AIMC Topic: Gastroscopy
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Development and validation of a hypoxemia prediction model in middle-aged and elderly outpatients undergoing painless gastroscopy.
Scientific reports
May 23, 2025
Hypoxemia is a common complication associated with anesthesia in painless gastroscopy. With the aging of the social population, the number of cases of hypoxemia among middle-aged and elderly patients is increasing. However, tools for predicting hypox...
Constructing an artificial intelligence-assisted system for the assessment of gastroesophageal valve function based on the hill classification (with video).
BMC medical informatics and decision making
Mar 24, 2025
OBJECTIVE: In the functional assessment of the esophagogastric junction (EGJ), the endoscopic Hill classification plays a pivotal role in classifying the morphology of the gastroesophageal flap valve (GEFV). This study aims to develop an artificial i...
A risk prediction model for gastric cancer based on endoscopic atrophy classification.
BMC cancer
Mar 21, 2025
BACKGROUNDS: Gastric cancer (GC) is a prevalent malignancy affecting the digestive system. We aimed to develop a risk prediction model based on endoscopic atrophy classification for GC.
A prospective and comparative study on improving the diagnostic accuracy of early gastric cancer based on deep convolutional neural network real-time diagnosis system (with video).
Surgical endoscopy
Jan 22, 2025
BACKGROUND: Endoscopic diagnosis of early gastric cancer (EGC) is a challenge. It is not clear whether deep convolutional neural network (DCNN) model could improve the endoscopists' diagnostic performance.
Prospective Evaluation of Real-Time Artificial Intelligence for the Hill Classification of the Gastroesophageal Junction.
United European gastroenterology journal
Dec 12, 2024
BACKGROUND: Assessment of the gastroesophageal junction (GEJ) is an integral part of gastroscopy; however, the absence of standardized reporting hinders consistency of examination documentation. The Hill classification offers a standardized approach ...
A deep learning approach for gastroscopic manifestation recognition based on Kyoto Gastritis Score.
Annals of medicine
Nov 5, 2024
OBJECTIVE: The risk of gastric cancer can be predicted by gastroscopic manifestation recognition and the Kyoto Gastritis Score. This study aims to validate the applicability of AI approaches for recognizing gastroscopic manifestations according to th...
Development and application of an artificial intelligence-assisted endoscopy system for diagnosis of Helicobacter pylori infection: a multicenter randomized controlled study.
BMC gastroenterology
Sep 30, 2024
BACKGROUND: The early diagnosis and treatment of Heliobacter pylori (H.pylori) gastrointestinal infection provide significant benefits to patients. We constructed a convolutional neural network (CNN) model based on an endoscopic system to diagnose H....
Efficient artificial intelligence-based assessment of the gastroesophageal valve with Hill classification through active learning.
Scientific reports
Aug 13, 2024
Standardized assessment of the gastroesophageal valve during endoscopy, attainable via the Hill classification, is important for clinical assessment and therapeutic decision making. The Hill classification is associated with the presence of hiatal he...
An artificial intelligence system for comprehensive pathologic outcome prediction in early gastric cancer through endoscopic image analysis (with video).
Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association
Jul 2, 2024
BACKGROUND: Accurate prediction of pathologic results for early gastric cancer (EGC) based on endoscopic findings is essential in deciding between endoscopic and surgical resection. This study aimed to develop an artificial intelligence (AI) model to...
Machine learning models to predict submucosal invasion in early gastric cancer based on endoscopy features and standardized color metrics.
Scientific reports
May 7, 2024
Conventional endoscopy is widely used in the diagnosis of early gastric cancers (EGCs), but the graphical features were loosely defined and dependent on endoscopists' experience. We aim to establish a more accurate predictive model for infiltration d...
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