrouzi Wu², Ge Lin¹^,²()

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:2025-03-04 Revised:2025-05-30 Online:2025-06-04 Published:2025-06-04
Contact: Ge Lin E-mail:tanglinyaotly@163.com;linggf@hotmail.com

Abstract

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.

Key words: fetal chromosomal numerical abnormalities, ultrasonography imaging, artificial intelligence, prenatal screening

Linyao Tang, Jingrouzi Wu, Ge Lin. Application of ultrasound and artificial intelligence in the prediction of fetal chromosomal numerical abnormalities[J]. Hereditas(Beijing), 2025, 47(12): 1326-1339.

Figures/Tables 3

Fig. 1

Table 1

Table 2

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模型	优势	局限性	参考文献
卷积神经网络(CNN)	· 高效医学图像分析 · 自动学习分层特征 · 处理各种医学图像模式	· 需要大量已标记数据 · 若计算量大存在硬件要求 · 数据量小容易过拟合	[46,47]
递归神经网络(RNN)	· 可处理顺序数据 · 可捕获数据时间依赖性 · 可处理心电图数据	· 出现数据梯度消失问题 · 数据序列过长时分析受阻 · 计算成本高	[48]
长短期记忆递归神经网络(LSTM)	· 可缓解梯度消失问题 · 适合构建时间模式模型 · 可处理脑电图信号数据	· 复杂架构可能导致过拟合 · 训练速度比标准CNN慢 · 无法进行超参数调优	[49]
Transformer	· 可处理医学文本数据 · 通过自我注意机制捕捉上下文 · 可处理可变长度的序列数据	· 最初是用于固定序列数据分析 · 可能需要大量数据 · 若计算量大存在硬件要求	[50]
残差神经网络(ResNet)	· 可处理存在深层残余链接的数据 · 可解决梯度消失问题 · 可处理图像分类问题 · 是较好的迁移学习概述模型	· 模型较为复杂 · 需要大量训练数据 · 计算量较大	[51]

报道文献	算法类型	预测疾病类型	样本量	孕周	模型性能	优势与局限
Verma等^[54]	自适应梯度下降算法	胎儿染色体异常	100张超声图像	11~14	准确率98.64%；精确率97.2%	预测模型准确率高性能优越，但样本量较小
Thomas等^[55]	AlexNet	唐氏综合征	100张超声图像	11~14	分类准确率91.7%； AUC=0.95	该系统对唐氏综合征分类判断准确率极高，但样本量较小
Sun等^[56]	LASSO回归	21-三体综合征	624例胎儿超声图像	11~14	AUC=0.98	将颈项透明层与面部参数结合，提高早期识别能力，但需要高质量的图像和测量手段
Zhang等^[57]	卷积神经网络(CNN)	21-三体综合征	822例胎儿超声图像	11~14	训练集AUC=0.98；验证集AUC=0.95	深度学习模型优于传统方法，但需要大量已被标记的数据，数据前期处理量大
Yekdast等^[58]	卷积神经网络(CNN)和粒子群优算法	唐氏综合征	300张超声图像	11~14	准确率99.38%	准确率极高，但研究为单中心研究且样本量稍小
Tang等^[59](Fgds-EL)	卷积神经网络(CNN)和随机森林(RF)集成学习	多种遗传性疾病	932张胎儿超声图像	11~14	敏感性0.92	实现多类型染色体数目异常同步筛查，但需要多种遗传疾病的标记数据
Tang等^[60] (Pgds-ResNet)	深度神经网络(DNN)	多种遗传性疾病	1,120例胎儿超声图像	11~14	敏感性0.83~0.96	可通过非侵入性方法筛查多种遗传性疾病，但对罕见疾病的识别还需进一步研究

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

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