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

Table 1 Characteristics of the included studies

Ref.	Journal	Country	Sample size	Sex (male:female)	IBS	Control	Biomarker	AI used	Specificity	Sensitivity	Accuracy	AUC
Shepherd et al[17], 2014	J Breath Res	United Kingdom	80		34	46	Gas chromatography	Artificial neural network analyses			0.54
Aggio et al[18], 2017	Aliment Pharmacol Ther	United Kingdom	69	26:43	28	41	Gas chromatography	ML (SVM)			0.91
Mao et al[19], 2020	Hum Brain Mapp	China	68	34:34	34	34	Neuroimaging, HF	ML (SVM)	HF: 0.671, 0.806, 0.529; all: 0.750	HF: 0.626, 0.438, 0.668; all: 0.679	HF: 0.649, 0.619, 0.599; all: 0.715	HF: 0.708, 0.659, 0.61; all: 0.776
Fukui et al[8], 2020	J Clin Med	Japan	111	46:65	85	26	Gut microbiome	ML (random forest model)	> 0.90	> 0.80		0.846
Su et al[9], 2022	Nat Commun	China	1038		145	893	Gut microbiome	ML (random forests, k-nearest neighbors, SVM multi-layer perceptron and SVM)	0.98	0.94	0.98	0.99
Tanaka et al[20], 2023	Front Microbiol	Japan	70	70:0	35	35	Protease activity, C-terminal residue of K, R, S, or G, all probes, microbiome, and metabolome	ML (random forest model)		Protease activity: 0.727, and all probes: 0.909	Protease activity: 0.81, and all probes: 0.905	Protease activity: 0.83, all probes: 0.92, microbiome: 0.58, metabolome: 0.67

IBS: Irritable bowel syndrome; AI: Artificial intelligence; AUC: Area under the curve; HF: Habenula function; ML: Machine learning; SVM: Support vector machine.