Purpose To assess the diagnostic performance of distributed human intelligence for the
classification of polyp candidates identified with computer-aided detection (CAD) for …

Computer-aided detection (CAD) systems have been shown to improve the diagnostic
performance of CT colonography (CTC) in the detection of premalignant colorectal polyps …

Objectives To quantify the incremental benefit of computer-assisted-detection (CAD) for
polyps, for inexperienced readers versus experienced readers of CT colonography. Methods …

Background Artificial intelligence (AI) assistance has been considered as a promising way to
improve colonoscopic polyp detection, but there are limited prospective studies on real‐time …

Research in computer-aided diagnosis (CAD) and the application of artificial intelligence
(AI) in the endoscopic evaluation of the gastrointestinal tract is novel. Since colonoscopy …

Purpose The purpose of this study was to develop an advanced computer‐aided detection
(CAD) scheme utilizing massive‐training artificial neural networks (MTANNs) to allow …

Background and Aim Application of artificial intelligence in medicine is now attracting
substantial attention. In the field of gastrointestinal endoscopy, computer‐aided diagnosis …

Purpose To evaluate stand-alone performance of computer-aided detection (CAD) for
colorectal polyps of 6 mm or larger at computed tomographic (CT) colonography in a large …

The purpose of this study was to investigate the incremental effect of focused training on
observer performance when using computer-assisted detection (CAD) software to interpret …

Background Colonoscopy reduces the incidence of colorectal cancer (CRC) by allowing
detection and resection of neoplastic polyps. Evidence shows that many small polyps are …