Artificial intelligence in gastrointestinal endoscopy: The future is almost here

Figure 1 Automatic polyp detection by Wang et al[40]. A: Original image obtained during colonoscopy; B: Automatic detection by box method; C: Probability map whereby red indicates high probability of polyp and blue indicates low probability of polyp; D: Automatic detection by paint method whereby blue coloring indicates location of polyp.

Figure 2 Output from artifical intelligence-assisted endocytoscopy system by Misawa et al[57]. A: Input from endocytoscopy with narrow band imaging; B: Extracted vessel image whereby green light represents extracted vessel image; C: System outputs diagnosis of neoplastic or non-neoplastic; D: Probability of diagnosis calculated by support vector machine classifier. NBI: narrow band imaging.

Figure 3 Automatic polyp classification system. 1: Input from narrow band imaging; 2: Computer diagnosis of NICE type 1 (hyperplastic) vs NICE type 2 (adenomatous); 3: Probability of diagnosis; 4: Computer determined confidence in diagnosis probability. Obtained with permission from Dr. Michael Byrne (Division of Gastroenterology at Vancouver General Hospital and UBC).