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

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

Background and Aims Recently, the use of computer-aided detection (CADe) for
colonoscopy has been investigated to improve the adenoma detection rate (ADR). We …

Recent breakthroughs in artificial intelligence (AI), specifically via its emerging sub-field
“deep learning,” have direct implications for computer-aided detection and diagnosis (CADe …

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 & Aims One-fourth of colorectal neoplasias are missed during screening
colonoscopies; these can develop into colorectal cancer (CRC). Deep learning systems …

Background and Aims One-fourth of colorectal neoplasia are missed at screening
colonoscopy, representing the main cause of interval colorectal cancer. Deep learning …

Background Tandem colonoscopy studies have found that about one in five adenomas are
missed at colonoscopy. It remains debatable whether the combination of a computer-aided …

The number of artificial intelligence (AI) tools for colonoscopy on the market is increasing
with supporting clinical evidence. Nevertheless, their implementation is not going smoothly …

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