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Create a Free AccountAnatomy - Colorectal
Deploying automated machine learning for computer vision projects: a brief introduction for endoscopists
Artificial intelligence (AI) and machine learning (ML) will play a growing role in gastroenterology. Computer-aided polyp detection is an early example of the how AI/ML can be applied to clinical practice.1 There is a broad array of potential applications for ML and computer vision in GI endoscopy. The most powerful computer vision models today use advanced deep learning algorithms that may be unfamiliar to most gastroenterologists, given that courses in machine learning have not yet been incorporated into most medical training pathways.Introducing computer-aided detection to the endoscopy suite
Artificial intelligence (AI) within GI endoscopy is an area of growing interest and ongoing innovation. The use of computer-aided diagnosis (CADx) and computer-aided detection (CADe) technologies based on AI algorithms may help augment endoscopists’ performance if these tools can be effectively incorporated into the endoscopy suite.1Simultaneous detection and characterization of diminutive polyps with the use of artificial intelligence during colonoscopy
The application of artificial intelligence (AI) in colonoscopy is attracting a growing amount of attention because it has the potential to improve the quality of colonoscopy.1,2 The main focuses of research in this field comprise automated polyp detection3,4 and characterization5,6 (ie, pathologic prediction), which may respectively contribute to a higher rate of adenoma detection and a reduction of the costs related to unnecessary polypectomy. However, there has not yet been any report of technology capable of simultaneous polyp detection and characterization, which is the optimal situation for fully automated colonoscopic observation.
