Gastroenterology

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 ma...

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 t...

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 ob...

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 un...

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 compu...

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 dia...

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 Pretraine...

Evaluating the synergistic use of advanced liver models and AI for the prediction of drug-induced liver injury.

INTRODUCTION: Drug-induced liver injury (DILI) is a leading cause of acute liver failure. Hepatotoxi...

SDR-Former: A Siamese Dual-Resolution Transformer for liver lesion classification using 3D multi-phase imaging.

Automated classification of liver lesions in multi-phase CT and MR scans is of clinical significance...

Practical X-ray gastric cancer diagnostic support using refined stochastic data augmentation and hard boundary box training.

Endoscopy is widely used to diagnose gastric cancer and has a high diagnostic performance, but it mu...

Predicting early recurrence in locally advanced gastric cancer after gastrectomy using CT-based deep learning model: a multicenter study.

BACKGROUND: Early recurrence in patients with locally advanced gastric cancer (LAGC) portends aggres...

Identification of hub biomarkers in liver post-metabolic and bariatric surgery using comprehensive machine learning (experimental studies).

BACKGROUND: The global prevalence of non-alcoholic fatty liver disease (NAFLD) is approximately 30%,...

Use of Artificial Intelligence in Lower Gastrointestinal and Small Bowel Disorders: An Update Beyond Polyp Detection.

Machine learning and its specialized forms, such as Artificial Neural Networks and Convolutional Neu...

Optimizing AI models to predict esophageal squamous cell carcinoma risk by incorporating small datasets of soft palate images.

There is a currently an unmet need for non-invasive methods to predict the risk of esophageal squamo...

Improving Outcomes in Hepatocellular Carcinoma through Integration of Machine Learning: Development of a Tumor-Associated Macrophage Signature.

INTRODUCTION: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors globally. Ma...

Multiscale deep learning radiomics for predicting recurrence-free survival in pancreatic cancer: A multicenter study.

PURPOSE: This multicenter study aimed to develop and validate a multiscale deep learning radiomics n...

A Machine Learning Model Score Predicts Advanced Fibrosis and Cirrhosis in Indeterminate FIB-4 Patients.

The Liver Intensive Care Unit.

Major advances in managing critically ill patients with liver disease have improved their prognosis ...

Impact of deep learning reconstructions on image quality and liver lesion detectability in dual-energy CT: An anthropomorphic phantom study.

BACKGROUND: Deep learning image reconstruction (DLIR) algorithms allow strong noise reduction while ...

Super-resolution deep-learning reconstruction with 1024 matrix improves CT image quality for pancreatic ductal adenocarcinoma assessment.

OBJECTIVES: To evaluate the efficiency of super-resolution deep-learning reconstruction (SR-DLR) opt...

A Rectal Cancer Surgery Dataset: Use of artificial intelligence to aid automation of error identification.

Minimally invasive surgery is complex and prone to variation not routinely objectively measured. We ...