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

Mori, Yuichi; Kudo, Shin‐ei; Wakamura, Kunihiko; Misawa, Masashi; Ogawa, Yushi; Kutsukawa, Makoto; Kudo, Toyoki; Hayashi, Takemasa; Miyachi, Hideyuki; Ishida, Fumio; Inoue, Haruhiro

doi:10.1016/j.gie.2014.09.008

Cited by 131 publications

(104 citation statements)

References 32 publications

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“…Thus, a wide variety of CAD systems are available at present. Importantly, the diagnostic accuracy of these novel methods is higher than that achieved by endoscopic diagnosis by less-experienced endoscopists [9][10][11] .…”

Section: Discussionmentioning

confidence: 98%

“…These real-time CAD systems are operated during the actual endoscopic observation. Moreover, Japanese researchers have successfully developed an automated CAD using endocytoscopy based on automated extraction of ultramagnified nuclear features followed by machine-learning analysis from the same group [9][10][11] . Thus, a wide variety of CAD systems are available at present.…”

Section: Discussionmentioning

confidence: 99%

“…Previous studies have reported the usefulness of CAD for colon polyps. CAD for colon polyps has been suggested as a particularly useful tool for trainee colonoscopists, as the use of a CAD system helps avoid the complications associated with endoscopic resections [9][10][11] . Moreover, the higher rate of correct diagnoses using a CAD system also suggests the potential for this diagnostic tool to improve the quality of colonoscopic examinations.…”

Section: Introductionmentioning

confidence: 99%

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

Komeda

Handa²,

Watanabe³

et al. 2017

Oncology

176

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: 98%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Komeda

Handa²,

Watanabe³

et al. 2017

Oncology

176

111

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“…Recently, the computer‐aided diagnosis (CAD) system is gaining attention as an attractive endoscopic diagnostic tool. Some investigators reported that the use of CAD is effective in the management of diminutive and small colorectal polyps . However, participants think that some diminutive or small lesions can be diagnosed only by using WLI.…”

Section: Discussionmentioning

confidence: 99%

Endoscopic management of colorectal tumors less than 10 mm in size: Current status and future perspectives in Japan from a questionnaire survey

Uraoka

Oka

Ichihara

et al. 2018

Digestive Endoscopy

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“…Their first model was based on automated extraction of nuclear areas stained with methylene blue followed by quantitative analysis of six nuclear features. Their accuracy for identifying neoplastic changes was 89.2% . In their follow‐up study, reported in 2016 and 2018, the diagnostic algorithm was improved by adding texture analysis for feature extraction and a support vector machine as a classifier, thereby producing an output image of the predicted pathology along with the probability of the diagnosis .…”

Section: Automated Polyp Characterizationmentioning

confidence: 99%

Artificial intelligence and colonoscopy: Current status and future perspectives

Kudo

Mori

Misawa

et al. 2019

Digestive Endoscopy

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110

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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.

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

Abstract: EC-CAD provides fully automated instant classification of colorectal polyps with excellent sensitivity, accuracy, and objectivity. Thus, it can be a powerful tool for facilitating decision making during routine colonoscopy.

Cited by 131 publications

References 32 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

Endoscopic management of colorectal tumors less than 10 mm in size: Current status and future perspectives in Japan from a questionnaire survey

Artificial intelligence and colonoscopy: Current status and future perspectives

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