Artificial intelligence in pancreatitis: A narrative review on advancing precision diagnosis, prognosis, and therapeutic strategies

Table 1 Application of high-accuracy artificial intelligence models in the diagnosis and prognosis management of pancreatitis

Application	Prediction target	AI methodology	Patient cohort	Input data type	Performance metrics	Stage of clinical translation	Ref.
Diagnosis	AP early diagnosis and risk stratification	XGBoost model	61894	Vital signs and lab measurements	AUC: 0.921	Experimental	[47]
		Logistic regression model	275	28 important radiomics features, lipase levels	AUC: 0.933	Experimental	[32]
	Pancreatic lesions detection and segmentation	V-Net model	144	Pancreatic volume. The split volume ratios	AUC: 0.854	Experimental	[31]
		SegFormer model	165	EUS images	AUC: 0.95	Validation phase	[41]
	Pediatric pancreas segmentation	PanSegNet	84	Imaging parameters, biologic sex, age at imaging, clinical diagnosis	Dice coefficient: Approximately 0.88	Validation phase	[37]
	Non-invasive diagnosis of PDAC and CP	DLR model	558	CEUS images	Training cohort AUC: 0.986. Internal validation cohort AUC: 0.978. External validation cohorts 1 AUC: 0.967. External validation cohorts 2 AUC: 0.953	Validation phase	[39]
			491	EUS images	AUC: 0.936	Validation phase	[40]
	Distinguish AIP from PDAC	SVM-RFE model	111	251 expert-designed features from 2D and 3D PET/CT images	AUC: 0.93 ± 0.01	Validation phase	[44]
		Random forest model	182	431 radiomics features. Two types of CT parameters with dual phase CT	AUC: 0.975	Validation phase	[45]
	AP severity early prediction	LightGBM model	215	Radiomics features	Training cohort AUC: 0.992. Validation cohort AUC: 0.965. Test cohort AUC: 0.894	Hospital IT integration	[33,64]
		CTA model	192	Age, gender, BMI, temperature, pulse, systolic blood pressure, WBC, CRP, albumin, calcium, APACHE II and BISAP, Balthazar grade, CTSI, MCTSI, EPIC score	Training cohort AUC: 0.853. Validation cohort AUC: 0.833	Validation phase	[51]
		Random forest model	648	Blood urea nitrogen, serum creatinine, albumin, HDL, LDL, calcium, glucose	Training cohort AUC: 0.89. Test cohort AUC: 0.96	Validation phase	[48]
			740	Age, gender, BMI, comorbidities, serum biochemical index, CT	Training cohort AUC: 0.969. Training cohort AUC: 0.961	Validation phase	[52]
Prognosis management	Mortality	XGBoost model	499	WBC count, hemoglobin, platelet count, serum creatinine, albumin	AUC: 0.881	Hospital IT integration	[61,64]
		ANNs model	337	Total bilirubin, creatinine, amylase, lipase, LDH	AUC: 0.769	Experimental	[72]
	Multiple organ failure	ANNs model	312	Age, hematocrit, serum glucose, BUN, serum calcium	AUC: 0.96 ± 0.02	Experimental	[62]
		SVM model	263	HCT, K-time, IL-6, creatinine	AUC: 0.840	Validation phase	[69]
	Complications	ANNs model	217	Pancreatic necrosis rate, LDH, oxyhemoglobin saturation	AUC: 0.859 ± 0.048	Experimental	[66]
		XGBoost model	334	APACHE II, IAP, PCT rank	AUC: 0.9193	Experimental	[67]
		GBDT model	1672	Age, vasopressors, mechanical ventilation, GCS	AUC: 0.985	Validation phase	[70]
	Recurrence	XGBoost model	531	TG levels, smoking, drinking, ANC	AUC: 0.779	Experimental	[79]
		CECT model	389	Age, etiology, CTSI, hospital stay, pancreatic necrosis	AUC: 0.941	Experimental	[76]

The key information of 22 representative artificial intelligence model studies with high accuracy in pancreatitis diagnosis and prognosis, including prediction target, patient population, artificial intelligence methodology, input data type, performance indicator (e.g., area under the curve), and clinical translation stage. AI: Artificial intelligence; AP: Acute pancreatitis; AUC: Area under the curve; EUS: Endoscopic ultrasound; PDAC: Pancreatic ductal adenocarcinoma; CP: Chronic pancreatitis; DLR: Deep learning radiomics; CEUS: Contrast-enhanced ultrasound; AIP: Autoimmune pancreatitis; SVM-RFE: Support vector machine with recursive feature elimination; 2D: Two-dimensional; 3D: Three-dimensional; PET: Positron emission tomography; CT: Computed tomography; LightGBM: Light gradient boosting machine; CTA: Computed tomography angiography; BMI: Body mass index; WBC: White blood cell; CRP: C-reactive protein; APACHE: Accuracy of Acute Physiology and Chronic Health Evaluation; BISAP: Bedside Index of Severity in Acute Pancreatitis; CTSI: Computed tomography severity index; MCTSI: Modified computed tomography severity index; EPIC: Early prognostic imaging classification; HDL: High-density lipoprotein; LDL: Low-density lipoprotein; BP: Back propagation; LDH: Lactate dehydrogenase; ANNs: Artificial neural networks; BUN: Blood urea nitrogen; HCT: Hematocrit; IL-6: Interleukin-6; IAP: Intra-abdominal pressure; PCT: Procalcitonin; GBDT: Gradient boosting decision tree; GCS: Glasgow Coma Scale; TG: Triglycerides; ANC: Absolute neutrophil count; CECT: Contrast-enhanced computed tomography.