- Recently, the use of computer-aided detection (CADe) for colonoscopy has been investigated to improve the adenoma detection rate (ADR). We aimed to assess the efficacy of a regulatory-approved CADe in a large-scale study with high numbers of patients and endoscopists.
- A Dutch research team engaged in the national colorectal cancer screening program published an “amazing” result in 2020.1 As many as 60% of T1 (submucosally invasive) colorectal cancers detected during the program were misdiagnosed as adenomas by the on-site endoscopists and thus could be susceptible to inappropriate treatment intervention. Is this surprisingly low sensitivity for cancer recognition (40% in this case) reality? We would say yes, although many retrospective studies assessing advanced endoscopic modalities have suggested >90% sensitivities in predicting T1 cancers.
- Artificial intelligence (AI)–assisted polyp detection systems for colonoscopic use are currently attracting attention because they may reduce the possibility of missed adenomas. However, few systems have the necessary regulatory approval for use in clinical practice. We aimed to develop an AI-assisted polyp detection system and to validate its performance using a large colonoscopy video database designed to be publicly accessible.
- Artificial intelligence (AI) for GI endoscopy is an important and rapidly growing area of research. Much initial work in AI for endoscopy has focused on detection and optical diagnosis of colon polyps. However, AI has the potential to aid clinical decision making in many other aspects of gastroenterology.1 In this issue of Gastrointestinal Endoscopy, Zhou and colleagues2 explore the potential of AI to address one of the most clinically important issues in the management of early gastric cancers (EGCs): prediction of invasion depth.