Computer-based classification of small colorectal polyps by using narrow-band imaging with optical magnification

Groß, Sebastian; Trautwein, Christian; Behrens, Axel; Winograd, Ron; Palm, Stephan; Lutz, Holger H.; Schirin-Sokhan, Ramin; Hecker, Hartmut; Aach, Til; Tischendorf, Jens J. W.

doi:10.1016/j.gie.2011.08.001

Cited by 126 publications

(109 citation statements)

References 20 publications

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“…As a result, this diagnostic system has the potential to avoid the complications related to unnecessary endoscopic procedures. 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] .…”

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

186

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

186

111

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“…Machine algorithms based on magnification NBI vascular features differentiated neoplastic and non-neoplastic lesions with sensitivities ranging from 85% to 93%. 41,42 Retrospective DL studies for the diagnosis of diminutive polyps has surpassed PIVI-2 criteria with sensitivities ranging from 96% to 98%, specificity 78-83% and NPV 91.5-97% in two studies. 43,44 Prospective evaluation with real-time magnification NBI also exceeded the PIVI-2 criteria with NPV 93.3%, accuracy of 93.2% and was also able to predict surveillance strategy in exact agreement with histology in 92.7%.…”

Section: Narrow Band Imagingmentioning

confidence: 99%

The future of endoscopy: Advances in endoscopic image innovations

Gulati

Patel

Emmanuel

et al. 2019

Digestive Endoscopy

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The latest state of the art technological innovations have led to a palpable progression in endoscopic imaging and may facilitate standardisation of practice. One of the most rapidly evolving modalities is artificial intelligence with recent studies providing real-time diagnoses and encouraging results in the first randomised trials to conventional endoscopic imaging. Advances in functional hypoxia imaging offer novel opportunities to be used to detect neoplasia and the assessment of colitis. Three-dimensional volumetric imaging provides spatial information and has shown promise in the increased detection of small polyps. Studies to date of self-propelling colonoscopes demonstrate an increased caecal intubation rate and possibly offer patients a more comfortable procedure. Further development in robotic technology has introduced ex vivo automated locomotor upper gastrointestinal and small bowel capsule devices. Eye-tracking has the potential to revolutionise endoscopic training through the identification of differences in experts and non-expert endoscopist as trainable parameters. In this review, we discuss the latest innovations of all these technologies and provide perspective into the exciting future of diagnostic luminal endoscopy.

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“…Moreover, the overall accuracy in prediction of histology es markedly influenced by expertise in NBI interpretation, as has been shown in a study performed in a non academic setting in which sensitivity for high-confidence prediction was 77% and specificity 78% [73]. Experts have been shown to perform better than non-experts and with a higher interobserver agreement [74]. Fortunately, NBI interpretation of histology can be easily learned.…”

Section: Prediction Of Histology Of Diminutive Polypsmentioning

confidence: 94%

In vivo Optical Diagnosis of Polyp Histology: Can We Omit Pathological Examination of Diminutive Polyps?

Bustamante-Balén¹

2013

Colonoscopy and Colorectal Cancer Screening - Future Directions

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Computer-based classification of small colorectal polyps by using narrow-band imaging with optical magnification

Cited by 126 publications

References 20 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

The future of endoscopy: Advances in endoscopic image innovations

In vivo Optical Diagnosis of Polyp Histology: Can We Omit Pathological Examination of Diminutive Polyps?

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