Gastroenterology

Research status and progress of deep learning in automatic esophageal cancer detection.

Esophageal cancer (EC), a common malignant tumor of the digestive tract, requires early diagnosis and timely treatment to improve patient prognosis. Automated detection of EC using medical imaging has the potential to increase screening efficiency and diagnostic accuracy, thereby significantly improving long-term survival rates and the quality of life of patients. Recent advances in deep learning ...

Prediction of Ki-67 expression in hepatocellular carcinoma with machine learning models based on intratumoral and peritumoral radiomic features.

BACKGROUND: Hepatocellular carcinoma (HCC) is one of the most common malignant tumours of the digestive system worldwide. The expression of Ki-67 is crucial for the diagnosis, treatment, and prognostic evaluation of HCC.

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

BACKGROUND: Gastric cancer (GC) has a poor prognosis, and the accurate prediction of patient survival remains a significant challenge in oncology. Mac...

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 important for decision making regarding patient trea...

Automating Performance Status Annotation in Oncology Using Llama-3.

This work explores the automated extraction of medical information from Dutch clinical notes using Llama-3 and a limited amount of annotations. We com...

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 rising, making it crucial to predict mortality and...

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 proteins play a critical role in tumor progress and metas...

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

Objective: Ulcerative colitis (UC), characterized by chronic inflammation with alternating remission-relapse cycles, requires precise histological h...

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

Pre-training on image-text colonoscopy records offers substantial potential for improving endoscopic image analysis, but faces challenges including ...

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 if not identified early. Early assessment of abno...

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 psychological, physiological, and socioeconomic burdens ...

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 in the decision-making process of ovarian tumor tr...

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 intestinal resection in patients with inguinal incarcer...

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, driven by the goals of minimizing invasiveness, improvi...

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 mortality due to its progressive nature and associated...

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 result in muscle atrophy, polyneuropathy, increase...

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 morbidity and mortality rate. Early recognition of SAP is ...

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 associated fatty liver disease (MASLD), with an incre...

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. Recent studies have shown that lactylation modification...