Gastroenterology

Feature Mixing Approach for Detecting Intraoperative Adverse Events in Laparoscopic Roux-en-Y Gastric Bypass Surgery

Intraoperative adverse events (IAEs), such as bleeding or thermal injury, can lead to severe postoperative complications if undetected. However, their rarity results in highly imbalanced datasets, posing challenges for AI-based detection and severity quantification. We propose BetaMixer, a novel deep learning model that addresses these challenges through a Beta distribution-based mixing approach...

Landmark-Free Preoperative-to-Intraoperative Registration in Laparoscopic Liver Resection

Liver registration by overlaying preoperative 3D models onto intraoperative 2D frames can assist surgeons in perceiving the spatial anatomy of the liver clearly for a higher surgical success rate. Existing registration methods rely heavily on anatomical landmark-based workflows, which encounter two major limitations: 1) ambiguous landmark definitions fail to provide efficient markers for registr...

FocusNet: Transformer-enhanced Polyp Segmentation with Local and Pooling Attention

Colonoscopy is vital in the early diagnosis of colorectal polyps. Regular screenings can effectively prevent benign polyps from progressing to CRC. ...

Attention GhostUNet++: Enhanced Segmentation of Adipose Tissue and Liver in CT Images

Accurate segmentation of abdominal adipose tissue, including subcutaneous (SAT) and visceral adipose tissue (VAT), along with liver segmentation, is...

Rapid pathologic grading-based diagnosis of esophageal squamous cell carcinoma Raman spectroscopy and a deep learning algorithm.

BACKGROUND: Esophageal squamous cell carcinoma is a major histological subtype of esophageal cancer. Many molecular genetic changes are associated wit...

Predicting ulcer in H&E images of inflammatory bowel disease using domain-knowledge-driven graph neural network

Inflammatory bowel disease (IBD) involves chronic inflammation of the digestive tract, with treatment options often burdened by adverse effects. Ide...

Benchmarking Multi-Organ Segmentation Tools for Multi-Parametric T1-weighted Abdominal MRI

The segmentation of multiple organs in multi-parametric MRI studies is critical for many applications in radiology, such as correlating imaging biom...

Leveraging Anatomical Priors for Automated Pancreas Segmentation on Abdominal CT

An accurate segmentation of the pancreas on CT is crucial to identify pancreatic pathologies and extract imaging-based biomarkers. However, prior re...

Enhanced Anomaly Detection for Capsule Endoscopy Using Ensemble Learning Strategies

Capsule endoscopy is a method to capture images of the gastrointestinal tract and screen for diseases which might remain hidden if investigated with...

AI and Machine Learning Computational Modeling that Takes into Consideration Gut Microbiota for a Personalized Decision Support Preoperative Planning for an Optimum Liver Regeneration After Partial Hepatectomy.

INTRODUCTION: Gut microbiota (GM) is implicated in the remnant liver regeneration (LR) after partial hepatectomy (PH) and affects outcomes. Our study ...

Machine learning-based reconstruction of prognostic staging for gastric cancer patients with different differentiation grades: A multicenter retrospective study.

BACKGROUND: The prognosis of gastric cancer (GC) patients is poor, and an accurate prognostic staging system would help assess patients' prognostic st...

Noninvasive prediction of esophagogastric varices in hepatitis B: An extreme gradient boosting model based on ultrasound and serology.

BACKGROUND: Severe esophagogastric varices (EGVs) significantly affect prognosis of patients with hepatitis B because of the risk of life-threatening ...

Artificial intelligence-aided optical biopsy improves the diagnosis of esophageal squamous neoplasm.

BACKGROUND: Early detection of esophageal squamous neoplasms (ESN) is essential for improving patient prognosis. Optical diagnosis of ESN remains chal...

Artificial intelligence-assisted approach to assessing bowel wall thickness in pediatric inflammatory bowel disease using intestinal ultrasound images.

BACKGROUND AND AIM: Intestinal ultrasound (IUS) potentially spares patients from repeated endoscopies under sedation and eliminates the need for alter...

Leveraging network uncertainty to identify regions in rectal cancer clinical target volume auto-segmentations likely requiring manual edits.

BACKGROUND AND PURPOSE: While Deep Learning (DL) auto-segmentation has the potential to improve segmentation efficiency in the radiotherapy workflow, ...

Deep Learning-Accelerated Non-Contrast Abbreviated Liver MRI for Detecting Malignant Focal Hepatic Lesions: Dual-Center Validation.

OBJECTIVE: To compare a deep learning (DL)-accelerated non-enhanced abbreviated MRI (AMRI) protocol with standard AMRI (AMRI) of the liver in terms of...

RRM-TransUNet: Deep-Learning Driven Interactive Model for Precise Pancreas Segmentation in CT Images.

BACKGROUND: Pancreatic diseases such as cancer and pancreatitis pose significant health risks. Early detection requires precise segmentation results. ...

Utilizing Machine Learning to Predict Liver Allograft Fibrosis by Leveraging Clinical and Imaging Data.

BACKGROUND AND AIM: Liver transplant (LT) recipients may succumb to graft-related pathologies, contributing to graft fibrosis (GF). Current methods to...

Artificial Intelligence-Based Diets: A Role in the Nutritional Treatment of Metabolic Dysfunction-Associated Steatotic Liver Disease?

BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD) is a growing global health concern. Effective management of this conditio...

An Integrated AI-Enabled System Using One Class Twin Cross Learning (OCT-X) for Early Gastric Cancer Detection

Early detection of gastric cancer, a leading cause of cancer-related mortality worldwide, remains hampered by the limitations of current diagnostic ...