Bhagavathula Akshaya Srikanth, Al Qady Ahmed Mourtada, Aldhaleei Wafa A
Department of Public Health, College of Health and Human Sciences, North Dakota State University, Fargo, ND 58102, United States.
Division of Gastroenterology, Hepatology and Nutrition, University of Florida, Gainesville, FL 32607, United States.
World J Gastroenterol. 2025 Jun 21;31(23):106836. doi: 10.3748/wjg.v31.i23.106836.
Irritable bowel syndrome (IBS) affects approximately 9%-12% of the global population, presenting substantial diagnostic challenges due to symptom subjectivity and lack of definitive biomarkers.
To systematically examine the diagnostic accuracy of artificial intelligence (AI) models applied to various biomarkers in IBS diagnosis.
A comprehensive search of six databases identified 18053 articles published up to May 31, 2024. Following screening and eligibility criteria, six observational studies involving 1366 participants from the United Kingdom, China, and Japan were included. Risk of bias and reporting quality were assessed using quality assessment of diagnostic accuracy studies-2, prediction model risk of bias assessment tool-AI, and transparent reporting of a multivariable prediction model for individual prognosis or diagnosis-AI tools. Key metrics included sensitivity, specificity, accuracy, and area under the curve (AUC).
The included studies applied AI models such as random forests, support vector machines, and neural networks to biomarkers like fecal microbiome composition, gas chromatography data, neuroimaging features, and protease activity. Diagnostic accuracy ranged from 54% to 98% (AUC: 0.61-0.99). Models using fecal microbiome data achieved the highest performance, with one study reporting 98% sensitivity and specificity (AUC = 0.99). While most studies demonstrated high methodological quality, significant variability in datasets, biomarkers, and validation methods limited meta-analysis feasibility and generalizability.
AI models show potential to improve IBS diagnostic accuracy by integrating complex biomarkers which will aid the development of algorithms to direct treatment strategies. However, methodological inconsistencies and limited population diversity underscore the need for standardized protocols and external validation to ensure clinical applicability.
肠易激综合征（IBS）影响着全球约9%-12%的人口，由于症状的主观性和缺乏明确的生物标志物，带来了巨大的诊断挑战。
系统地检验应用于IBS诊断中各种生物标志物的人工智能（AI）模型的诊断准确性。
对六个数据库进行全面检索，共识别出截至2024年5月31日发表的18053篇文章。按照筛选和纳入标准，纳入了六项观察性研究，涉及来自英国、中国和日本的1366名参与者。使用诊断准确性研究质量评估-2、预测模型偏倚风险评估工具-AI以及个体预后或诊断多变量预测模型透明报告-AI工具对偏倚风险和报告质量进行评估。关键指标包括敏感性、特异性、准确性和曲线下面积（AUC）。
纳入的研究将随机森林、支持向量机和神经网络等AI模型应用于粪便微生物群组成、气相色谱数据、神经影像特征和蛋白酶活性等生物标志物。诊断准确性范围为54%至98%（AUC：0.61-0.99）。使用粪便微生物群数据的模型表现最佳，一项研究报告其敏感性和特异性均为98%（AUC = 0.99）。虽然大多数研究显示出较高的方法学质量，但数据集、生物标志物和验证方法的显著差异限制了荟萃分析的可行性和普遍性。
AI模型通过整合复杂的生物标志物显示出提高IBS诊断准确性的潜力，这将有助于开发指导治疗策略的算法。然而，方法学上的不一致和有限的人群多样性凸显了标准化方案和外部验证以确保临床适用性的必要性。
