Gastroenterology

Integrating Artificial Intelligence into Endoscopy Training.

As artificial intelligence (AI) becomes integrated into gastrointestinal endoscopy, training programs must adapt to prepare learners for AI-assisted practice. This review outlines current AI applications, ranging from polyp detection to workflow analysis, and proposes a structured curriculum embedding AI at every stage of training. The framework aims for both technical proficiency and adaptive exp...

Integrating machine learning of bulk and single cell RNA data to characterize immune mechanism of m6A related mitophagy genes in malignant gastric cancer.

BACKGROUND AND OBJECTIVE: Gastric cancer is a heterogeneous and complicated epithelial cancers. Chronic H. pylori and EBV infection, as well as intestinal microbiota exposure make gastric cancer encountered a complex tumor immune microenvironment. Mitophagy and m6A are deeply involved in immune microenvironment in the development of tumors. METHODS: We used integrating machine learning of bulk and...

Functional traits shape gut microbial assembly beyond phylogeny in estuarine fish.

Host-specific patterns of symbiotic microbiomes are ubiquitous in nature, yet the intricate interplay among host phylogeny, functional traits, and gut...

Overcoming missing data in spatial metabolomics with machine learning imputation to accelerate downstream discovery.

Mass spectrometry imaging (MSI)-based spatial metabolomics exhibits extensive missing values; yet, practical guidance on how imputation choices affect...

Development and validation of a multimodal deep learning model for early esophageal squamous neoplasia detection and invasion depth prediction.

Early detection of esophageal squamous cell carcinoma (ESCC) is critical for optimizing patient outcomes. Magnifying endoscopy and endoscopic ultrason...

Natural language processing of biomedical text to map and prioritize protein-disease associations in HFpEF.

The validation of promising clinical biomarkers, molecular mechanisms, and novel drug targets in cardiovascular disease (CVD) is hindered by a vast an...

Benzo[a]pyrene promotes gastric cancer progression via activation of the Correa cascade through modulation of the STAT3-TP53-MMP9 molecular axis.

To investigate the role of Benzo[a]pyrene (BaP) in driving the Correa cascade during gastric cancer development, we employed an integrated strategy co...

Minimum Clinically Achievable Dose for Detecting Liver Lesions Using Deep Learning Image Reconstruction: A Phantom and Patient Study.

RATIONALE AND OBJECTIVES: To investigate the performance of deep learning image reconstruction (DLIR) at an ultra-low dose of approximately 4.5 mGy fo...

A Multiomic Liquid Biopsy for the Earlier Detection of Colorectal Cancer.

UNLABELLED: Timely diagnosis and intervention in colorectal cancer are critical to improving patient outcomes and limiting disease progression. Screen...

Automatic localization of cross sections corresponding to standard transesophageal echocardiographic views on computed tomography volume.

Aims: Preoperative observation of computed tomography (CT) cross sections corresponding to standard transesophageal echocardiographic (TEE) views is u...

Early recurrence prediction and risk stratification of hepatocellular carcinoma after transarterial chemoembolization achieving imaging complete response based on contrast-enhanced CT machine learning.

OBJECTIVES: To develop and validate machine learning (ML) models using clinical and contrast-enhanced CT (CECT) parameters to assess recurrence risk i...

Characterization of Kidney and Liver Cystic Phenotype Associated with GANAB Using Advanced Imaging Biomarkers.

UNLABELLED: : Background and Hypothesis: Monoallelic pathogenic variants in GANAB cause autosomal dominant cystic kidney and liver disease, but quanti...

Bayesian machine learning enables discovery of risk factors for hepatosplenic multimorbidity related to schistosomiasis.

One in 25 deaths worldwide is related to liver disease, and often with multiple hepatosplenic conditions. Yet, little is understood of the risk factor...

Proteomics-based machine learning model for predicting secondary infection in HBV-related liver failure.

Patients with Hepatitis B Virus-related liver failure are highly vulnerable to secondary infections (SI), yet early predictive tools remain limited. I...

Predicting short-term mortality in severe cirrhosis: An interpretable machine learning model integrating routine clinical indicators.

BACKGROUND: The critical need for precise risk stratification in severe liver cirrhosis is underscored by its substantial 30-day mortality rates, dema...

Multi-omics profiling reveals cholic acid-mediated immunosuppression driven by peritumoral ductular reactions in hepatocellular carcinoma.

Hepatocellular carcinoma remains a leading cause of cancer mortality worldwide, with peritumoral microenvironment interactions playing a critical role...

Application of a Natural Language Processing Framework for Data Extraction From Pathology Reports Across Multiple Cancer Types.

BACKGROUND: Pathological reports provide comprehensive insights into the clinical and pathological features of different cancer types. However, extrac...

A CT radiomics signature enables risk stratification and survival prediction in colorectal liver metastases.

Colorectal liver metastases (CRLM) represent a major clinical challenge because outcomes after hepatic resection vary widely between patients. Preoper...

A fully automated workflow for the digital image analysis of the intestinal microcolony survival assay.

Comprehensive machine learning identifies anoikis signatures predicting therapeutic resistance and survival in gastric cancer.