ArticleJournal of digital imaging2023
Automated Multimodal Machine Learning for Esophageal Variceal Bleeding Prediction Based on Endoscopy and Structured Data.
Article in Journal of digital imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it, 38 citations in OpenAlex.
- Machine Learning in Predicting the Risk of Esophagogastric Variceal Bleeding Among Patients With Liver Cirrhosis: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Machine learning-based CT radiomics for identifying high risk esophageal varices of bleeding in hepatocellular carcinoma.Abdominal radiology (New York) · 2026Article
- Artificial Intelligence in Gastrointestinal Endoscopy and Hemostatic Decision-Making: Current Evidence, Clinical Implications and Implementation Barriers.Life (Basel, Switzerland) · 2026Review
- Development and prospective validation of a machine learning model to predict mortality in cirrhosis with esophageal variceal bleeding.World journal of hepatology · 2026Article
- Nutritional indicator-based prediction model for esophagogastric variceal bleeding in cirrhosis using XGBoost and SHAP.Frontiers in nutrition · 2026Article
- Multimodal prediction models integrating radiomics and three-dimensional deep learning for acute respiratory distress syndrome in acute pancreatitis patients.The Journal of international medical research · 2026Article
- Artificial Intelligence in Patient Blood Management: A Systematic Review of Predictive, Diagnostic, and Decision Support Applications.Journal of clinical medicine · 2025Review
- Development and validation of machine learning models to predict esophagogastric variceal rebleeding risk in HBV-related cirrhosis after endoscopic treatment: a prospective multicenter study.EClinicalMedicine · 2025Article
- Explainable ResNet-long short-term memory model for the classification of bowel sounds frequency based on multifeature fusion.The Journal of international medical research · 2025Article
- Multimodal predictive model for strangulation risk in adhesive small bowel obstruction using deep learning and electronic health record data.The Journal of international medical research · 2025Observational
- Features on contrast-enhanced computed tomography for predicting recurrent esophageal variceal bleeding in patients with hepatocellular carcinoma.BMC gastroenterology · 2025Article
- Explainable semi-supervised model for predicting invasion depth of esophageal squamous cell carcinoma based on the IPCL and AVA patterns.Scientific reports · 2025Article
- Predictive modeling for early detection of refractory esophageal stricture following esophageal atresia surgery: insight from a machine learning study.Pediatric surgery international · 2025Article
- Recent advances in machine learning for precision diagnosis and treatment of esophageal disorders.World journal of gastroenterology · 2025Review
- Visualizing global progress and challenges in esophagogastric variceal bleeding.World journal of gastrointestinal surgery · 2025Article
- Noninvasive prediction of esophagogastric varices in hepatitis B: An extreme gradient boosting model based on ultrasound and serology.World journal of gastroenterology · 2025Article
- Comparison of Deep Learning and Traditional Machine Learning Models for Predicting Mild Cognitive Impairment Using Plasma Proteomic Biomarkers.International journal of molecular sciences · 2025Article
- Esophageal varices detection and bleeding risk assessment with artificial intelligence: a systematic review.iGIE : innovation, investigation and insights · 2024Article
- A Semi-Supervised Learning Framework for Classifying Colorectal Neoplasia Based on the NICE Classification.Journal of imaging informatics in medicine · 2024Article
- Applications of Artificial Intelligence-Based Systems in the Management of Esophageal Varices.Journal of personalized medicine · 2024Review
Corrections and comments
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Authors and funding
13 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Esophageal variceal (EV) bleeding is a severe medical emergency related to cirrhosis. Early identification of cirrhotic patients with at a high risk of EV bleeding is key to improving outcomes and optimizing medical resources. This study aimed to evaluate the feasibility of automated multimodal machine learning (MMML) for predicting EV bleeding by integrating endoscopic images and clinical structured data. This study mainly includes three steps: step 1, developing deep learning (DL) models using EV images by 12-month bleeding on TensorFlow (backbones include ResNet, Xception, EfficientNet, ViT and ConvMixer); step 2, training and internally validating MMML models integrating clinical structured data and DL model outputs to predict 12-month EV bleeding on an H2O-automated machine learning platform (algorithms include DL, XGBoost, GLM, GBM, RF, and stacking); and step 3, externally testing MMML models. Furthermore, existing clinical indices, e.g., the MELD score, Child‒Pugh score, APRI, and FIB-4, were also examined. Five DL models were transfer learning to the binary classification of EV endoscopic images at admission based on the occurrence or absence of bleeding events during the 12-month follow-up. An EfficientNet model achieved the highest accuracy of 0.868 in the validation set. Then, a series of MMML models, integrating clinical structured data and the output of the EfficientNet model, were automatedly trained to predict 12-month EV bleeding. A stacking model showed the highest accuracy (0.932), sensitivity (0.952), and F1-score (0.879) in the test dataset, which was also better than the existing indices. This study is the first to evaluate the feasibility of automated MMML in predicting 12-month EV bleeding based on endoscopic images and clinical variables.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.