Artificial intelligence and colonoscopy: Current status and future perspectives

Abstract

Background and Aim Application of artificial intelligence in medicine is now attracting substantial attention. In the field of gastrointestinal endoscopy, computer‐aided diagnosis (CAD) for colonoscopy is the most investigated area, although it is still in the preclinical phase. Because colonoscopy is carried out by humans, it is inherently an imperfect procedure. CAD assistance is expected to improve its quality regarding automated polyp detection and characterization (i.e. predicting the polyp's pathology). It could help prevent endoscopists from missing polyps as well as provide a precise optical diagnosis for those detected. Ultimately, these functions that CAD provides could produce a higher adenoma detection rate and reduce the cost of polypectomy for hyperplastic polyps. Methods and Results Currently, research on automated polyp detection has been limited to experimental assessments using an algorithm based on ex vivo videos or static images. Performance for clinical use was reported to have >90% sensitivity with acceptable specificity. In contrast, research on automated polyp characterization seems to surpass that for polyp detection. Prospective studies of in vivo use of artificial intelligence technologies have been reported by several groups, some of which showed a >90% negative predictive value for differentiating diminutive (≤5 mm) rectosigmoid adenomas, which exceeded the threshold for optical biopsy. Conclusion We introduce the potential of using CAD for colonoscopy and describe the most recent conditions for regulatory approval for artificial intelligence‐assisted medical devices.

Diagnostic performance of artificial intelligence to identify deeply invasive colorectal cancer on non-magnified plain endoscopic images

“…With progress in computer science, artificial intelligence (AI) is being increasingly applied to interpretation of medical images [11,12]. In colonoscopy, computer-aided diagnosis (CAD) systems for polyp detection or polyp characterization have been developed, and several clinical trials have validated the function of CAD systems [13][14][15].…”

Next generation of endoscopy: Harmony with artificial intelligence and robotic‐assisted devices

“…The CNN convolves the images that are reduced in size for further max pooling and threshold-based activation, thus avoiding the risk of overfitting and providing an attractive analytic model for GI endoscopy. 2 Computer-aided diagnosis (CAD) in colonoscopy is garnering increased investigation. [2][3][4][5][6] AI technology is expected to have two major roles in colonoscopy practice 5 -automated polyp detection (CADe) and histopathology characterization (CADx).…”

“…2 Computer-aided diagnosis (CAD) in colonoscopy is garnering increased investigation. [2][3][4][5][6] AI technology is expected to have two major roles in colonoscopy practice 5 -automated polyp detection (CADe) and histopathology characterization (CADx). To be maximally effective, CADe should have a high sensitivity for identification with a low rate of false positives and should maintain faster processing speeds to be applicable in real-time during colonoscopy.…”

“…Compared with CADe, under WLI endoscopy, advanced optical technologies including narrowband imaging (NBI) and endocytoscopy have been available for CADx in realtime performance. 2,4,6 Mori et al 4 conducted a prospective study for a diagnose-and-leave strategy to identify diminutive, non-neoplastic recto-sigmoid polyps during on-going endocytoscopy, which provides 500-fold magnification. However, similar to the prior CNN application studies in the GI endoscopic field, the CAD-assisted endocytoscopy employed a creation of software referred to as EndoBrain (Olympus, Tokyo, Japan).…”

“…However, similar to the prior CNN application studies in the GI endoscopic field, the CAD-assisted endocytoscopy employed a creation of software referred to as EndoBrain (Olympus, Tokyo, Japan). 2,4 Another challenge of research is the application of the DL algorithm to the detection of proximal colonic lesions. In this regard, sessile serrated lesions should be the target of interest, because they are known to be associated with an alternative CRC pathway.…”