Artificial intelligence in gastroenterology practice: Current perspectives and emerging prospects

Table 5 Studies evaluating applications of artificial intelligence in pancreatic diseases

Ref.	Clinical application	Data type	AI method	Sample size	Study design, center design, and validation design	Major performance metric	Principal limitation
Mukherjee et al[82]	PDAC prediction	Prediagnostic CT scans	SVM	420 CT scans, 155 prediagnostic, 265 controls	Retrospective case-control, internal test set, independent internal and external NIH sets, internal and external validation	AUC 0.98 (95%CI: 0.94-0.98), accuracy 92.2% (internal validation), specificity 96.2% (external validation), outperformed radiologists (mean AUC 0.66)	Retrospective design, small external validation cohorts
Mukherjee et al[83]	PDAC prediction	Standard-of-care CT scans	Ensemble with logistic regression, random forest, and XGBoost	1462 scans, 969 training and 493 test	Retrospective, multicenter, independent test set and external specificity validation cohorts	AUC 0.82 (95%CI: 0.81-0.83), sensitivity 73.0%, specificity 81.1% (internal validation), 81.3% specificity (external validation)	Retrospective design, prospective validation in high-risk cohorts is required
Alves et al[84]	PDAC detection	Routine CT scans	DL	3440 patients, 2224 for training, 86 for tuning, 1130 for sequestered test, multi-reader substudy of 391 cases assessed by 68 radiologists	International multicenter observational study, sequestered testing cohort and radiologist subset	Mean AUROC 0.92 (95%CI: 0.90-0.93) (external validation), AI performance was statistically superior to the 68-radiologist comparison (P < 0.001)	Observational design, results represent average radiologist performance
Placido et al[85]	Pancreatic cancer prediction	Health records with ICD codes	Transformer and GRU	9.2 million patients from DNPR and US-VA	Retrospective cohort, random 10% test split and cross-application between health systems, internal and cross-validation	AUROC 0.88 in Denmark and 0.78 in US-VA (internal validation), Danish model on US-VA dataset yielded AUROC 0.71	Performance declined when applied across different healthcare systems
Kuwahara et al[88]	IPMN classification	EUS digital still images	DL with ResNet50	50 patients, 3970 images	Retrospective single-center, 10-fold cross-validation	Accuracy 94.0%, AUC 0.98, diagnostic accuracy 94.0% vs 56.0% for human preoperative diagnosis	Small sample size, retrospective design, only surgical cases included
Kurita et al[89]	Pancreatic cystic lesion classification	Cyst fluid markers and clinical data	DL with TensorFlow	85 patients	Retrospective, single-center, observational study, 5-fold cross-validation	AUROC 0.966, accuracy 92.9%, AI outperformed cyst-fluid carcinoembryonic antigen (AUROC 0.719) and cytology (AUROC 0.739)	Small sample size, single-center study, subjective input factors
Machicado et al[90]	IPMN classification	nCLE video frames	CNN	35 patients, 15027 frames	Post hoc analysis of a prospective multicenter cohort, performance on post hoc video data, internal validation	Accuracy 82.9%-85.7%, sensitivity 83.3%	Small patient cohort, limited to pathologically confirmed cases
Yuan et al[91]	Pancreatic cystic neoplasm malignancy prediction	Contrast-enhanced CT scans	Mamba U-Net and XGBoost	1835 patients, 1249 primary cohort (999 training, 250 validation), external test sets of 173, 289 and 124 patients	Multicenter retrospective and prospective study, external test sets and prospective real-world cohort, internal and external validation	AUC 0.845-0.988 (external validation), malignancy detection 81.8% (prospective validation), interpretation time reduced 23.7%, and workload reduced 39.3%	Focused on intralesional features, did not incorporate peripancreatic findings
Duh et al[92]	Pancreatic cystic lesion classification	Abdominal CT scans	Attention Gate U-Net	335 abdominal CT scans, 297 pancreatic cystic lesions and 38 controls, training 241 scans/93 patients, independent test 94 scans/43 patients	Retrospective single-center, independent testing set of 94 scans, internal validation	Sensitivity 93.1%, specificity 81.8%	Single-center design, small control cohort, lacks external validation

AI: Artificial intelligence; PDAC: Pancreatic ductal adenocarcinoma; CT: Computed tomography; SVM: Support vector machine; NIH: National Institutes of Health; AUC: Area under the curve; CI: Confidence interval; XGBoost: Extreme gradient boosting; DL: Deep learning; AUROC: Area under the receiver operating characteristic curve; ICD: International Classification of Diseases; GRU: Gated recurrent unit; DNPR: Danish National Patient Registry; US-VA: United States Veterans Affairs; IPMN: Intraductal papillary mucinous neoplasm; EUS: Endoscopic ultrasonography; nCLE: Needle-based confocal laser endomicroscopy; CNN: Convolutional neural network; ResNet: Residual network.