Shear Matthew A, Robinson Peter N, Sparks Teresa N
Division of Maternal-Fetal Medicine, Department of Obstetrics, Gynecology, & Reproductive Sciences, University of California, San Francisco, California, USA; Division of Medical Genetics, Department of Pediatrics, University of California, San Francisco, California, USA.
Berlin Institute of Health at Charité, Berlin, Germany.
Best Pract Res Clin Obstet Gynaecol. 2025 Feb;98:102575. doi: 10.1016/j.bpobgyn.2024.102575. Epub 2024 Dec 15.
Genetic tests available in the prenatal setting have expanded rapidly with next generation sequencing, and fetal imaging can detect a breadth of many structural and functional abnormalities. To identify a fetal genetic disease, deep phenotyping is increasingly important to generate a differential diagnosis, choose the most appropriate genetic tests, and inform the results of those tests. The Human Phenotype Ontology (HPO) organizes and defines the features of human disease to support deep phenotyping, and ongoing efforts are being made to improve the scope of the HPO to comprehensively include fetal phenotypes. There are important limitations of fetal phenotyping to understand, including ongoing structural development and limited knowledge of how many genetic diseases present uniquely in utero. This article provides an overview of the use of HPO terms and artificial intelligence in the approach to fetal phenotyping and genetic testing.
随着下一代测序技术的发展，产前可用的基因检测迅速增加，胎儿成像可以检测出多种结构和功能异常。为了识别胎儿遗传疾病，深度表型分析对于进行鉴别诊断、选择最合适的基因检测以及解读这些检测结果变得越来越重要。人类表型本体（HPO）组织并定义了人类疾病的特征以支持深度表型分析，并且正在不断努力扩大HPO的范围以全面纳入胎儿表型。胎儿表型分析存在一些重要的局限性需要了解，包括持续的结构发育以及对子宫内独特呈现的遗传疾病数量的了解有限。本文概述了HPO术语和人工智能在胎儿表型分析和基因检测方法中的应用。
