Background Artificial intelligence using computer-aided diagnosis (CADx) in real time with
images acquired during colonoscopy may help colonoscopists distinguish between …

Background & Aims Artificial Intelligence (AI) could support cost-saving strategies for
colonoscopy because of its accuracy in the optical diagnosis of colorectal polyps. However …

Background: Computer-aided diagnosis (CAD) for colonoscopy may help endoscopists
distinguish neoplastic polyps (adenomas) requiring resection from nonneoplastic polyps not …

We developed a computer-aided diagnosis (CAD) system based on linked color imaging
(LCI) images to predict the histological results of polyps by analyzing the colors of the …

Background and Aims Artificial intelligence (AI) is being implemented in colonoscopy
practice, but no study has investigated whether AI is cost saving. We aimed to quantify the …

Background & Aims Narrow-band imaging (NBI) can be used to determine whether
colorectal polyps are adenomatous or hyperplastic. We investigated whether an artificial …

Background Use of artificial intelligence may increase detection of colorectal neoplasia at
colonoscopy by improving lesion recognition (CADe) and reduce pathology costs by …

Computer-aided diagnosis offers a promising solution to reduce variation in colonoscopy
performance. Pooled miss rates for polyps are as high as 22%, and associated interval …

Background and aims: Clinical data suggest that the quality of optical diagnoses of
colorectal polyps differs markedly among endoscopists. The aim of this study was to develop …

Artificial intelligence (AI) has increasingly been employed in multiple fields, and there has
been significant interest in its use within gastrointestinal endoscopy. Computer-aided …