Artificial intelligence in upper gastrointestinal bleeding: Can machine learning predict endotherapy requirements?

Table 3 Key studies evaluating artificial intelligence/machine learning models in upper gastrointestinal bleeding

Ref.	Design/setting	Dataset size	Population/data type	Predictors/inputs	Algorithm/model	Comparator (if any)	Performance metrics (AUROC/sensitivity/specificity /PPV/NPV)	Validation type	Major limitations
Shung et al[6], 2020	Retrospective, multicentre, external validation	1958 patients (training + validation)	Acute UGIB (clinical + lab data)	Vital signs, Hb, BUN, comorbidities, transfusion need	Gradient boosting machine (XGBoost)	GBS, AIMS65	AUROC 0.90 vs 0.87 (GBS); sensitivity 100%, specificity 26%	External (prospective cohort)	Limited ethnic diversity; no imaging variables
Raghareutai et al[31], 2025	Retrospective review of a prospectively collected dataset	1389 cases	Non-variceal UGIB; demographic, clinical + lab data	Age, vitals, Hb, BUN, SRH	Linear discriminant analysis	Rockall, GBS	AUROC 0.74 (0.81 in validation test set)	Internal + temporal validation	Small sample; limited external testing; static variables
Nazarian et al[11], 2024	Multicentre retrospective cohort	970 patients	AUGIB: Need for hemostatic therapy	Clinical + lab + endoscopic features	Random forest classifier	GBS, Rockall	AUROC 0.84 vs 0.72 in GBS	Five-fold cross-validation	Retrospective bias; heterogeneous image quality
He et al[20], 2024	Multicenter prospective; external validation	3868 images from 1200 patients	Peptic ulcer bleed; Forrest classification	Endoscopic images	Deep CNN	Endoscopist	Accuracy 91.2%; AUC 0.80 in validation data set	External validation	Limited to image data; no clinical integration
Yen et al[12], 2021	Retrospective single-centre image analysis	2738 images (2289 train, 449 test)	Peptic ulcer bleed	Endoscopic image features	MobileNetV2 (CNN)	Human endoscopist	AUROC 0.91 vs 0.80 (human); sensitivity 94%; specificity 92% (3 class category)	Internal (hold-out)	Retrospective design; limited generalisability
Boros et al[8], 2025	Retrospective EHR registry (Hungarian GI bleed registry)	1021 records	GI bleed (mixed aetiology)	Demographics, labs, vitals, comorbidities	XGBoost/CatBoost models	GBS, Rockall	AUROC 0.79 in GIBleed, AUC 0.84 in mortality	5-fold cross-validation	Retrospective; national registry bias
Bai et al[14], 2025	Multicenter prospective; international; internal	2467 cirrhotic patients with UGIB	Cirrhosis with acute UGIB	Clinical + lab parameters	LS-SVMR (least-squares SVM regression)	Logistic regression model	AUROC 0.986	External (prospective)	Limited to the cirrhotic population
Levi et al[16], 2021	Retrospective ICU database (MIMIC-III + eICU-CRD)	Approximately 6000 ICU admissions	GI bleeding patients in the ICU	Demographic + vital + lab series	LSTM neural network	Logistic regression	AUROC > 0.80 in the MIMIC-III data set	Cross-dataset validation	Limited prospective validation; EHR data noise

AUROC: Area under the receiver operating characteristic curves; PPV: Positive predictive value; NPV: Negative predictive value; UGIB: Upper gastrointestinal bleed; Hb: Hemoglobin; BUN: Blood urea nitrogen; XGBoost: Extreme gradient boosting; GBS: Glasgow-Blatchford score; AIMS65: Albumin, international normalized ratio, mental status, systolic blood pressure, age > 65 years; SRH: Stigmata of recent haemorrhage; AUGIB: Acute upper gastrointestinal bleed; CNN: Convolutional neural networks; AUC: Area under the curve; EHR: Electronic health record; GI: Gastrointestinal; CatBoost: Categorical boosting; LS-SVMR: Least square support vector machine regression; SVM: Support vector machine; ICU: Intensive care unit; MIMIC-III: Medical Information Mart for Intensive Care; eICU-CRD: EICU Collaborative Research Database; LSTM: Long Short-Term Memory.