Translational artificial intelligence in gastrointestinal and hepatic disorders: Advancing intelligent clinical decision-making for diagnosis, treatment, and prognosis

Table 3 Summary of artificial intelligence in intestinal diseases

Disease	Application	Ref.	Study design	Country/region	Modality	Test set	AI model	Main findings
Crohn's disease	Diagnosis	Li et al[66]	R	China	CTE, histopathology	167 patients	ML	A validated CTE-based radiomics model accurately distinguishes moderate-severe from non-mild fibrosis in Crohn's bowel walls, significantly outperforming radiologists' visual assessments
	Diagnosis	Majtner et al[67]	P	Denmark	pan-CE	7744 images	DL	Auto-detects Crohn's ulcers with 98.4% accuracy. Comparable small/Large bowel accuracy (98.5% vs 98.1%). Distinguishes severity (κ = 0.72)
	Diagnosis	Klang et al[68]	R	Israel	CE	27892 images	CNN	DL model detects Crohn's enteric strictures at 93.5% accuracy (AUC 0.989), precisely distinguishing strictures from ulcers (including severe), enabling automated CE diagnosis
	Treatment	Konikoff et al[69]	R	Israel	APCT	101 patients	ML	Developed an ML model using indicators, such as NLR, to predict Crohn's complication risk in emergency CT (AUC 0.774), enabling risk stratification to reduce unnecessary scans
	Treatment	Con et al[70]	R	Australia	Serum biomarkers	146 patients	DL	RNN with serial biomarkers (AUC 0.754) outperforms logistic regression (AUC 0.659) in predicting biochemical remission (CRP < 5 mg/L) at 12 months post-anti-TNF therapy in Crohn's disease
	Prognosis	Ungaro et al[71]	P	United States, Canada	PEA	265 patients	ML	ML model identified 5 plasma protein markers for penetrating (B3) and 4 for stricturing (B2) complications, outperforming traditional models (B3 AUC 0.79)
	Prognosis	Stidham et al[72]	R	United States	Laboratory examination	2809 patients	ML	Machine learning leveraging routine longitudinal lab data predicts Crohn's surgical risk (AUC 0.78)
SINENs	Diagnosis	Kjellman et al[73]	P	Northern Europe	PET/CT, MRI etc.	278 patients	ML	Multi-plasma protein markers with Random Forest boost SI-NETs diagnosis (Sensitivity 89%, Specificity 91%, AUC 0.99)
	Diagnosis	Clift et al[74]	R	United Kingdom	EHR	382 patients	ML	XGBoost using primary care EHRs effectively identifies undiagnosed high-risk SI-NET patients (AUC 0.869)

R: Retrospective; P: Prospective; CTE: Computed tomography enterography; ML: Machine learning; pan-CE: Pan-enteric capsule endoscopy; APCT: Abdominopelvic computed tomography; RNN: Recurrent neural network; PEA: Proximity extension assay; HER: Electronic health records; DL: Deep learning; AUC: Area under curve; CE: Capsule endoscopy; CT: Computed tomography; CRP: C-reactive protein; TNF: Tumor necrosis factor; PET: Positron emission tomography; MRI: Magnetic resonance imaging; SI-NETs: Small intestine neuroendocrine tumours; SINENs: Small intestinal neuroendocrine neoplasms; NLR: Neutrophil-to-lymphocyte ratio.