A neural network algorithm for detection of GI angiectasia during small-bowel capsule endoscopy

Leenhardt, Romain; Vasseur, Pauline; Li, Cynthia; Saurin, Jean Christophe; Rahmi, Gabriel; Cholet, Franck; Becq, Aymeric; Marteau, Philippe; Histace, Aymeric; Dray, Xavier; Sacher‐Huvelin, Sylvie; Mesli, Farida; Leandri, Chloé; Nion–Larmurier, Isabelle; Lecleire, Stéphane; Gérard, Romain; Duburque, Clotilde; Vanbiervliet, Geoffroy; Amiot, Xavier; Mouel, Jean Philippe Le; Delvaux, Michel; Jacob, P; Simon-Shane, Camille; Romain, Olivier

doi:10.1016/j.gie.2018.06.036

Cited by 179 publications

(140 citation statements)

References 10 publications

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“…For example, SBCE devices will be advanced further, including high‐frame‐rate imaging, full spherical imaging and high‐resolution imaging. With regard to CE software, artificial intelligence and computer‐assisted diagnosis will be applied to reduce the burden for CE readers . Hence, the need to read CE videos for long periods will be removed.…”

Section: Future Perspectivesmentioning

confidence: 99%

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Capsule endoscopy for small‐intestinal disorders: Current status

Hosoe

Takabayashi

Ogata

et al. 2019

Digestive Endoscopy

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Small‐bowel capsule endoscopy (SBCE) is used widely because of its non‐invasive and patient‐friendly nature. SBCE can visualize entire small‐intestinal mucosa and facilitate detection of small‐intestinal abnormalities. In this review article, we focus on the current status of SBCE. Several platforms for SBCE are available worldwide. Third‐generation SBCE (PillCam® SB3) has a high‐resolution camera equipped with an adaptive frame rate system. Several software modes have been developed to reduce the reading time for capsule endoscopy and to minimize the possibility of missing lesions. The main complication of SBCE is capsule retention. Thus, the main contraindication for SBCE is known or suspected gastrointestinal obstruction unless intestinal patency is proven. Possible indications for SBCE are obscure gastrointestinal bleeding, Crohn's disease, small‐intestinal polyps and tumors, and celiac disease. Colon capsule endoscopy (CCE) can observe inflamed colonic mucosa non‐invasively, and allows for the continuous and non‐invasive observation of the entire intestinal tract (pan‐endoscopy). Recently, application of CCE as pan‐enteric endoscopy for inflammatory bowel diseases (including Crohn's disease) has been reported. In the near future, reading for CE will be assisted by artificial intelligence, and reading CE videos for long periods will not be required.

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Section: Future Perspectivesmentioning

confidence: 99%

“…With regard to CE software, artificial intelligence and computerassisted diagnosis will be applied to reduce the burden for CE readers. 106 Hence, the need to read CE videos for long periods will be removed.…”

Section: Future Perspectivesmentioning

confidence: 99%

Capsule endoscopy for small‐intestinal disorders: Current status

Hosoe

Takabayashi

Ogata

et al. 2019

Digestive Endoscopy

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“…This algorithm yielded a sensitivity of 90% and a specificity of 97%. Recently, Leenhardt et al 25 reported the usefulness of the CNN algorithm used for small-bowel detection in still frames. They reported the sensitivity, specificity, PPV, and NPV as 100%, 96%, 96%, and 100%, respectively.…”

Section: Haracteristics Of Patients With Small-bowelmentioning

confidence: 99%

“…This technique can extract specific features and quantities from a training data set using multiple network layers (convolutional layers and pooling layers) and a back‐propagation algorithm. Reports of CNN for CE images are scarce, and there are few studies on CNN for detecting angioectasia in CE images . However, CNN has been shown to be useful for endoscopy and has the potential to be an outstanding detection method that may overcome the above problem.…”

Section: Introductionmentioning

confidence: 99%

“…Reports of CNN for CE images are scarce, 22,23 and there are few studies on CNN for detecting angioectasia in CE images. 24,25 However, CNN has been shown to be useful for endoscopy and has the potential to be an outstanding detection method that may overcome the above problem. In the present study, we aimed to develop and validate a CNN-based program for the automatic detection of angioectasia in small-bowel images of CE.…”

Section: Introductionmentioning

confidence: 99%

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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

129

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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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