Gastroenterology

Interpretable machine learning model for predicting the risk of dysphagia in patients after cardiac surgery.

BACKGROUND: Dysphagia is recognized as one of the most common severe complications following cardiac surgery, with the potential to result in adverse clinical outcomes, including aspiration pneumonia, malnutrition, and prolonged hospitalization. Early identification of patients at high risk for postoperative dysphagia is critical for implementing targeted preventive interventions and improving cli...

Preoperative prediction of metachronous liver metastasis in colorectal cancer using a deep learning-based radiomics model with automatic segmentation: a multicenter study.

OBJECTIVES: To develop and validate an integrated clinical-radiomics nomogram predicting the risk of metachronous liver metastasis (MLM) in patients with colorectal cancer (CRC). METHODS: In this multicenter retrospective study, 518 CRC patients underwent nnU-Net-based automatic segmentation of CT images. Radiomic features from CRC and liver regions were extracted and selected using LASSO regressi...

Artificial Intelligence as a New Player in Dietary Management of Inflammatory Bowel Disease.

Virtual Multi-Phase Contrast Enhanced Liver MRI Using Deep Learning for Evaluating Hepatocellular Carcinoma.

A deep learning (DL) model was developed to generate contrast-enhanced MRI (CE-MRI) at multiple enhancement phases (arterial, portal venous, transitio...

A Deep Learning Algorithm for Liver Metastasis Detection at Contrast-enhanced Abdominal CT in Patients with Colorectal Cancer: A Comparative Study with Radiologists.

Purpose To evaluate the performance of a deep learning algorithm (DLA) for detecting liver metastases (LM) in patients with colorectal cancer (CRC) ac...

A Network-Based Association of IBD and Colorectal Cancer Using Proteomics Data.

BACKGROUND: Colorectal cancer (CRC) is a major cause of morbidity and mortality, with chronic inflammation from inflammatory bowel disease (IBD) repre...

Pediatric Personalized Deep Learning Models for Segmentation of Hepatoblastoma at CT and MRI.

Purpose To evaluate the generalizability of adult-trained models for hepatoblastoma segmentation to pediatric patients and to develop two deep learnin...

Impact of Annotation Level on Multisequence MRI Models for Preoperative Microvascular Invasion Prediction in Hepatocellular Carcinoma.

Purpose To evaluate the performance of deep learning models integrating multimodal data for predicting microvascular invasion (MVI) in hepatocellular ...

Identification and Validation of cGAS-STING Pathway-Associated Predictive and Therapeutic Models for Esophageal Squamous Cell Cancer Patients via Artificial Intelligence and Multi-Omics.

BACKGROUND: Esophageal squamous cell cancer (ESCC) is a malignancy derived from the Esophagus, and dysregulation of the cGAS-STING pathway contributes...

Generalizability of deep learning-based dose conversion model in proton beam therapy.

BACKGROUND: In proton beam therapy (PBT), the analytical pencil beam (PB) algorithm involves dose uncertainties in inhomogeneous regions, making accur...

Artificial Intelligence in FIT-Positive Colonoscopy: Balancing Detection Metrics and Clinical Impact.

Multi-modal deep learning model for predicting recurrence of moderately severe and severe acute pancreatitis.

PURPOSE: To overcome the limitations of single-modality predictors by developing and validating a multimodal model (APNet) that integrates clinical fa...

Identification and validation of key PANoptosis-related genes via integrative machine learning and single-cell sequencing in AILI.

Acetaminophen (APAP) overdose is a leading cause of drug-induced liver injury and acute liver failure. PANoptosis, a recently defined form of programm...

Predicting infected pancreatic necrosis in acute pancreatitis using machine learning models and feature selection.

Infected pancreatic necrosis (IPN) is a life-threatening complication of acute pancreatitis (AP), and its early prediction remains challenging. This s...

Metabolic liver imaging: What you need to know.

Metabolic liver diseases represent a growing global health concern with significant diagnostic and prognostic implications. Imaging offers non-invasiv...

Gut Microbiota and Metabolic Health: From Dysbiosis to Therapeutics.

The gut microbiota (GM) is a pivotal regulator of host metabolism and a contributor to the pathophysiology of obesity, type 2 diabetes (T2D), and meta...

Nanozymes for Liver Disease Therapy: Advances in Catalytic Activity, Targeting Strategies, and Clinical Translation.

Natural and artificial enzymes have emerged as promising candidates for biomedical applications, possessing the potential to address redox imbalances ...

Integrated Single-Cell and Bulk Transcriptome Analysis Reveals the Prognostic Significance and Immune Regulatory Mechanisms of Sialylation in Hepatocellular Carcinoma.

Liver Hepatocellular Carcinoma (LIHC) is a high-mortality primary liver cancer. Its treatment and prognosis are highly dependent on disease stage and ...

Use of Bioinformatics and Machine Learning to Identify Circadian Rhythm Disruption- and Endoplasmic Reticulum Stress-Associated Biomarkers in Nonalcoholic Fatty Liver Disease.

Nonalcoholic fatty liver disease (NAFLD) is a common chronic liver disorder closely linked to circadian rhythm disruption (CRD) and endoplasmic reticu...

Predicting non-alcoholic fatty liver disease (NAFLD) using machine learning algorithms: Evidence from a large-scale community cohort in Taiwan.

Closely associated with metabolic disorders, non-alcoholic fatty liver disease (NAFLD) substantially increases the risk of hepatocellular carcinoma. T...