Capsule endoscopy of the small bowel

McAlindon, Mark E.; Ching, Hey-Long; Yung, Diana E; Sidhu, Reena; Koulaouzidis, Anastasios

doi:10.21037/atm.2016.09.18

Cited by 46 publications

(35 citation statements)

References 64 publications

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“…The CE plays an important role in diagnosing such small-bowel disease as obscure bleeding [28][29][30] , Crohn's disease 31 , and tumors, but the gastroduodenal retention remains to be a problem in some patients. If the CE passes through the stomach and duodenal bulb into the DSD too slowly, the medical staff have to be continuously monitoring even for hours, and they have to discern whether the long retention of the CE occurred in the stomach for fear of the battery exhausted.…”

Section: Discussionmentioning

confidence: 99%

A pilot trial of Convolution Neural Network for automatic retention-monitoring of capsule endoscopes in the stomach and duodenal bulb

Gan

Liu

Yang

et al. 2020

Sci Rep

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the retention of a capsule endoscope (ce) in the stomach and the duodenal bulb during the examination is a troublesome problem, which can make the medical staff spend several hours observing whether the ce enters the descending segment of the duodenum (DSD). this paper investigated and evaluated the convolution neural network (cnn) for automatic retention-monitoring of the ce in the stomach or the duodenal bulb. A trained CNN system based on 180,000 CE images of the DSD, stomach, and duodenal bulb was used to assess its recognition of the accuracy by calculating the area under the receiver operating characteristic curve (ROC-AUC), sensitivity and specificity. The AUC for distinguishing the DSD was 0.984. The sensitivity, specificity, positive predictive value, and negative predictive value of the CNN were 97.8%, 96.0%, 96.1% and 97.8%, respectively, at a cut-off value of 0.42 for the probability score. the deviated rate of the time into the DSD marked by the cnn at less than ±8 min was 95.7% (P < 0.01). these results indicate that the cnn for automatic retention-monitoring of the ce in the stomach or the duodenal bulb can be used as an efficient auxiliary measure in the clinical practice.Capsule endoscope (CE) has become one of the best diagnostic tools for diagnosing small intestinal diseases because of its painless and non-invasive nature 1-4 , but it has some weaknesses. One of these is that after the CE is swallowed, its movement in the digestive tract is completely dependent on gastrointestinal motility, especially the gastroduodenal emptying velocity. If the gastroduodenal emptying velocity is too slow, the CE can become stagnated in the stomach or duodenal bulb for several hours, which can cause energy loss of the built-in battery. Thus, examination of the whole small intestine may not be finished. How to predict the residence time of the CE in the stomach or duodenal bulb has not been solved, and medical staff may have to wait for several hours in the examination room to monitor whether the CE enters the descending segment of the duodenum (DSD) 5,6 . If the CE cannot enter the DSD in 2-3 h, some interventions, e.g., drugs or gastroscopy, can be used to push the CE forward into the DSD 7 , which is a tedious and boring task, especially for some patients who have to undergo the CE examination at the same time, which could greatly increase the monitoring workload for the medical staff.Artificial intelligence (AI), as a new technique, has been developed in the recent years, which includes Autoencoder 8 , Deep Belief Network 9 , Convolution Neural Network (CNN) 10 , and Deep Residual Network 11 , and they have been used in the medical image analysis and have been proved to be effective in some medical diagnostic fields, such as pulmonary nodules 12 , breast lesions 13,14 , skin cancer 15 , early gastrointestinal cancers 16,17 , polyps 18 , and small-bowel diseases [19][20][21][22][23] .Of those techniques, the CNN 24 is a type of deep learning mode 25-27 that requires the preprocessing of the image data inputt...

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Section: Discussionmentioning

confidence: 99%

A pilot trial of Convolution Neural Network for automatic retention-monitoring of capsule endoscopes in the stomach and duodenal bulb

Gan

Liu

Yang

et al. 2020

Sci Rep

View full text Add to dashboard Cite

show abstract

“…VCE is now an established method in the work-up of small bowel diseases (1,2). VCE is a safe technique and accumulating data from large studies indicate that the incidence of intestinal retention is less than 2%.…”

Section: Discussionmentioning

confidence: 99%

“…Video capsule endoscopy (VCE) has emerged as the method of choice for visualizing the small bowel mucosa (1,2). Beside the work-up of obscure gastrointestinal bleeding (OGIB) and in the evaluation of intestinal lesions related to non-steroidal anti-inflammatory drugs (NSAIDs) and celiac disease, VCE is particularly useful in the diagnosis and surveillance of Crohn's disease (3)(4)(5)(6)(7)(8).…”

Section: Introductionmentioning

confidence: 99%

Colonic obstruction caused by video capsule entrapment in a metal stent

Tóth¹,

Marthinsen²,

Bergström³

et al. 2017

Ann. Transl. Med.

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Video capsule endoscopy (VCE) has become the method of choice for visualizing the small bowel mucosa and is generally considered to be a safe method. Although uncommon, the most feared complication of VCE is capsule retention that can potentially lead to life-threatening bowel obstruction. Herein, we present for the first time a case of capsule retention in a colonic stent. The patient had known Crohn's disease with colonic involvement and underwent an uneventful but incomplete small bowel VCE for assessment of disease activity and extension for optimizing medical treatment. Five months later, the patient presented with intestinal obstruction due to a Crohn's-stricture in the sigmoid colon, which was successfully decompressed with a self-expandable metal stent. Nonetheless, two days later the patient showed signs of bowel obstruction again and abdominal X-ray showed that the capsule was trapped in the metal stent in the sigmoid colon. Subsequently, emergency surgery was performed and the patient fully recovered. Intestinal capsule retention necessitating interventional removal is rare. This report describes a unique case of capsule retention in a colonic metal stent and highlights the potential risk of performing capsule endoscopy examinations in patients with gastrointestinal stents.

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“…[2][3][4][5][6][7][8][9][10][11] Usually, CE analysis requires, on average, 30-40 minutes of intense focus and attention. 12 Maintaining a high level of concentration while viewing 60 000 images of the same organ per physician is difficult, particularly because this form of endoscopy is completely passive, with no additional stimulus provided by the need to manipulate an instrument or interact with patients or other staff.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence using a convolutional neural network for automatic detection of small‐bowel angioectasia in capsule endoscopy images

Tsuboi

Oka

Aoyama³

et al. 2019

Digestive Endoscopy

133

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Background and Aim Although small‐bowel angioectasia is reported as the most common cause of bleeding in patients and frequently diagnosed by capsule endoscopy (CE) in patients with obscure gastrointestinal bleeding, a computer‐aided detection method has not been established. We developed an artificial intelligence system with deep learning that can automatically detect small‐bowel angioectasia in CE images. Methods We trained a deep convolutional neural network (CNN) system based on Single Shot Multibox Detector using 2237 CE images of angioectasia. We assessed its diagnostic accuracy by calculating the area under the receiver operating characteristic curve (ROC‐AUC), sensitivity, specificity, positive predictive value, and negative predictive value using an independent test set of 10 488 small‐bowel images, including 488 images of small‐bowel angioectasia. Results The AUC to detect angioectasia was 0.998. Sensitivity, specificity, positive predictive value, and negative predictive value of CNN were 98.8%, 98.4%, 75.4%, and 99.9%, respectively, at a cut‐off value of 0.36 for the probability score. Conclusions We developed and validated a new system based on CNN to automatically detect angioectasia in CE images. This may be well applicable to daily clinical practice to reduce the burden of physicians as well as to reduce oversight.

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Capsule endoscopy of the small bowel

Cited by 46 publications

References 64 publications

A pilot trial of Convolution Neural Network for automatic retention-monitoring of capsule endoscopes in the stomach and duodenal bulb

A pilot trial of Convolution Neural Network for automatic retention-monitoring of capsule endoscopes in the stomach and duodenal bulb

Colonic obstruction caused by video capsule entrapment in a metal stent

Artificial intelligence using a convolutional neural network for automatic detection of small‐bowel angioectasia in capsule endoscopy images

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