Advancements in the diagnosis of biliopancreatic diseases: A comparative review and study on future insights

Table 3 AI-Assisted based prediction models for magnetic resonance imaging models

Clinical data availability	AI algorithm	Equipment	Reference standard	Outcome measured	AUC	Ref.
With clinical data	SVM	1.5 T GE	Surgical resection	ALN metastasis	0.87
	SVM	3.0 T GE	Surgical resection	ALN metastasis	0.810
	RF	N/A	Surgical resection	ALN metastasis	0.91
Without or with insufficient clinical data	LDA, RF, NB, KNN, SVM	3.0 T Siemens	FNA or surgical resection	ALN metastasis	0.82
	SVM, KNN, and LDA	3.0 T Siemens	FNA or surgical resection	ALN metastasis	0.8615
	LDA	1.5 T Aurora	Surgical resection	ALN metastasis	0.812
	SVM, XGBoost	3.0 T GE	Surgical resection	ALN metastasis	0.83
	SVM	1.5 T Philips	Surgical resection	SLN metastasis	0.852
	CNN	1.5 T GE	18FDG-PET	ALN metastasis	0.91
	RF	1.5 T Philips	Surgical resection	SLN metastasis	0.868
	Lasso regression	1.5 T Siemens	Surgical resection	ALN metastatic burden	0.81

AI: Artificial intelligence; ALN: Axillary lymph node; ANN: Artificial neural network; AUC: Area under the curve; CNN: Convolutional neural network; DA: Deformable attention; DCNNs: Deep convolutional neural networks; DT: Decision tree; FNA: Fine-needle aspiration; KNN: k-nearest neighbors; LDA: Linear discriminant analysis; NB: Naïve Bayes; RF: Random forest; SLN: Sentinel lymph node; SVM: Support vector machine; 18FDG-PET: Fluorodeoxyglucose positron emission tomography.