Gastroenterology

AI at the Forefront: Navigating Oncologic Care for Six Gastrointestinal Cancers According to the NCCN Guidelines Utilizing Gemini-1.0 Ultra and ChatGPT-4.

BACKGROUND AND OBJECTIVES: We explored the ability of large language models (LLMs) ChatGPT-4 and Gemini 1.0 Ultra in guiding clinical decision-making for six gastrointestinal cancers using the National Comprehensive Cancer Network (NCCN) Clinical Practice Guidelines.

Artificial intelligence in imaging diagnosis of liver tumors: current status and future prospects.

Liver cancer remains a significant global health concern, ranking as the sixth most common malignancy and the third leading cause of cancer-related deaths worldwide. Medical imaging plays a vital role in managing liver tumors, particularly hepatocellular carcinoma (HCC) and metastatic lesions. However, the large volume and complexity of imaging data can make accurate and efficient interpretation c...

Development and validation of an AI-driven radiomics model using non-enhanced CT for automated severity grading in chronic pancreatitis.

OBJECTIVE: To develop and validate the chronic pancreatitis CT severity model (CATS), an artificial intelligence (AI)-based tool leveraging automated ...

Enhancing non-invasive early detection of gastric cancer: a critical reflection on exosome ncRNA and machine learning.

Comprehensive Bibliometric Analysis of Prediction Models for HCC: Current Trends and Future Prospects.

BACKGROUND: Hepatocellular carcinoma (HCC) is the most common primary malignant liver tumor, with rising incidence and mortality rates posing a signif...

Demarcating the Development of Cirrhosis and Its Complications via Plasma Proteomic Features in an Asymptomatic Population.

The early detection of liver cirrhosis and its complications is a conundrum in clinical practice. We aim to address this conundrum using proteomic fea...

Polarisation Sensitive Optical Coherence Tomography Image Characteristics for Gastrointestinal Tumours and Normal Tissues at Surgical Margins in Dogs.

The treatment of choice for canine alimentary tract neoplasms is surgical excision, but it can sometimes be difficult to achieve wide margins due to n...

A Systematic Review of the Clinical Impact of Implementing Artificial Intelligence in Upper Aerodigestive Tract Endoscopy.

BACKGROUND: Endoscopy is essential in upper aerodigestive tract (UADT) examination, particularly in the early detection of laryngopharyngeal lesions. ...

Comparison of publicly available artificial intelligence models for pancreatic segmentation on T1-weighted Dixon images.

PURPOSE: This study aimed to compare three publicly available deep learning models (TotalSegmentator, TotalVibeSegmentator, and PanSegNet) for automat...

Study on the quantitative analysis of Tilianin based on Raman spectroscopy combined with deep learning.

Tilianin is a commonly used pharmaceutical ingredient with various biological activities such as antioxidant, anti-inflammatory, and anticancer, which...

A Novel Switching of Artificial Intelligence to Generate Simultaneously Multimodal Images to Assess Inflammation and Predict Outcomes in Ulcerative Colitis-(With Video).

OBJECTIVES: Virtual Chromoendoscopy (VCE) is pivotal for assessing activity and predicting outcomes in Ulcerative Colitis (UC), though interobserver v...

Association Between Automated Coronary Artery Calcium From Routine Chest Computed Tomography Scans and Cardiovascular Risk in Patients With Colorectal or Gastric Cancer.

BACKGROUND: As cardiovascular disease (CVD) is the leading cause of noncancer mortality in colorectal or gastric cancer patients, it is essential to i...

Deep Learning Methodology for Quantification of Normal Pancreas Structures.

Histopathologic evaluation plays a crucial role in assessing morphological tissue alterations in disease models and toxicology studies. Identifying sm...

Artificial intelligence (AI) and CT in abdominal imaging: image reconstruction and beyond.

Computed tomography (CT) is a cornerstone of abdominal imaging, playing a vital role in accurate diagnosis, appropriate treatment planning, and diseas...

Predicting mucosal healing in Crohn's disease: development of a deep-learning model based on intestinal ultrasound images.

OBJECTIVE: Predicting treatment response in Crohn's disease (CD) is essential for making an optimal therapeutic regimen, but relevant models are lacki...

Development and validation of machine learning models for distant metastasis of primary hepatic carcinoma: a population-based study.

BACKGROUND: Primary liver cancer is the sixth most common cancer globally and ranks third in cancer-related mortality. Patients with distant metastasi...

Computational theranostics strategy for pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is a formidable challenge in modern medicine, characterized by its insidious progression, early systemic metas...

Unraveling the power of radiomics: prediction and exploration of lymph node metastasis in stage T1/2 esophageal squamous cell carcinoma.

Accurate assessment of lymph node metastasis (LNM) in T1/2-stage esophageal squamous cell carcinoma (ESCC) is critical for treatment planning but rema...

The relevance of cardiac and gastric interoception for disordered eating behavior.

BACKGROUND: Gastric interoception (i.e., the perception of gastrointestinal signals such as hunger, satiety or nausea) in the context of eating has re...

Microbiome multi-omics analysis reveals novel biomarkers and mechanisms linked with CD etiopathology.

BACKGROUND: The gut microbiome plays a key role in the development of inflammatory bowel disease (IBD), as imbalances in microbial composition are ass...