Gastroenterology

Artificial Intelligence-Powered Insights into Polyclonality and Tumor Evolution.

Recent studies have revealed that polyclonality-where multiple distinct subclones cooperate during early tumor development-is a critical feature of tumor evolution, as demonstrated by Sadien et al. and Lu et al. in (October 2024). These findings show that early polyclonal interactions can overcome fitness barriers, ultimately transitioning to monoclonality as dominant clones emerge. Understanding...

Natural orifice transluminal endoscopic surgery: history and current development.

Natural orifice transluminal endoscopic surgery (NOTES) represents an innovative advancement in minimally invasive surgery, utilizing natural body orifices to access the peritoneal cavity to minimize surgical trauma, reduce postoperative pain, and avoid visible scars. Since its inception, NOTES has faced challenges such as technical complexity and securing safe access closure, which initially limi...

Therapeutic horizons in metabolic dysfunction-associated steatohepatitis.

Metabolic dysfunction-associated steatohepatitis (MASH), the progressive inflammatory form of MASLD, is now a leading cause of chronic liver disease w...

Harnessing artificial intelligence for detection of pancreatic cancer: a machine learning approach.

PURPOSE: Pancreatic cancer (PC) is one of the most lethal malignancies, often presenting with nonspecific symptoms and a dismal prognosis. Despite adv...

Machine learning based obesity and aging related signature for predicting the prognosis and immunotherapy benefit in stomach adenocarcinoma.

BACKGROUND: Stomach adenocarcinoma (STAD) is one of most common cancers with high invasiveness and poor prognosis. Obesity and aging are correlated wi...

A novel artificial intelligence-based system for quality monitoring during esophagogastroduodenoscopy: a multicenter randomized controlled study.

Esophagogastroduodenoscopy (EGD) is the pivotal procedure for diagnosis of upper gastrointestinal (UGI) lesions. However, significant variation in EGD...

Enhancing ultrasonographic detection of hepatocellular carcinoma with artificial intelligence: current applications, challenges and future directions.

BACKGROUND: Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality worldwide, with early detection playing a crucial role ...

Unveiling the mechanisms and promising molecular targets of curcumin in pancreatic cancer through multi-dimensional data.

Pancreatic cancer (PC) is a highly aggressive and fatal malignancy, primarily affecting older males. Curcumin, a potential anti-cancer agent, has been...

Impact of tertiary lymphoid structure-associated biomarkers on pancreatic cancer via a dual-disease analysis of psoriasis and pancreatic cancer.

Pancreatic cancer (PC), often referred to as the "king of cancers", has demonstrated limited success with immunotherapies, many of which are still und...

Deep learning model for grading carcinoma with Gini-based feature selection and linear production-inspired feature fusion.

The most common types of kidneys and liver cancer are renal cell carcinoma (RCC) and hepatic cell carcinoma (HCC), respectively. Accurate grading of t...

A novel approach to overcome black box of AI for optical diagnosis in colonoscopy.

Accurate real-time optical diagnosis that distinguishes neoplastic from non-neoplastic colorectal lesions during colonoscopy can lower the costs of pa...

Deep learning for smartphone-aided detection system of Helicobacter Pylori in gastric biopsy.

Helicobacter pylori (HP) have chronically infected more than half of the world's population and is a cause of chronic gastritis, peptic ulcers and gas...

Machine learning developed LKB1-AMPK signaling related signature for prognosis and drug sensitivity in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is one of the most common tumors worldwide, posing a significant threat to the life and health of people globally. LKB1...

Modeling the prediction of spontaneous rupture and bleeding in hepatocellular carcinoma via machine learning algorithms.

This study aimed to identify the risk factors associated with spontaneous rupture and bleeding in hepatocellular carcinoma, establish a prediction mod...

Consensus artificial intelligence-driven prognostic signature for predicting the prognosis of hepatocellular carcinoma: a multi-center and large-scale study.

Hepatocellular carcinoma (HCC), a leading cause of global cancer mortality, requires molecular stratification to advance precision oncology. This stud...

Radiomics analysis based on dynamic contrast-enhanced MRI for predicting early recurrence after hepatectomy in hepatocellular carcinoma patients.

This study aimed to develop a machine learning model based on Magnetic Resonance Imaging (MRI) radiomics for predicting early recurrence after curativ...

Comparison of AI chatbot predicted and realworld survival outcomes in hepatocellular carcinoma.

This study compares survival predictions made by an artificial intelligence (AI) based chatbot with real-world data in hepatocellular carcinoma (HCC) ...

CFM-UNet: coupling local and global feature extraction networks for medical image segmentation.

In medical image segmentation, traditional CNN-based models excel at extracting local features but have limitations in capturing global features. Conv...

A machine learning-based framework for predicting metabolic syndrome using serum liver function tests and high-sensitivity C-reactive protein.

Metabolic Syndrome (MetS) comprises a clustering of conditions that significantly increase the risk of heart disease, stroke, and diabetes. Timely det...

A hybrid XAI-driven deep learning framework for robust GI tract disease diagnosis.

The stomach is one of the main digestive organs in the GIT, essential for digestion and nutrient absorption. However, various gastrointestinal disease...