Gastroenterology

Prediction of Crohn's disease based on deep feature recognition.

BACKGROUND: Crohn's disease is a complex genetic disease that involves chronic gastrointestinal inflammation and results from a complex set of genetic, environmental, and immunological factors. By analyzing data from the human microbiome, genetic information can be used to predict Crohn's disease. Recent advances in deep learning have demonstrated its effectiveness in feature extraction and the us...

Retrospective Analysis of Radiofrequency Ablation in Patients with Small Solitary Hepatocellular Carcinoma: Survival Outcomes and Development of a Machine Learning Prognostic Model.

BACKGROUND AND OBJECTIVE: The effectiveness of radiofrequency ablation (RFA) in improving long-term survival outcomes for patients with a solitary hepatocellular carcinoma (HCC) measuring 5 cm or less remains uncertain. This study was designed to elucidate the impact of RFA therapy on the survival outcomes of these patients and to construct a prognostic model for patients following RFA.

Development and application of an artificial intelligence-assisted endoscopy system for diagnosis of Helicobacter pylori infection: a multicenter randomized controlled study.

BACKGROUND: The early diagnosis and treatment of Heliobacter pylori (H.pylori) gastrointestinal infection provide significant benefits to patients. We...

Aliado - A design concept of AI for decision support in oncological liver surgery.

BACKGROUND: The interest in artificial intelligence (AI) is increasing. Systematic reviews suggest that there are many machine learning algorithms in ...

Machine learning for post-liver transplant survival: Bridging the gap for long-term outcomes through temporal variation features.

BACKGROUND: The long-term survival of liver transplant (LT) recipients is essential for optimizing organ allocation and estimating mortality outcomes....

Polynomial-SHAP analysis of liver disease markers for capturing of complex feature interactions in machine learning models.

Liver disease diagnosis is pivotal for effective patient management, and machine learning techniques have shown promise in this domain. In this study,...

Effectiveness of data-augmentation on deep learning in evaluating rapid on-site cytopathology at endoscopic ultrasound-guided fine needle aspiration.

Rapid on-site cytopathology evaluation (ROSE) has been considered an effective method to increase the diagnostic ability of endoscopic ultrasound-guid...

Real-time segmentation of biliary structure in pure laparoscopic donor hepatectomy.

Pure laparoscopic donor hepatectomy (PLDH) has become a standard practice for living donor liver transplantation in expert centers. Accurate understan...

Identification of Key Genes in Fetal Gut Development at Single-Cell Level by Exploiting Machine Learning Techniques.

The study of fetal gut development is critical due to its substantial influence on immediate neonatal and long-term adult health. Current research lar...

ReBiA-Robotic Enabled Biological Automation: 3D Epithelial Tissue Production.

The Food and Drug Administration's recent decision to eliminate mandatory animal testing for drug approval marks a significant shift to alternative me...

Machine learning algorithms and biomarkers identification for pancreatic cancer diagnosis using multi-omics data integration.

PURPOSE: Pancreatic cancer is a lethal type of cancer with most of the cases being diagnosed in an advanced stage and poor prognosis. Developing new d...

Bowel preparation before elective right colectomy: Multitreatment machine-learning analysis on 2,617 patients.

BACKGROUND: In the worldwide, real-life setting, some candidates for right colectomy still receive no bowel preparation, some receive oral antibiotics...

Raman fiber-optic probe for rapid diagnosis of gastric and esophageal tumors with machine learning analysis or similarity assessments: a comparative study.

Gastric and esophageal cancers, the predominant forms of upper gastrointestinal malignancies, contribute significantly to global cancer mortality. Rou...

Personalized food consumption detection with deep learning and Inertial Measurement Unit sensor.

For individuals diagnosed with diabetes mellitus, it is crucial to keep a record of the carbohydrates consumed during meals, as this should be done at...

Randomized controlled trial of an artificial intelligence diagnostic system for the detection of esophageal squamous cell carcinoma in clinical practice.

BACKGROUND: Artificial intelligence (AI) has made remarkable progress in image recognition using deep learning systems. It has been used to detect eso...

Real-time augmentation of diagnostic nasal endoscopy video using AI-enabled edge computing.

AI-enabled augmentation of nasal endoscopy video images is feasible in the clinical setting. Edge computing hardware can interface with existing nasal...

Effectiveness of artificial intelligence assisted colonoscopy on adenoma and polyp miss rate: A meta-analysis of tandem RCTs.

BACKGROUND AND AIMS: One-fourth of colorectal neoplasia is missed at screening colonoscopy, representing the leading cause of interval colorectal canc...

Novel prognostic signature for hepatocellular carcinoma using a comprehensive machine learning framework to predict prognosis and guide treatment.

BACKGROUND: Hepatocellular carcinoma (HCC) is highly aggressive, with delayed diagnosis, poor prognosis, and a lack of comprehensive and accurate prog...

Identification of macrotrabecular-massive hepatocellular carcinoma through multiphasic CT-based representation learning method.

BACKGROUND: Macrotrabecular-massive hepatocellular carcinoma (MTM-HCC) represents an aggressive subtype of HCC and is associated with poor survival.

Artificial Intelligence in Gastrointestinal Endoscopy.

Recent advancements in artificial intelligence (AI) have significantly impacted the field of gastrointestinal (GI) endoscopy, with applications spanni...