Lin Qiongfen, Tam Paul Kwong-Hang, Tang Clara Sze-Man
Department of Surgery, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
Faculty of Medicine, Macau University of Science and Technology, Macau, Macau SAR, China.
Front Pediatr. 2023 Aug 1;11:1203289. doi: 10.3389/fped.2023.1203289. eCollection 2023.
Genetic mutations are critical factors leading to congenital surgical diseases and can be identified through genomic analysis. Early and accurate identification of genetic mutations underlying these conditions is vital for clinical diagnosis and effective treatment. In recent years, artificial intelligence (AI) has been widely applied for analyzing genomic data in various clinical settings, including congenital surgical diseases. This review paper summarizes current state-of-the-art AI-based approaches used in genomic analysis and highlighted some successful applications that deepen our understanding of the etiology of several congenital surgical diseases. We focus on the AI methods designed for the detection of different variant types and the prioritization of deleterious variants located in different genomic regions, aiming to uncover susceptibility genomic mutations contributed to congenital surgical disorders.
基因突变是导致先天性外科疾病的关键因素，可通过基因组分析进行识别。早期准确识别这些疾病背后的基因突变对于临床诊断和有效治疗至关重要。近年来，人工智能（AI）已广泛应用于各种临床环境中的基因组数据分析，包括先天性外科疾病。这篇综述文章总结了目前用于基因组分析的基于AI的最新方法，并强调了一些成功应用，这些应用加深了我们对几种先天性外科疾病病因的理解。我们专注于为检测不同变异类型以及对位于不同基因组区域的有害变异进行优先级排序而设计的AI方法，旨在揭示导致先天性外科疾病的易感性基因组突变。
