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.

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 Artificial intelligence and colon capsule endoscopy: automatic detection of blood in colon capsule endoscopy using a convolutional neural network

2021 ◽  
Vol 09 (08) ◽  
pp. E1264-E1268
Author(s):  
Miguel Mascarenhas Saraiva ◽  
João P. S. Ferreira ◽  
Hélder Cardoso ◽  
João Afonso ◽  
Tiago Ribeiro ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Convolutional Neural Network ◽  
Capsule Endoscopy ◽  
Learning Algorithm ◽  
Colorectal Neoplasia ◽  
Automatic Detection ◽  
Predictive Values ◽  
Colon Capsule Endoscopy ◽  
Colon Capsule

AbstractColon capsule endoscopy (CCE) is a minimally invasive alternative to conventional colonoscopy. Most studies on CCE focus on colorectal neoplasia detection. The development of automated tools may address some of the limitations of this diagnostic tool and widen its indications for different clinical settings. We developed an artificial intelligence model based on a convolutional neural network (CNN) for the automatic detection of blood content in CCE images. Training and validation datasets were constructed for the development and testing of the CNN. The CNN detected blood with a sensitivity, specificity, and positive and negative predictive values of 99.8 %, 93.2 %, 93.8 %, and 99.8 %, respectively. The area under the receiver operating characteristic curve for blood detection was 1.00. We developed a deep learning algorithm capable of accurately detecting blood or hematic residues within the lumen of the colon based on colon CCE images.

scholarly journals P113 Automatic detection of colonic ulcers and erosions in colon capsule endoscopy images using a convolutional neural network

2021 ◽  
Vol 15 (Supplement_1) ◽  
pp. S205-S206
Author(s):  
T Ribeiro ◽  
M Mascarenhas Saraiva ◽  
J Afonso ◽  
H Cardoso ◽  
J Ferreira ◽  
...  
Keyword(s):  
Neural Network ◽  
Minimally Invasive ◽  
Convolutional Neural Network ◽  
Capsule Endoscopy ◽  
Colonic Mucosa ◽  
Automatic Detection ◽  
Validation Dataset ◽  
Predictive Values ◽  
Colon Capsule Endoscopy ◽  
Colon Capsule

Abstract Background Conventional colonoscopy is gold standard for the diagnosis and monitoring of patients with suspected or known inflammatory bowel disease (IBD). Nevertheless, it is a potentially painful procedure with risk of complications, including bleeding and perforation. Colon capsule endoscopy (CCE) is a minimally invasive alternative for patients unwilling to undergo colonoscopy or when it is contraindicated or unfeasible. However, CCE produces thousands of frames and their revision is a time-consuming and monotonous task. The detection of ulcers and erosions is paramount for the diagnosis and assessment of the activity of IBD. However, these lesions may appear in a very small number of frames, thus increasing the risk of overlooking significant lesions. Our aim was to develop an Artificial Intelligence (AI) algorithm, based on a multilayer Convolutional Neural Network (CNN) for automatic detection of ulcers and erosions in CCE exams. Methods A total of 24 CCE exams (PillCam COLON 2®) performed at a single centre between 2010–2020 were analysed. From these exams, 3 635 frames of the colonic mucosa were extracted, 770 containing colonic ulcers or erosions and 2 865 showing normal colonic mucosa. These images were used for construction of training (80% of the frames) and validation (20% of the frames) datasets. For automatic identification of these lesions, these images were inputted in a CNN model with transfer learning. The output provided by the CNN (Figure 1) was compared to the classification provided by a consensus of specialists. Performance marks included sensitivity, specificity, positive and negative predictive values, accuracy and area under the receiving operator characteristics curve (AUROC). Results After the optimization of the neural architecture of the CNN, our model automatically detected colonic ulcers and erosions with a sensitivity of 90.3%, specificity of 98.8% and an accuracy of 97.0% (Figure 2). The AUROC was 0.99 (Figure 3). The mean processing time for the validation dataset was 11s (approximately 66 frames/s). Conclusion We developed a CNN model which demonstrated high levels of efficiency for the automatic detection of ulcers and erosions in CCE images. Our study lays the foundations for the development of effective AI tools for application to CCE exams. These systems may enhance the diagnostic accuracy and reading efficiency of CCE, thus expanding the role of minimally-invasive colonic exploration in the management of IBD patients.

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).

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