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

Table 1 Machine learning models for the prediction of gastrointestinal bleed

Supervised machine learning
Classification task	Comment	Regression task	Comment
K-nearest neighbours	Data classification as per k-nearest neighbours, non-parametric	Gradient boosting model	A combination of weaker models (e.g., a decision tree) to create a stronger prediction model
		XGBoost
		LightGBM
		CatBoost
Neural network	Deep learning method composed of interconnected layers of artificial neurons	Support vector machine	Technique to categorise data points by finding an optimal hyperplane
ANN
CNN
Decision tree	An arranged tree in which internal nodes are attributes, branches are decisions, and leaves are outcomes or labels	Regression analysis	Useful for predicting time-to-event outcomes, including covariates and event times, such as bleeding recurrence

XGBoost: Extreme gradient boosting; LightGBM: Light gradient boosting machine; CatBoost: Categorical boosting; ANN: Artificial neural network; CNN: Convolutional neural network.

Table 2 Evidence and efficacy of use of artificial intelligence in gastrointestinal bleed

Ref.	Primary focus	Data type	Study type	Study metrics	Key AI/ML method(s)	Comment
Raghareutai et al[31], 2025	Pre-endoscopy risk stratification	Demograph, clinical and lab values, n = 1389	Retrospective review of prospectively collected data, internal validation	AUROC 0.74 to predict endoscopic intervention (0.81 in validation test set)	linear discriminant analysis	Identify patients of AUGIB who need endoscopic intervention. Dynamic changes need multiple inputs of data
Shung et al[6], 2020	Pre-endoscopy risk stratification	Clinical and lab data, n = 1958	Retrospective, multicentre, external validation	ML vs GBS; AUROC 0.90 vs 0.87 (external validation) sensitivity 100%, specificity 26%	Gradient boosting machines (XGBoost)	The ML model could identify low-risk patients who can be safely discharged
He et al[20], 2024	Real-time endoscopic prediction	Endoscopic images training data n = 3868, internal; validation data, n = 834; external validation data, n = 521	Multicenter prospective; external validation	AUC 0.80 in the validation data set, accuracy of 91.2%	DCNN (image-based Forrest)	The DCNN system showed more accurate and stable diagnostic performance than endoscopists in the prospective clinical comparison test
Bai et al[14], 2025	Post-GI bleed mortality in cirrhosis	Clinical and lab data, n = 2467 cirrhotics	Multicenter prospective, international internal validation, no external validation	AUC of 0.789 (up to 0.986 in LS-SVMR model)	LS-SVMR (least squares support vector machine regression)	CAGIB score is similar to CTP, MELD and MELD-Na for the prediction of mortality. No comparison with AIMS65, Rockall and GBS
Boros et al[8], 2025	Post-GI bleed mortality	EHR registry (Hungarian GI bleed registry), n = 1021	Retrospective, fivefold cross-validation	XGBoost and CatBoost AUC 0.79 vs 0.62 of GBS in GIB	XGBoost and CatBoost	CatBoost reached a sensitivity of 78% and a specificity of 74%

AUROC: Area under the receiver operating characteristic curves; AUGIB: Acute upper gastrointestinal bleed; ML: Machine learning; GBS: Glasgow-Blatchford score; XGBoost: Extreme gradient boosting; AUC: Area under the curve; DCNN: Deep convolutional neural networks; GI: Gastrointestinal; LS-SVMR: Least square support vector machine regression; CAGIB: Cirrhosis acute gastrointestinal bleeding; CTP: Child-Turcotte-Pugh; MELD: Model for end-stage liver disease; MELD-Na: Model for end-stage liver disease-sodium; AIMS65: Albumin, international normalized ratio, mental status, systolic blood pressure, age > 65 years; EHR: Electronic health record; CatBoost: Categorical boosting.

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.