Gastroenterology

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 ultrasound backscattered radiofrequency (RF) signals is emerging for tissue characterization as the RF signals carry abundant information related to tissue microstructures. However, the existing methods only used the time-domain information of the RF signals for liver fibrosis assessment, and the liver re...

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 preoperative computed tomography (CT) scans to predict superior mesenteric artery (SMA) margin status in patients undergoing Whipple for pancreatic ductal adenocarcinoma (PDAC), and to compare algorithm performance to that of expert abdominal radiologists and surgical oncologists. BACKGROUND: Complete surgic...

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 strengthening the stomach, in which sterol is one o...

Deep Learning With Ultrasound Images Enhance the Diagnosis of Nonalcoholic Fatty Liver.

OBJECTIVE: This research aimed to improve diagnosis of non-alcoholic fatty liver disease (NAFLD) by deep learning with ultrasound Images and reduce th...

Artificial intelligence-based model for the recurrence of hepatocellular carcinoma after liver transplantation.

BACKGROUND: Artificial intelligence-based models might improve patient selection for liver transplantation in hepatocellular carcinoma. The objective ...

Machine learning approaches to detect hepatocyte chromatin alterations from iron oxide nanoparticle exposure.

This study focuses on developing machine learning models to detect subtle alterations in hepatocyte chromatin organization due to Iron (II, III) oxide...

Deep learning-based automated liver contouring using a small sample of radiotherapy planning computed tomography images.

INTRODUCTION: No study has yet investigated the minimum amount of data required for deep learning-based liver contouring. Therefore, this study aimed ...

Depth estimation from monocular endoscopy using simulation and image transfer approach.

Obtaining accurate distance or depth information in endoscopy is crucial for the effective utilization of navigation systems. However, due to space co...

Explore key genes of Crohn's disease based on glycerophospholipid metabolism: A comprehensive analysis Utilizing Mendelian Randomization, Multi-Omics integration, Machine Learning, and SHAP methodology.

BACKGROUND AND AIMS: Crohn's disease (CD) is a chronic, complex inflammatory condition with increasing incidence and prevalence worldwide. However, th...

Assessing the Readability, Reliability, and Quality of AI-Modified and Generated Patient Education Materials for Endoscopic Skull Base Surgery.

BACKGROUND: Despite National Institutes of Health and American Medical Association recommendations to publish online patient education materials at or...

Explainable machine learning for assessing upper respiratory tract of racehorses from endoscopy videos.

Laryngeal hemiplegia (LH) is a major upper respiratory tract (URT) complication in racehorses. Endoscopy imaging of horse throat is a gold standard fo...

Machine learning-causal inference based on multi-omics data reveals the association of altered gut bacteria and bile acid metabolism with neonatal jaundice.

Early identification of neonatal jaundice (NJ) appears to be essential to avoid bilirubin encephalopathy and neurological sequelae. The interaction be...

A machine learning-based prediction model for delayed clinically important postoperative nausea and vomiting in high-risk patients undergoing laparoscopic gastrointestinal surgery.

BACKGROUND: Delayed clinically important postoperative nausea and vomiting (CIPONV) could lead to significant consequences following surgery. We aimed...

Perovskite Probe-Based Machine Learning Imaging Model for Rapid Pathologic Diagnosis of Cancers.

Accurately distinguishing tumor cells from normal cells is a key issue in tumor diagnosis, evaluation, and treatment. Fluorescence-based immunohistoch...

Fast prediction of personalized abdominal organ doses from CT examinations by radiomics feature-based machine learning models.

The X-rays emitted during CT scans can increase solid cancer risks by damaging DNA, with the risk tied to patient-specific organ doses. This study aim...

Machine learning to predict completion of treatment for pancreatic cancer.

BACKGROUND: Chemotherapy enhances survival rates for pancreatic cancer (PC) patients postsurgery, yet less than 60% complete adjuvant therapy, with a ...

A novel deep learning identifier for promoters and their strength using heterogeneous features.

Promoters, which are short (50-1500 base-pair) in DNA regions, have emerged to play a critical role in the regulation of gene transcription. Numerous ...

Accuracy and consistency of publicly available Large Language Models as clinical decision support tools for the management of colon cancer.

BACKGROUND: Large Language Models (LLM; e.g., ChatGPT) may be used to assist clinicians and form the basis of future clinical decision support (CDS) f...

Artificial intelligence-aided ultrasound imaging in hepatopancreatobiliary surgery: where are we now?

BACKGROUND: Artificial intelligence (AI) models have been applied in various medical imaging modalities and surgical disciplines, however the current ...

Development and internal validation of an artificial intelligence-assisted bowel sounds auscultation system to predict early enteral nutrition-associated diarrhoea in acute pancreatitis: a prospective observational study.

An artificial intelligence-assisted prediction model for enteral nutrition-associated diarrhoea (ENAD) in acute pancreatitis (AP) was developed utili...