Impact of an automated system for endocytoscopic diagnosis of small colorectal lesions: an international web-based study

Mori, Yuichi; Kudo, Shin‐ei; Chiu, Philip Wai Yan; Singh, Rajvinder; Misawa, Masashi; Wakamura, Kunihiko; Hayashi, Takemasa; Katagiri, Atsushi; Miyachi, Hideyuki; Ishida, Fumio; Maeda, Yukihide; Inoue, Haruhiro; Nimura, Y; Oda, Masahiro; Mori, Kensaku

doi:10.1055/s-0042-113609

Cited by 97 publications

(86 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…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: 99%

“…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%

See 2 more Smart Citations

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

Komeda

Handa²,

Watanabe³

et al. 2017

Oncology

181

111

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Komeda

Handa²,

Watanabe³

et al. 2017

Oncology

181

111

View full text Add to dashboard Cite

show abstract

“…In recent years, the innovation of AI technology has been accelerating remarkably. In the field of gastroenterology, Mori et al 18 reported a CAD system for the diagnosis of small colorectal polyps. Misawa et al 19 reported a CAD system for the detection of colorectal polyps.…”

Section: Discussionmentioning

confidence: 99%

Potential of automatic diagnosis system with linked color imaging for diagnosis of Helicobacter pylori infection

Yasuda

Hiroyasu

Hiwa

et al. 2019

Digestive Endoscopy

View full text Add to dashboard Cite

Background and Aim It is necessary to establish universal methods for endoscopic diagnosis of Helicobacter pylori (HP) infection, such as computer‐aided diagnosis. In the present study, we propose a multistage diagnosis algorithm for HP infection. Methods The aims of this study are to: (i) to construct an interpretable automatic diagnostic system using a support vector machine for HP infection; and (ii) to compare the diagnosis capability of our artificial intelligence (AI) system with that of endoscopists. Presence of an HP infection determined through linked color imaging (LCI) was learned through machine learning. Trained classifiers automatically diagnosed HP‐positive and ‐negative patients examined using LCI. We retrospectively analyzed the new images from 105 consecutive patients; 42 were HP positive, 46 were post‐eradication, and 17 were uninfected. Five endoscopic images per case taken from different areas were read into the AI system, and used in the HP diagnosis. Results Accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the diagnosis of HP infection using the AI system were 87.6%, 90.4%, 85.7%, 80.9%, and 93.1%, respectively. Accuracy of the AI system was higher than that of an inexperienced doctor, but there was no significant difference between the diagnosis of experienced physicians and the AI system. Conclusions The AI system can diagnose an HP infection with significant accuracy. There remains room for improvement, particularly for the diagnosis of post‐eradication patients. By learning more images and considering a diagnosis algorithm for post‐eradication patients, our new AI system will provide diagnostic support, particularly to inexperienced physicians.

show abstract

“…44 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. 45,46 They also developed a more userfriendly CAD system based on endocytoscopy combined with NBI that required no prior staining. 47 This system focused on the analysis of microvessels on the surface of polyps and provided an overall accuracy of 90.0%.…”

Section: Endocytoscopymentioning

confidence: 99%

Artificial intelligence and colonoscopy: Current status and future perspectives

Kudo

Mori

Misawa

et al. 2019

Digestive Endoscopy

Self Cite

117

View full text Add to dashboard Cite

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.

show abstract

Impact of an automated system for endocytoscopic diagnosis of small colorectal lesions: an international web-based study

Cited by 97 publications

References 25 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

Potential of automatic diagnosis system with linked color imaging for diagnosis of Helicobacter pylori infection

Artificial intelligence and colonoscopy: Current status and future perspectives

Contact Info

Product

Resources

About