AI Medical Compendium Journal:
Journal of gastroenterology and hepatology

Showing 11 to 20 of 58 articles

Automatic assessment of bowel preparation by an artificial intelligence model and its clinical applicability.

Journal of gastroenterology and hepatology
BACKGROUND AND AIM: Reliable bowel preparation assessment is important in colonoscopy. However, current scoring systems are limited by laborious and time-consuming tasks and interobserver variability. We aimed to develop an artificial intelligence (A...

Comparison of deep learning models to traditional Cox regression in predicting survival of colon cancer: Based on the SEER database.

Journal of gastroenterology and hepatology
BACKGROUND AND AIM: In this study, a deep learning algorithm was used to predict the survival rate of colon cancer (CC) patients, and compared its performance with traditional Cox regression.

Development and validation of a novel colonoscopy withdrawal time indicator based on YOLOv5.

Journal of gastroenterology and hepatology
BACKGROUND AND AIM: The study aims to introduce a novel indicator, effective withdrawal time (WTS), which measures the time spent actively searching for suspicious lesions during colonoscopy and to compare WTS and the conventional withdrawal time (WT...

Validation of GPT-4 for clinical event classification: A comparative analysis with ICD codes and human reviewers.

Journal of gastroenterology and hepatology
BACKGROUND AND AIM: Effective clinical event classification is essential for clinical research and quality improvement. The validation of artificial intelligence (AI) models like Generative Pre-trained Transformer 4 (GPT-4) for this task and comparis...

The effect of incorporating domain knowledge with deep learning in identifying benign and malignant gastric whitish lesions: A retrospective study.

Journal of gastroenterology and hepatology
BACKGROUND AND AIM: Early whitish gastric neoplasms can be easily misdiagnosed; differential diagnosis of gastric whitish lesions remains a challenge. We aim to build a deep learning (DL) model to diagnose whitish gastric neoplasms and explore the ef...

Two-stage deep-learning-based colonoscopy polyp detection incorporating fisheye and reflection correction.

Journal of gastroenterology and hepatology
BACKGROUND AND AIM: Colonoscopy is a useful method for the diagnosis and management of colorectal diseases. Many computer-aided systems have been developed to assist clinicians in detecting colorectal lesions by analyzing colonoscopy images. However,...

Artificial intelligence-aided diagnostic imaging: A state-of-the-art technique in precancerous screening.

Journal of gastroenterology and hepatology
BACKGROUND AND AIM: Chromoendoscopy with the use of indigo carmine (IC) dye is a crucial endoscopic technique to identify gastrointestinal neoplasms. However, its performance is limited by the endoscopist's skill, and no standards are available for l...

Comparison of clinical utility of deep learning-based systems for small-bowel capsule endoscopy reading.

Journal of gastroenterology and hepatology
BACKGROUND AND AIM: Convolutional neural network (CNN) systems that automatically detect abnormalities from small-bowel capsule endoscopy (SBCE) images are still experimental, and no studies have directly compared the clinical usefulness of different...

Outcomes of robot-assisted versus laparoscopic surgery for colorectal cancer in morbidly obese patients: A propensity score-matched analysis of the US Nationwide Inpatient Sample.

Journal of gastroenterology and hepatology
BACKGROUND AND AIM: Morbid obesity is associated with poorer postoperative outcomes in colorectal cancer (CRC) patients. We aimed to evaluate short-term outcomes after robotic versus conventional laparoscopic CRC resection in morbidly obese patients.

Application of convolutional neural networks for evaluating the depth of invasion of early gastric cancer based on endoscopic images.

Journal of gastroenterology and hepatology
BACKGROUND AND AIM: Recently, artificial intelligence (AI) has been used in endoscopic examination and is expected to help in endoscopic diagnosis. We evaluated the feasibility of AI using convolutional neural network (CNN) systems for evaluating the...