Gastroenterology

An Integrated Radiopathomics Machine Learning Model to Predict Pathological Response to Preoperative Chemotherapy in Gastric Cancer.

RATIONALE AND OBJECTIVES: Accurately predicting the pathological response to chemotherapy before tre...

Use of artificial intelligence improves colonoscopy performance in adenoma detection: a systematic review and meta-analysis.

BACKGROUND AND AIMS: Artificial intelligence (AI) is increasingly used to improve adenoma detection ...

Machine learning approach to predict blood-secretory proteins and potential biomarkers for liver cancer using omics data.

Identifying non-invasive blood-based biomarkers is crucial for early detection and monitoring of liv...

Next-generation pediatric care: nanotechnology-based and AI-driven solutions for cardiovascular, respiratory, and gastrointestinal disorders.

BACKGROUND: Global pediatric healthcare reveals significant morbidity and mortality rates linked to ...

A prognostic biomarker of disulfidptosis constructed by machine learning framework model as potential reporters of pancreatic adenocarcinoma.

BACKGROUND: Pancreatic adenocarcinoma (PAAD), known for its high lethality, has not been thoroughly ...

Early detection of pancreatic cancer by comprehensive serum miRNA sequencing with automated machine learning.

BACKGROUND: Pancreatic cancer is often diagnosed at advanced stages, and early-stage diagnosis of pa...

Evaluation of online chat-based artificial intelligence responses about inflammatory bowel disease and diet: correspondence.

Research progress of the Otubains subfamily in hepatocellular carcinoma.

In cancer research, oncogenesis can be affected by modulating the deubiquitination pathway. Ubiquiti...

Multitask Learning on Graph Convolutional Residual Neural Networks for Screening of Multitarget Anticancer Compounds.

Recently, various modern experimental screening pipelines and assays have been developed to find pro...

Machine-learning model to predict the tacrolimus concentration and suggest optimal dose in liver transplantation recipients: a multicenter retrospective cohort study.

Titrating tacrolimus concentration in liver transplantation recipients remains a challenge in the ea...

Meta-analysis of the effectiveness of early endoscopic treatment of Acute biliary pancreatitis based on lightweight deep learning model.

BACKGROUND: Acute biliary pancreatitis (ABP) is a clinical common acute abdomen. After the first pan...

Performance of AI-Enabled Electrocardiogram in the Prediction of Metabolic Dysfunction-Associated Steatotic Liver Disease.

BACKGROUND AND AIMS: Accessible noninvasive screening tools for metabolic dysfunction-associated ste...

Accelerated T2-weighted MRI of the Bowel at 3T Using a Single-shot Technique with Deep Learning-based Image Reconstruction: Impact on Image Quality and Disease Detection.

RATIONALE AND OBJECTIVE: A single-shot T2-weighted deep-learning-based image reconstruction (DL-HAST...

Toward an Explainable Large Language Model for the Automatic Identification of the Drug-Induced Liver Injury Literature.

Drug-induced liver injury (DILI) stands as a significant concern in drug safety, representing the pr...

Liver Cancer Diagnosis: Enhanced Deep Maxout Model with Improved Feature Set.

This work proposed a liver cancer classification scheme that includes Preprocessing, Feature extract...

Role of artificial intelligence in Crohn's disease intestinal strictures and fibrosis.

Crohn's disease (CD) is a chronic inflammatory disorder of the gastrointestinal tract. Intestinal fi...

Informatics-driven solutions for optimal care delivery in liver transplantation.

Clinical informatics, which combines health information technology and clinical expertise, aims to i...

Development of an artificial intelligence-based model to predict early recurrence of neuroendocrine liver metastasis after resection.

PURPOSE: We sought to develop an artificial intelligence (AI)-based model to predict early recurrenc...

Ultrasonic Assessment of Liver Fibrosis Using One-Dimensional Convolutional Neural Networks Based on Frequency Spectra of Radiofrequency Signals with Deep Learning Segmentation of Liver Regions in B-Mode Images: A Feasibility Study.

The early detection of liver fibrosis is of significant importance. Deep learning analysis of ultras...

A Computer Vision Algorithm to Predict Superior Mesenteric Artery Margin Status for Patients With Pancreatic Ductal Adenocarcinoma.

OBJECTIVE: To evaluate the feasibility of developing a computer vision algorithm that uses preoperat...

Optimization of the extraction process of total steroids from (Schwein.) Pat. by artificial neural network (ANN)-response surface methodology and identification of extract constituents.

(Schwein.) Pat has pharmacological effects such as tonifying the spleen, dispelling dampness, and s...