Gastroenterology

Online interpretable dynamic prediction models for clinically significant posthepatectomy liver failure based on machine learning algorithms: a retrospective cohort study.

BACKGROUND: Posthepatectomy liver failure (PHLF) is the leading cause of mortality in patients undergoing hepatectomy. However, practical models for accurately predicting the risk of PHLF are lacking. This study aimed to develop precise prediction models for clinically significant PHLF.

Deep Learning and Automatic Differentiation of Pancreatic Lesions in Endoscopic Ultrasound: A Transatlantic Study.

INTRODUCTION: Endoscopic ultrasound (EUS) allows for characterization and biopsy of pancreatic lesions. Pancreatic cystic neoplasms (PCN) include mucinous (M-PCN) and nonmucinous lesions (NM-PCN). Pancreatic ductal adenocarcinoma (P-DAC) is the commonest pancreatic solid lesion (PSL), followed by pancreatic neuroendocrine tumor (P-NET). Although EUS is preferred for pancreatic lesion evaluation, i...

Applying Deep-Learning Algorithm Interpreting Kidney, Ureter, and Bladder (KUB) X-Rays to Detect Colon Cancer.

Early screening is crucial in reducing the mortality of colorectal cancer (CRC). Current screening methods, including fecal occult blood tests (FOBT) ...

Is ChatGPT-4 a Reliable Tool in Autoimmune Hepatitis?

INTRODUCTION: Artificial intelligence-based chatbots offer a potential avenue for delivering personalized counseling to patients with autoimmune hepat...

Construction of a combined prognostic model for pancreatic ductal adenocarcinoma based on deep learning and digital pathology images.

BACKGROUND: Deep learning has made significant advancements in the field of digital pathology, and the integration of multiple models has further impr...

Diagnosis of Pancreatic Ductal Adenocarcinoma Using Deep Learning.

Recent advances in artificial intelligence (AI) research, particularly in image processing technologies, have shown promising applications across vari...

Analysis and validation of diagnostic biomarkers and immune cell infiltration characteristics in Crohn's disease by integrating bioinformatics and machine learning.

Crohn's disease (CD) presents significant diagnostic and therapeutic challenges due to its unclear etiology, frequent relapses, and limited treatment ...

Artificial intelligence-based automated determination in breast and colon cancer and distinction between atypical and typical mitosis using a cloud-based platform.

Artificial intelligence (AI) technology in pathology has been utilized in many areas and requires supervised machine learning. Notably, the annotation...

The role of aspirin in preventing gastrointestinal cancers.

Cancer remains an increasing global health issue and is projected to cause 50% of all global deaths by 2050. Gastrointestinal (GI) tract cancers curre...

Past, Present, and Future: A History Lesson in Artificial Intelligence.

Over the past 5 decades, artificial intelligence (AI) has evolved rapidly. Moving from basic models to advanced machine learning and deep learning sys...

Advancements in early detection of pancreatic cancer: the role of artificial intelligence and novel imaging techniques.

Early detection is crucial for improving survival rates of pancreatic ductal adenocarcinoma (PDA), yet current diagnostic methods can often fail at th...

Decision Fusion Model for Predicting Microvascular Invasion in Hepatocellular Carcinoma Based on Multi-MR Habitat Imaging and Machine-Learning Classifiers.

RATIONALE AND OBJECTIVES: Accurate prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is crucial for guiding treatment. This...

Predicting Portal Pressure Gradient in Patients with Decompensated Cirrhosis: A Non-invasive Deep Learning Model.

BACKGROUND: A high portal pressure gradient (PPG) is associated with an increased risk of failure to control esophagogastric variceal hemorrhage and r...

WISE: Efficient WSI selection for active learning in histopathology.

Deep neural network (DNN) models have been applied to a wide variety of medical image analysis tasks, often with the successful performance outcomes t...

Prediction of hepatocellular carcinoma response to radiation segmentectomy using an MRI-based machine learning approach.

PURPOSE: To evaluate the value of pre-treatment MRI-based radiomics in patients with hepatocellular carcinoma (HCC) for the prediction of response to ...

The use of artificial intelligence in colonoscopic evaluations.

PURPOSE OF REVIEW: This review aims to highlight the transformative impact of artificial intelligence in the field of gastrointestinal endoscopy, part...

Machine Learning Models for Predicting Significant Liver Fibrosis in Patients with Severe Obesity and Nonalcoholic Fatty Liver Disease.

PURPOSE: Although noninvasive tests can be used to predict liver fibrosis, their accuracy is limited for patients with severe obesity and nonalcoholic...

A novel endoscopic artificial intelligence system to assist in the diagnosis of autoimmune gastritis: a multicenter study.

BACKGROUND:  Autoimmune gastritis (AIG), distinct from Helicobacter pylori-associated atrophic gastritis (HpAG), is underdiagnosed due to limited awar...

Clinical evaluation of accelerated diffusion-weighted imaging of rectal cancer using a denoising neural network.

BACKGROUND: To evaluate the effectiveness of a deep learning denoising approach to accelerate diffusion-weighted imaging (DWI) and thus improve diagno...

Key genes and pathways in the molecular landscape of pancreatic ductal adenocarcinoma: A bioinformatics and machine learning study.

Pancreatic ductal adenocarcinoma (PDAC) is recognized for its aggressive nature, dismal prognosis, and a notably low five-year survival rate, undersco...