Diagnostic accuracy and quality of artificial intelligence models in irritable bowel syndrome: A systematic review

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.