AIMC Topic: Gastroscopy

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Evaluating large language models for information extraction from gastroscopy and colonoscopy reports through multi-strategy prompting.

Journal of biomedical informatics
OBJECTIVE: To systematically evaluate large language models (LLMs) for automated information extraction from gastroscopy and colonoscopy reports through prompt engineering, addressing their ability to extract structured information, recognize complex...

Artificial intelligence for early gastric cancer boundary recognition in NBI and nF-NBI endoscopic images.

Scandinavian journal of gastroenterology
OBJECTIVES: Precise delineation of early gastric cancer (EGC) margins is essential for complete resection during endoscopic submucosal dissection. This study aimed to develop deep learning-based models for EGC boundary detection in narrow-band imagin...

Development and validation of a hypoxemia prediction model in middle-aged and elderly outpatients undergoing painless gastroscopy.

Scientific reports
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
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
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.

Prospective Evaluation of Real-Time Artificial Intelligence for the Hill Classification of the Gastroesophageal Junction.

United European gastroenterology journal
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
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
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
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...