Development of a real-time endoscopic image diagnosis support system using deep learning technology in colonoscopy

Abstract

Gaps in colonoscopy skills among endoscopists, primarily due to experience, have been identified, and solutions are critically needed. Hence, the development of a real-time robust detection system for colorectal neoplasms is considered to significantly reduce the risk of missed lesions during colonoscopy. Here, we develop an artificial intelligence (AI) system that automatically detects early signs of colorectal cancer during colonoscopy; the AI system shows the sensitivity and specificity are 97.3% (95% confidence interval [CI] = 95.9%–98.4%) and 99.0% (95% CI = 98.6%–99.2%), respectively, and the area under the curve is 0.975 (95% CI = 0.964–0.986) in the validation set. Moreover, the sensitivities are 98.0% (95% CI = 96.6%–98.8%) in the polypoid subgroup and 93.7% (95% CI = 87.6%–96.9%) in the non-polypoid subgroup; To accelerate the detection, tensor metrics in the trained model was decomposed, and the system can predict cancerous regions 21.9 ms/image on average. These findings suggest that the system is sufficient to support endoscopists in the high detection against non-polypoid lesions, which are frequently missed by optical colonoscopy. This AI system can alert endoscopists in real-time to avoid missing abnormalities such as non-polypoid polyps during colonoscopy, improving the early detection of this disease.

Application of Massive Parallel Deep Learning Algorithm in the Prediction of Colorectal Carcinogenesis of Familial Polyposis

“…The presence of censored data predicts events that may not occur during the observation period. Data whose observed survival time is less than or equal to the true survival time is called right-censored data [34]. The research focus of this paper is to design an algorithm based on deep learning to handle right-censored data [35].…”

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

“…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).…”

“…Consecutively, their AI program has been learning about not only prominent but subtle lesions including slightly elevated and depressed lesions through the right and left colon and rectum, and are slated to start clinical trials using the AI model. 6 Larger prospective multicenter trials are required to be conducted in diverse settings of hospitals, including endoscopists varying from non-experts to expert contributors, to provide such promising data in representative high volume centers. 3,4,6 Currently, a facet of a recognized caveat of CNN is its black box nature where classifications of the DL algorithm are not readily apparent.…”

“…6 Larger prospective multicenter trials are required to be conducted in diverse settings of hospitals, including endoscopists varying from non-experts to expert contributors, to provide such promising data in representative high volume centers. 3,4,6 Currently, a facet of a recognized caveat of CNN is its black box nature where classifications of the DL algorithm are not readily apparent. Nowadays, CAD-assisted endoscopy is expected to serve as a second observer, helping to detect neoplastic lesions as early as possible.…”