Improved classification and localization approach to small bowel capsule endoscopy using convolutional neural network

2020 ◽  
Author(s):  
Yunseob Hwang ◽  
Han Hee Lee ◽  
Chunghyun Park ◽  
Bayu Adhi Tama ◽  
Jin Su Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Small Bowel ◽  
Convolutional Neural Network ◽  
Capsule Endoscopy ◽  
Localization Approach

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

2020 ◽  
Vol 32 (3) ◽  
pp. 382-390 ◽  
Author(s):  
Akiyoshi Tsuboi ◽  
Shiro Oka ◽  
Kazuharu Aoyama ◽  
Hiroaki Saito ◽  
Tomonori Aoki ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Small Bowel ◽  
Convolutional Neural Network ◽  
Capsule Endoscopy ◽  
Automatic Detection

scholarly journals Automatic Detection of Small Intestinal Hookworms in Capsule Endoscopy Images Based on a Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Tao Gan ◽  
Yulin Yang ◽  
Shuaicheng Liu ◽  
Bing Zeng ◽  
Jinlin Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Small Bowel ◽  
Convolutional Neural Network ◽  
Capsule Endoscopy ◽  
High Speed ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Intelligence System ◽  
Sensitivity Specificity

Ancylostomiasis is a fairly common small bowel parasite disease identified by capsule endoscopy (CE) for which a computer-aided clinical detection method has not been established. We sought to develop an artificial intelligence system with a convolutional neural network (CNN) to automatically detect hookworms in CE images. We trained a deep CNN system based on a YOLO-V4 (You Look Only Once-Version4) detector using 11236 CE images of hookworms. We assessed its performance by calculating the area under the receiver operating characteristic curve and its sensitivity, specificity, and accuracy using an independent test set of 10,529 small-bowel images including 531 images of hookworms. The trained CNN system required 403 seconds to evaluate 10,529 test images. The area under the curve for the detection of hookworms was 0.972 (95% confidence interval (CI), 0.967-0.978). The sensitivity, specificity, and accuracy of the CNN system were 92.2%, 91.1%, and 91.2%, respectively, at a probability score cut-off of 0.485. We developed and validated a CNN-based system for detecting hookworms in CE images. By combining this high-accuracy, high-speed, and oversight-preventing system with other CNN systems, we hope it will become an important supplement for detecting intestinal abnormalities in CE images. This trial is registered with ChiCTR2000034546 (a clinical research of artificial-intelligence-aided diagnosis for hookworms in small intestine by capsule endoscope images).

scholarly journals Artificial Intelligence and Capsule Endoscopy: Automatic Detection of Small Bowel Blood Content Using a Convolutional Neural Network

2021 ◽  
pp. 1-8
Author(s):  
Miguel Mascarenhas Saraiva ◽  
Tiago Ribeiro ◽  
João Afonso ◽  
João P.S. Ferreira ◽  
Hélder Cardoso ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Small Bowel ◽  
Convolutional Neural Network ◽  
Capsule Endoscopy ◽  
Automatic Detection ◽  
Training Dataset ◽  
Validation Dataset ◽  
Accuracy And Precision ◽  
Total Pool

<b><i>Introduction:</i></b> Capsule endoscopy has revolutionized the management of patients with obscure gastrointestinal bleeding. Nevertheless, reading capsule endoscopy images is time-consuming and prone to overlooking significant lesions, thus limiting its diagnostic yield. We aimed to create a deep learning algorithm for automatic detection of blood and hematic residues in the enteric lumen in capsule endoscopy exams. <b><i>Methods:</i></b> A convolutional neural network was developed based on a total pool of 22,095 capsule endoscopy images (13,510 images containing luminal blood and 8,585 of normal mucosa or other findings). A training dataset comprising 80% of the total pool of images was defined. The performance of the network was compared to a consensus classification provided by 2 specialists in capsule endoscopy. Subsequently, we evaluated the performance of the network using an independent validation dataset (20% of total image pool), calculating its sensitivity, specificity, accuracy, and precision. <b><i>Results:</i></b> Our convolutional neural network detected blood and hematic residues in the small bowel lumen with an accuracy and precision of 98.5 and 98.7%, respectively. The sensitivity and specificity were 98.6 and 98.9%, respectively. The analysis of the testing dataset was completed in 24 s (approximately 184 frames/s). <b><i>Discussion/Conclusion:</i></b> We have developed an artificial intelligence tool capable of effectively detecting luminal blood. The development of these tools may enhance the diagnostic accuracy of capsule endoscopy when evaluating patients presenting with obscure small bowel bleeding.

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

2019 ◽  
Vol 89 (1) ◽  
pp. 189-194 ◽  
Author(s):  
Romain Leenhardt ◽  
Pauline Vasseur ◽  
Cynthia Li ◽  
Jean Christophe Saurin ◽  
Gabriel Rahmi ◽  
...  
Keyword(s):  
Neural Network ◽  
Small Bowel ◽  
Capsule Endoscopy ◽  
Network Algorithm ◽  
Neural Network Algorithm
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