Gastroenterology

eNCApsulate: neural cellular automata for precision diagnosis on capsule endoscopes.

PURPOSE: Wireless capsule endoscopy (WCE) is a noninvasive imaging method for the entire gastrointestinal tract and is a pain-free alternative to traditional endoscopy. It generates extensive video data that requires significant review time, and localizing the capsule after ingestion is a challenge. Techniques like bleeding detection and depth estimation can help with localization of pathologies, ...

Gut microbiome alterations and hepatic encephalopathy post-TIPS in liver cirrhosis patients.

BACKGROUND: The transjugular intrahepatic portosystemic shunt (TIPS), a crucial tool for treating complications related to portal hypertension in patients with liver cirrhosis, is often associated with an increased risk of postoperative complications such as hepatic encephalopathy. Accurate preoperative prediction of the risk of developing hepatic encephalopathy is critical for optimizing the rati...

Multi-omics integration and machine learning uncover molecular basal-like subtype of pancreatic cancer and implicate A2ML1 in promoting tumor epithelial-mesenchymal transition.

BACKGROUND: Pancreatic cancer (PC) represents a highly heterogeneous malignancy with poor prognosis, where precise molecular subtyping facilitates com...

Beam orientation optimization in IMRT using sparse mixed integer programming and non-convex fluence map optimization.

Beam orientation optimization (BOO) in intensity-modulated radiation therapy (IMRT) is a complex, non-convex problem traditionally addressed with heur...

Selective identification of polyploid hepatocellular carcinomas with poor prognosis by artificial intelligence-based pathological image recognition.

BACKGROUND: Polyploidy is frequently observed in cancer cells and is closely associated with chromosomal instability, which can lead to cancer progres...

Interpretable and generalizable deep learning model for preoperative assessment of microvascular invasion and outcome in hepatocellular carcinoma based on MRI: a multicenter study.

OBJECTIVES: This study aimed to develop an interpretable, domain-generalizable deep learning model for microvascular invasion (MVI) assessment in hepa...

Identification of key genes as diagnostic biomarkers for IBD using bioinformatics and machine learning.

BACKGROUND: The pathogenesis of inflammatory bowel disease (IBD) involves complex molecular mechanisms, and achieving clinical remission remains chall...

A Model Predicting Artificial Intelligence Use by Gastroenterology Nurses in Clinical Practice: A Cross-Sectional Multicenter Survey.

BACKGROUND AND AIMS: Nurses' participation during colonoscopy has been demonstrated to significantly improve the detection rate of polyps and adenomas...

Exploring Inflammatory Bowel Disease Discourse on Reddit Throughout the COVID-19 Pandemic Using OpenAI's GPT-3.5 Turbo Model: Classification Model Validation and Case Study.

BACKGROUND: Inflammatory bowel disease (IBD) is a chronic autoimmune disorder with an increasing prevalence in the general population. Internet-based ...

Deep learning-driven insights into the transmission dynamics of hepatitis B virus with treatment.

Viral infections have spread globally, profoundly affecting social and economic aspects of life and causing widespread suffering. Infection caused by ...

Machine learning-assisted multi-dimensional transcriptomic analysis of cytoskeleton-related molecules and their relationship with prognosis in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide, with a poor prognosis due to its aggressive nature and limited tr...

MRI-based habitat, intra-, and peritumoral machine learning model for perineural invasion prediction in rectal cancer.

OBJECTIVES: This study aimed to analyze preoperative multimodal magnetic resonance images of patients with rectal cancer using habitat-based, intratum...

An Interpretable Artificial Intelligence System for Crohn's Disease Ulcer Identification and Grading on Double-Balloon Enteroscopy Images.

BACKGROUND: Crohn's disease (CD) is an incurable inflammatory bowel disease that can lead to a variety of complications and requires lifelong treatmen...

Predicting semantic segmentation quality in laryngeal endoscopy images.

Endoscopy is a major tool for assessing the physiology of inner organs. Contemporary artificial intelligence methods are used to fully automatically l...

An automated machine-learning model for prognostic risk stratification of intermediate-stage hepatocellular carcinoma after transarterial chemoembolization.

BACKGROUND: Currently, there is still a lack of noninvasive, automated, and accurate machine-learning(ML) model for prognostic risk stratification of ...

Diagnostic performance of artificial intelligence based on contrast-enhanced computed tomography in pancreatic ductal adenocarcinoma: a systematic review and meta-analysis.

PURPOSE: This meta-analysis systematically evaluated the diagnostic performance of artificial intelligence (AI) based on contrast-enhanced computed to...

Development of a tongue image-based machine learning tool for the diagnosis of colorectal cancer: a prospective multicentre clinical cohort study.

Colorectal cancer (CRC) remains a persistent major global health burden, with traditional diagnostic methods like colonoscopy suffering from suboptima...

DCAF13 Regulates Cell Proliferation and Immune Escape of Hepatocellular Carcinoma Through Activating the NF-κB Pathway.

Hepatocellular carcinoma (HCC), the third leading cause of global cancer deaths, has a high unmet clinical need due to limited therapeutic efficacy. I...

A ubiquitous and interoperable deep learning model for automatic detection of pleomorphic gastroesophageal lesions.

In recent years, artificial intelligence (AI) has been widely explored to enhance capsule endoscopy (CE), with the goal of improving the efficiency of...

Clinical validation of AI assisted animal ultrasound models for diagnosis of early liver trauma.

The study aimed to develop an AI-assisted ultrasound model for early liver trauma identification, using data from Bama miniature pigs and patients in ...