Automatic detection and classification of protruding lesions in wireless capsule endoscopy images based on a deep convolutional neural network

Saito, Hiroaki; Aoki, Tomonori; Aoyama, Kazuharu; Kato, Yusuke; Tsuboi, Akiyoshi; Yamada, Atsuo; Fujishiro, Mitsuhiro; Oka, Shiro; Ishihara, Soichiro; Matsuda, Tomoki; Nakahori, Masato; Tanaka, Shinji; Koike, Kazuhiko; Tada, Takeshi

doi:10.1016/j.gie.2020.01.054

Cited by 140 publications

(98 citation statements)

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“…To detect protruding lesions and classify them into polyps, nodules, epithelial tumors, submucosal tumors, and venous structures, Saito et al developed a CNN model using 30,584 CE images from 292 patients. 31 When this CNN model ana- SB abnormal images included inflammation, ulcer, polyps, lymphangiectasia, bleeding, vascular disease, protruding lesion, lymphatic follicular hyperplasia, diverticulum, parasite, and other.…”

Section: Application Of Artificial Intelligence In Capsule Endoscopymentioning

confidence: 99%

“…To detect protruding lesions and classify them into polyps, nodules, epithelial tumors, submucosal tumors, and venous structures, Saito et al developed a CNN model using 30,584 CE images from 292 patients [ 31 ]. When this CNN model analyzed 17,507 test images (including 7,507 images of protruding lesions from 73 patients), the AUROC was 0.91 and the sensitivity and specificity were 90.7% and 79.8%, respectively.…”

Section: Artificial Intelligence In Capsule Endoscopymentioning

confidence: 99%

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The Future of Capsule Endoscopy: The Role of Artificial Intelligence and Other Technical Advancements

Yang

2020

Clin Endosc

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Section: Application Of Artificial Intelligence In Capsule Endoscopymentioning

confidence: 99%

“…To detect protruding lesions and classify them into polyps, nodules, epithelial tumors, submucosal tumors, and venous structures, Saito et al developed a CNN model using 30,584 CE images from 292 patients [ 31 ]. When this CNN model analyzed 17,507 test images (including 7,507 images of protruding lesions from 73 patients), the AUROC was 0.91 and the sensitivity and specificity were 90.7% and 79.8%, respectively.…”

Section: Artificial Intelligence In Capsule Endoscopymentioning

confidence: 99%

The Future of Capsule Endoscopy: The Role of Artificial Intelligence and Other Technical Advancements

Yang

2020

Clin Endosc

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“…Previous studies based on still frames have demonstrated the high diagnostic performances of handcrafted computerized algorithms at assessing SBCE cleanliness [11,13]. It has since been demonstrated that NN-based algorithms can significantly reduce the reading time of SBCE [7][8][9]14], but they have not been used yet to rate SB cleanliness. Our work demonstrates a valuable tool for better standardization and quality reporting in CE…”

Section: Accepted M Manuscriptmentioning

confidence: 99%

A neural network-based algorithm for assessing the cleanliness of small bowel during capsule endoscopy

et al. 2020

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Background and Aims. Cleanliness scores in small bowel (SB) capsule endoscopy (CE) have poor reproducibility. The aim of this study was to evaluate a neural network (NN)-based algorithm for automated assessment of the SB cleanliness during CE. Methods: First, 600 normal third-generation SBCE still frames were categorized as “adequate” or “inadequate” in terms of cleanliness by three expert readers, according to a 10-point scale and served as a training database. Then, 156 third-generation SBCE recordings were categorized in a consensual manner as “adequate” or “inadequate” in terms of cleanliness. This testing database was split into two independent 78-video subsets for the tuning and evaluation of the algorithm. Results: Using a threshold of 79% adequate still frames per video to achieve the best performance, the algorithm yielded a sensitivity of 90.3%, a specificity of 83.3%, and an accuracy of 89.7%. The reproducibility was perfect. The mean calculation time per video was 3 ± 1 minutes. Conclusion: This NN-based algorithm allowing automatic assessment of SB cleanliness during CE was highly sensitive and paves the way for automated, standardized SBCE reports.

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“…The sensitivity and specificity of the convolutional neural network were 90.7% (95% confidence interval, 90.0%-91.4%) and 79.8% (95% confidence interval, 79.0%-80.6%). 8 This study has several strengths. To our knowledge, it is the first of its kind to specifically evaluate the role of computer-aided detection of polypoid lesions in the small bowel; in addition, the authors show the beginnings of accurate in-situ computer-aided diagnosis during CE, although the concordance of diagnosis between the AI algorithm and the expert endoscopist reader varied greatly depending on the lesion type, with a 42% concordance for polyps and an 82% to 83% concordance for nodules and epithelial tumors.…”

mentioning

confidence: 98%

“…Copyright ª 2020 by the American Society for Gastrointestinal Endoscopy 0016-5107/$36.00 https://doi.org/10.1016/j.gie.2020.03.3851 (or measurable efficiency benefits for physicians). In any case, the study by Saito et al 8 is a necessary first step along this path.…”

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confidence: 99%

Deploying artificial intelligence to find the needle in the haystack: deep learning for video capsule endoscopy

Brown

Berzin

2020

Gastrointestinal Endoscopy

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Wireless video capsule endoscopy (CE) is a noninvasive endoscopic technique developed in the mid 1990s that has been used to visualize a wide spectrum of pathologic conditions of the small intestine, from inflammatory enteropathies such as inflammatory bowel disease and celiac disease to suspected small-bowel bleeding, to polyposis syndromes. Recent clinical practice guidelines put forth by the American Gastroenterological Association have recommended CE in a growing number of clinical scenarios, ranging from GI bleeding to polyposis syndromes. 1 Reviewing CE results in clinical practice, however, can be challenging and labor-intensive. A typical video capsule records 2 images per second and 50,000 to 60,000 still images in a single video recording. Finding a subtle abnormality among this number of images can feel akin to looking for a needle in the proverbial haystack, with average physicians' reading times ranging from 45 to 120 minutes. 2 Several strategies have been used that aim to reduce physicians' reading times and increase accuracy. These strategies have included using an endoscopy nurse as a preliminary reader 3 and using software designed to highlight images of potential interest. 2 Artificial intelligence (AI), specifically deep learning, may offer a promising approach to streamlining the CE reading process. Over the past decade, machine learning and deep learning (a subset of machine learning) have been applied broadly to GI endoscopy, in applications ranging from colon polyp detection to dysplasia detection in Barrett's esophagus. 4 In the past year, several important studies have examined the role of deep learning in the automatic detection of erosions, ulcerations, and angioectasias in CE. 5-7 In this issue of Gastrointestinal Endoscopy, Saito et al 8 examine the role of a deep learning algorithm in the detection of protruding lesions during wireless CE. The authors developed a deep learning algorithm based on a single-shot multibox detector deep convolutional neural network to specifically detect protruding lesions in the small bowel during CE. The algorithm was developed using 30,584 images of protruding lesions from 292 patients from 3 hospitals in Japan: Sendai Kousei Hospital,

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Automatic detection and classification of protruding lesions in wireless capsule endoscopy images based on a deep convolutional neural network

Cited by 140 publications

References 18 publications

The Future of Capsule Endoscopy: The Role of Artificial Intelligence and Other Technical Advancements

The Future of Capsule Endoscopy: The Role of Artificial Intelligence and Other Technical Advancements

A neural network-based algorithm for assessing the cleanliness of small bowel during capsule endoscopy

Deploying artificial intelligence to find the needle in the haystack: deep learning for video capsule endoscopy

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