Computer-aided system for predicting the histology of colorectal tumors by using narrow-band imaging magnifying colonoscopy (with video)

Takemura, Y; Yoshida, Shigeto; Tanaka, Shinji; Kawase, Rie; Onji, Keiichi; Oka, Shiro; Tamaki, Toru; Raytchev, Bisser; Kaneda, Kazufumi; Yoshihara, Masaharu; Chayama, Kazuaki

doi:10.1016/j.gie.2011.08.051

Cited by 102 publications

(84 citation statements)

References 11 publications

(18 reference statements)

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“…Initial studies report the usefulness of CAD for colorectal polyps using still images obtained from magnifying NBI observations [12,13] . As opposed to CAD using still images, 2 Japanese groups have developed CAD with real-time endoscopy [14,15] . These real-time CAD systems are operated during the actual endoscopic observation.…”

Section: Discussionmentioning

confidence: 99%

Computer-Aided Diagnosis Based on Convolutional Neural Network System for Colorectal Polyp Classification: Preliminary Experience

Komeda

Handa²,

Watanabe³

et al. 2017

Oncology

181

111

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Background and Aim: Computer-aided diagnosis (CAD) is becoming a next-generation tool for the diagnosis of human disease. CAD for colon polyps has been suggested as a particularly useful tool for trainee colonoscopists, as the use of a CAD system avoids the complications associated with endoscopic resections. In addition to conventional CAD, a convolutional neural network (CNN) system utilizing artificial intelligence (AI) has been developing rapidly over the past 5 years. We attempted to generate a unique CNN-CAD system with an AI function that studied endoscopic images extracted from movies obtained with colonoscopes used in routine examinations. Here, we report our preliminary results of this novel CNN-CAD system for the diagnosis of colon polyps. Methods: A total of 1,200 images from cases of colonoscopy performed between January 2010 and December 2016 at Kindai University Hospital were used. These images were extracted from the video of actual endoscopic examinations. Additional video images from 10 cases of unlearned processes were retrospectively assessed in a pilot study. They were simply diagnosed as either an adenomatous or nonadenomatous polyp. Results: The number of images used by AI to learn to distinguish adenomatous from nonadenomatous was 1,200:600. These images were extracted from the videos of actual endoscopic examinations. The size of each image was adjusted to 256 × 256 pixels. A 10-hold cross-validation was carried out. The accuracy of the 10-hold cross-validation is 0.751, where the accuracy is the ratio of the number of correct answers over the number of all the answers produced by the CNN. The decisions by the CNN were correct in 7 of 10 cases. Conclusion: A CNN-CAD system using routine colonoscopy might be useful for the rapid diagnosis of colorectal polyp classification. Further prospective studies in an in vivo setting are required to confirm the effectiveness of a CNN-CAD system in routine colonoscopy.

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Section: Discussionmentioning

confidence: 99%

Computer-Aided Diagnosis Based on Convolutional Neural Network System for Colorectal Polyp Classification: Preliminary Experience

Komeda

Handa²,

Watanabe³

et al. 2017

Oncology

181

111

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“…Actually, endoscopists usually take a lot of pictures of a polyp for finding a good image of the polyp for a paper-based report with medical evidences (Aabakken, 2009). In the clinical workflow, it would be helpful if an objective diagnosis by a computer-aided system as a kind of second opinion could be provided onto the video monitor directly or for pictures taken during the examination (Takemura et al, 2012). This can be used in two ways.…”

Section: Introductionmentioning

confidence: 99%

Computer-aided colorectal tumor classification in NBI endoscopy using local features

Tamaki

Yoshimuta

Kawakami

et al. 2013

Medical Image Analysis

Self Cite

128

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“…For computer analysis of vascularization features on NBI, Tischendorf et al 28 reported a sensitivity of 93.8% and a specificity of 61.2%, Gross et al 29 a sensitivity of 95.0% and a specificity of 90.3%, and Takemura et al a sensitivity of 97.8% and a specificity of 97.9%. 30 All of these are very high diagnostic performances; however, these diagnostic systems were not fully automated but required manual operation during image acquisition and processing by the system. In this regard, our newly developed EC-CAD provides fully automated diagnoses, requires no manual operation, and facilitates real-time decision making during endoscopy.…”

Section: Discussionmentioning

confidence: 99%

Novel computer-aided diagnostic system for colorectal lesions by using endocytoscopy (with videos)

Mori¹,

Kudo²,

Wakamura³

et al. 2015

Gastrointestinal Endoscopy

134

106

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Computer-aided system for predicting the histology of colorectal tumors by using narrow-band imaging magnifying colonoscopy (with video)

Cited by 102 publications

References 11 publications

Computer-Aided Diagnosis Based on Convolutional Neural Network System for Colorectal Polyp Classification: Preliminary Experience

Computer-Aided Diagnosis Based on Convolutional Neural Network System for Colorectal Polyp Classification: Preliminary Experience

Computer-aided colorectal tumor classification in NBI endoscopy using local features

Novel computer-aided diagnostic system for colorectal lesions by using endocytoscopy (with videos)

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