Gastroenterology

Predicting gastric cancer survival using machine learning: A systematic review.

BACKGROUND: Gastric cancer (GC) has a poor prognosis, and the accurate prediction of patient surviva...

Patient Survival Prediction by Analyzing Pathological Images of Patients After Liver Transplantation.

Predicting whether a patient will develop cancer using nuclear features on pathological images is im...

Machine learning for predicting all-cause mortality of metabolic dysfunction-associated fatty liver disease: a longitudinal study based on NHANES.

BACKGROUND: The mortality burden of metabolic dysfunction-associated fatty liver disease (MAFLD) is ...

Machine learning-assisted washing-free detection of extracellular vesicles by target recycling amplification based fluorescent aptasensor for accurate diagnosis of gastric cancer.

Extracellular vesicles (EVs) are promising non-invasive biomarkers for cancer diagnosis. EVs protein...

VIGIL: Vision-Language Guided Multiple Instance Learning Framework for Ulcerative Colitis Histological Healing Prediction

Objective: Ulcerative colitis (UC), characterized by chronic inflammation with alternating remissi...

Endo-CLIP: Progressive Self-Supervised Pre-training on Raw Colonoscopy Records

Pre-training on image-text colonoscopy records offers substantial potential for improving endoscop...

Machine learning-based diagnostic model for neonatal intestinal diseases in multiple centres: a cross-sectional study protocol.

BACKGROUND: Neonatal intestinal diseases often have an insidious onset and can lead to poor outcomes...

Machine learning prediction model of prolonged delay to loop ileostomy closure after rectal cancer surgery: a retrospective study.

BACKGROUND: Delayed closure of a temporary ileostomy in patients with rectal cancer may cause psycho...

The utility of low-dose pre-operative CT of ovarian tumor with artificial intelligence iterative reconstruction for diagnosing peritoneal invasion, lymph node and hepatic metastasis.

PURPOSE: Diagnosis of peritoneal invasion, lymph node metastasis, and hepatic metastasis is crucial ...

SHapley Additive exPlanations (SHAP)-based, multivariate machine-learning techniques with external validation: Construction of a preoperative interpretable predictive model for intestinal resection of incarcerated inguinal hernia.

BACKGROUND: Currently, there are a lack of effective tools for preoperative risk assessment of intes...

Artificial Intelligence in Gastroenterology and Hepatology: Potential and Perils.

Current Trends and Future Directions in Lumbar Spine Surgery: A Review of Emerging Techniques and Evolving Management Paradigms.

: Lumbar spine surgery has undergone significant technological transformation in recent years, drive...

Innovative Strategies in the Diagnosis and Treatment of Liver Cirrhosis and Associated Syndromes.

Liver cirrhosis continues to be a major global health issue, contributing to high morbidity and mort...

Personalized Nutrition Strategies for Patients in the Intensive Care Unit: A Narrative Review on the Future of Critical Care Nutrition.

Critically ill patients in intensive care units (ICUs) are at high risk of malnutrition, which can ...

Constructing a prediction model for acute pancreatitis severity based on liquid neural network.

Acute pancreatitis (AP) is a common disease, and severe acute pancreatitis (SAP) has a high morbidit...

Identification of potential metabolic biomarkers and immune cell infiltration for metabolic associated steatohepatitis by bioinformatics analysis and machine learning.

BACKGROUND: Metabolic associated steatohepatitis (MASH) represents a severe subtype of metabolic ass...

Identifying Lactylation-related biomarkers and therapeutic drugs in ulcerative colitis: insights from machine learning and molecular docking.

BACKGROUND: Ulcerative colitis (UC), a chronic relapsing-remitting inflammatory bowel disease. Recen...

Generalizable Pancreas Segmentation via a Dual Self-Supervised Learning Framework

Recently, numerous pancreas segmentation methods have achieved promising performance on local sing...

Two-Stage Automatic Liver Classification System Based on Deep Learning Approach Using CT Images.

Alveolar echinococcosis (AE) is a parasitic disease caused by Echinococcus multilocularis, where ear...

Effect of Deep Learning-Based Image Reconstruction on Lesion Conspicuity of Liver Metastases in Pre- and Post-contrast Enhanced Computed Tomography.

The purpose of this study was to investigate the utility of deep learning image reconstruction at me...

Deep learning diagnosis of hepatic echinococcosis based on dual-modality plain CT and ultrasound images: a large-scale, multicenter, diagnostic study.

BACKGROUND: Given the current limited accuracy of imaging screening for Hepatic Echinococcosis (HCE)...