Lee Kwang-Sig, Kim Eun Sun
AI Center, Korea University Anam Hospital, Seoul 02841, Republic of Korea.
Department of Gastroenterology, Korea University Anam Hospital, Seoul 02841, Republic of Korea.
Diagnostics (Basel). 2025 Sep 2;15(17):2227. doi: 10.3390/diagnostics15172227.
: The application of predictive and explainable artificial intelligence to bioinformatics data such as single nucleotide polymorphism (SNP) information is attracting rising attention in the diagnosis of various diseases. However, there are few reviews available on the recent progress of genetic artificial intelligence for the early diagnosis of gastrointestinal disease (GID). The purpose of this study is to complete a systematic review on the recent progress of genetic artificial intelligence in GID. : The source of data was ten original studies from PubMed. The ten original studies were eligible according to the following criteria: (participants) the dependent variable of GID or associated disease; (interventions/comparisons) artificial intelligence; (outcomes) accuracy, the area under the curve (AUC), and/or variable importance; a publication year of 2010 or later; and the publication language of English. : The performance outcomes reported varied within 79-100 for accuracy (%) and 63-98 for the AUC (%). Random forest was the best approach (AUC 98%) for the classification of inflammatory bowel disease with 13 single nucleotide polymorphisms (SNPs). Similarly, random forest was the best method (R-square 99%) for the regression of the gut microbiome SNP saturation number. The following SNPs were discovered to be major variables for the prediction of GID or associated disease: rs2295778, rs13337626, rs2296188, rs2114039 (esophageal adenocarcinoma); rs28785174, rs60532570, rs13056955, rs7660164 (Crohn's disease early intestinal resection); rs4945943 (Crohn's disease); rs316115020, rs316420452 (calcium metabolism); rs738409_G, rs2642438_A, rs58542926_T, rs72613567_TA (steatotic liver disease); rs148710154, rs75146099 (esophageal squamous cell carcinoma). The following demographic and health-related variables were found to be important predictors of GID or associated disease besides SNPs: age, body mass index, disease behavior, immune cell type, intestinal microbiome, MARCKS protein, smoking, and SNP density/number. No deep learning study was found even though deep learning was used as a search term together with machine learning. : Genetic artificial intelligence is effective and non-invasive as a decision support system for GID.
将预测性和可解释性人工智能应用于生物信息学数据,如单核苷酸多态性(SNP)信息,在各种疾病的诊断中越来越受到关注。然而,关于遗传人工智能在胃肠道疾病(GID)早期诊断方面的最新进展的综述却很少。本研究的目的是对遗传人工智能在GID方面的最新进展进行系统综述。:数据来源是来自PubMed的十项原创研究。这十项原创研究符合以下标准:(参与者)GID或相关疾病的因变量;(干预措施/对照)人工智能;(结果)准确性、曲线下面积(AUC)和/或变量重要性;2010年或之后的发表年份;以及英文发表语言。:报告的性能结果在准确性(%)方面为79 - 100,在AUC(%)方面为63 - 98。随机森林是用于通过13个单核苷酸多态性(SNP)对炎症性肠病进行分类的最佳方法(AUC 98%)。同样,随机森林是用于肠道微生物组SNP饱和度数量回归的最佳方法(决定系数99%)。以下SNP被发现是预测GID或相关疾病的主要变量:rs2295778、rs13337626、rs2296188、rs2114039(食管腺癌);rs28785174、rs60532570、rs13056955、rs7660164(克罗恩病早期肠道切除术);rs4945943(克罗恩病);rs316115020、rs316420452(钙代谢);rs738409_G、rs2642438_A、rs58542926_T、rs72613567_TA(脂肪性肝病);rs148710154、rs75146099(食管鳞状细胞癌)。除了SNP之外,还发现以下人口统计学和健康相关变量是GID或相关疾病的重要预测因素:年龄、体重指数、疾病行为、免疫细胞类型、肠道微生物组、MARCKS蛋白、吸烟以及SNP密度/数量。即使将深度学习与机器学习一起用作搜索词,也未发现深度学习研究。:遗传人工智能作为GID的决策支持系统是有效且非侵入性的。