Latest AI and machine learning research in gastroenterology for healthcare professionals.
BACKGROUND: Artificial intelligence (AI) models are being increasingly integrated into clinical care. Moreover, the availability of publicly accessible AI resources makes them attractive to patients seeking clinical information. Little is known regarding the use of large language models as patient resources for navigating major cancer diagnoses. OBJECTIVE: This study aimed to evaluate the content,...
AIM: To develop and validate a deep learning-based AI system for the dynamic, real-time differentiation of benign and malignant gastric ulcers during endoscopy, with the goal of enhancing diagnostic precision. METHODS: This was a multicenter, retrospective study collecting endoscopic images and videos from four tertiary hospitals in China. An improved YOLOv8 model, incorporating an illumination at...
Subepithelial lesions (SELs) of the gastrointestinal tract encompass a heterogeneous spectrum of histology, ranging from benign to malignant. Their de...
Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal human cancers, mostly due to its insidious onset that consequently leads to dia...
Metabolic dysfunction-associated steatotic liver disease (MASLD) represents a leading global health burden, yet its diagnosis and staging rely heavily...
BACKGROUND: Quality control can reduce variations in operators' performance conducting magnetically controlled capsule gastroscopy (MCCG). However, an...
The gut microbiome supports digestion, immunity, and metabolism; its imbalance (dysbiosis) drives inflammation and metabolic dysfunction, contributing...
BACKGROUND: Postoperative gastrointestinal (GI) bleeding is a serious complication after hip fracture surgery in older adults, yet perioperative risk ...
BACKGROUND: Daydreaming can be monitored either to avoid it while doing hands-on tasks or to enhance it to foster creativity. Although significant res...
BACKGROUND: This study sought to develop an innovative body composition (BC)-based deep learning (DL) model to precisely evaluate survival in gastric ...
OBJECTIVES: Diagnostic delay in Crohn's disease (CD) may increase the risk of poor prognosis. This study developed and validated a machine learning (M...
The primary goal of variceal screening in patients with cirrhosis is to identify high-risk esophageal varices (HREV) and implement preventative measur...
OBJECTIVES: Artificial intelligence (AI)-assisted endoscopy has been developed for the early detection of upper gastrointestinal cancer; however, its ...
BACKGROUND: Metabolic dysfunction-associated steatotic liver disease is increasingly recognized as a precursor to secondary hyperuricemia, significant...
PURPOSE: To evaluate feasibility and safety of robot-assisted subretinal injections through attached retina using the comanipulated Mynutia system in ...
Strong non-invasive tests (NITs) are needed to predict decompensation in patients with compensated advanced chronic liver disease (cACLD) and improve ...
BACKGROUND & AIMS: Transient elastography (TE) is routinely undertaken for non-invasive assessment of liver fibrosis and steatosis, but is limited by ...
PURPOSE: Placental growth factor (PGF) is associated with the progression of hepatocellular carcinoma (HCC), but current research on this relationship...
Purpose To develop and validate a deep learning model integrating tumor and visceral adipose tissue (VAT) CT scan features with clinical indicators to...