Gastroenterology

A deep learning-driven method for safe and effective ERCP cannulation.

PURPOSE: In recent years, the detection of the duodenal papilla and surgical cannula has become a critical task in computer-assisted endoscopic retrograde cholangiopancreatography (ERCP) cannulation operations. The complex surgical anatomy, coupled with the small size of the duodenal papillary orifice and its high similarity to the background, poses significant challenges to effective computer-ass...

Machine learning assisted radiomics in predicting postoperative occurrence of deep venous thrombosis in patients with gastric cancer.

BACKGROUND: Gastric cancer patients are prone to lower extremity deep vein thrombosis (DVT) after surgery, which is an important cause of death in postoperative patients. Therefore, it is particularly important to find a suitable way to predict the risk of postoperative occurrence of DVT in GC patients. This study aims to explore the effectiveness of using machine learning (ML) assisted radiomics ...

Excited state kinetics of tryptophan and NAD(P)H in blood plasma of normal and abnormal liver conditions: A tool to understand the metabolic changes and classification.

Early diagnosis at the metabolomic level is crucial for the treatment of liver cirrhosis and hepatocellular carcinoma (HCC). In this study, attempts w...

A foundation systematic review of natural language processing applied to gastroenterology & hepatology.

OBJECTIVE: This review assesses the progress of NLP in gastroenterology to date, grades the robustness of the methodology, exposes the field to a new ...

Machine learning-based plasma metabolomics for improved cirrhosis risk stratification.

BACKGROUND: Cirrhosis is a leading cause of mortality in patients with chronic liver disease (CLD). The rapid development of metabolomic technologies ...

Explainable Classification of Benign-Malignant Pulmonary Nodules With Neural Networks and Information Bottleneck.

Computerized tomography (CT) is a clinically primary technique to differentiate benign-malignant pulmonary nodules for lung cancer diagnosis. Early cl...

Use of artificial intelligence in submucosal vessel detection during third-space endoscopy.

While artificial intelligence (AI) shows high potential in decision support for diagnostic gastrointestinal endoscopy, its role in therapeutic endosco...

Eye-tracking dataset of endoscopist-AI teaming during colonoscopy: Retrospective and real-time acquisition.

Recent studies have demonstrated that integrating AI into colonoscopy procedures significantly improves the adenoma detection rate (ADR) and reduces t...

Constructing a machine learning model for systemic infection after kidney stone surgery based on CT values.

This study aims to develop a machine learning model utilizing Computed Tomography (CT) values to predict systemic inflammatory response syndrome (SIRS...

Deep learning radiomics nomogram for preoperatively identifying moderate-to-severe chronic cholangitis in children with pancreaticobiliary maljunction: a multicenter study.

BACKGROUND: Long-term severe cholangitis can lead to dense adhesions and increased fragility of the bile duct, complicating surgical procedures and el...

Statistical and machine learning based platform-independent key genes identification for hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the most prevalent and deadly form of liver cancer, and its mortality rate is gradually increasing worldwide. Existi...

Machine learning-based integration reveals immunological heterogeneity and the clinical potential of T cell receptor (TCR) gene pattern in hepatocellular carcinoma.

The T Cell Receptor (TCR) significantly contributes to tumor immunity, whereas the intricate interplay with the Hepatocellular Carcinoma (HCC) microen...

Identifying potential signatures of immune cells in hepatocellular carcinoma using integrative bioinformatics approaches and machine-learning strategies.

Hepatocellular carcinoma (HCC) is a malignant tumor regulated by the immune system. Immunotherapy using checkpoint inhibitors has shown encouraging ou...

Integrating radiological and clinical data for clinically significant prostate cancer detection with machine learning techniques.

In prostate cancer (PCa), risk calculators have been proposed, relying on clinical parameters and magnetic resonance imaging (MRI) enable early predic...

Machine learning-random forest model was used to construct gene signature associated with cuproptosis to predict the prognosis of gastric cancer.

Gastric cancer (GC) is one of the most common tumors; one of the reasons for its poor prognosis is that GC cells can resist normal cell death process ...

IPNet: An Interpretable Network With Progressive Loss for Whole-Stage Colorectal Disease Diagnosis.

Colorectal cancer plays a dominant role in cancer-related deaths, primarily due to the absence of obvious early-stage symptoms. Whole-stage colorectal...

An AI-assisted morphoproteomic approach is a supportive tool in esophagitis-related precision medicine.

Esophagitis is a frequent, but at the molecular level poorly characterized condition with diverse underlying etiologies and treatments. Correct diagno...

Deep learning and radiomics for gastric cancer serosal invasion: automated segmentation and multi-machine learning from two centers.

OBJECTIVE: The objective of this study is to develop an automated method for segmenting spleen computed tomography (CT) images using a deep learning m...

Comparative analysis of the DCNN and HFCNN Based Computerized detection of liver cancer.

Liver cancer detection is critically important in the discipline of biomedical image testing and diagnosis. Researchers have explored numerous machine...

Assessment of ChatGPT-generated medical Arabic responses for patients with metabolic dysfunction-associated steatotic liver disease.

BACKGROUND AND AIM: Artificial intelligence (AI)-powered chatbots, such as Chat Generative Pretrained Transformer (ChatGPT), have shown promising resu...