scholarly journals S557 Automatic Detection of Colonic Ulcers and Erosions in Colon Capsule Endoscopy Images Using a Convolutional Neural Network

2021 ◽  
Vol 116 (1) ◽  
pp. S254-S255
Author(s):  
Joao Afonso ◽  
Miguel Mascarenhas ◽  
Tiago Ribeiro ◽  
Hélder Cardoso ◽  
Patrícia Andrade ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Capsule Endoscopy ◽  
Automatic Detection ◽  
Colon Capsule Endoscopy ◽  
Colon Capsule

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 P188 Artificial Intelligence and Colon Capsule Endoscopy: Automatic detection of colonic mucosal lesions and blood using a convolutional neural network

2021 ◽  
Vol 15 (Supplement_1) ◽  
pp. S257-S258
Author(s):  
J Afonso ◽  
M Mascarenhas ◽  
T Ribeiro ◽  
H Cardoso ◽  
J Ferreira ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Convolutional Neural Network ◽  
Operating Characteristic ◽  
Automatic Detection ◽  
Diagnostic Error ◽  
Normal Mucosa ◽  
Colon Capsule Endoscopy ◽  
Colon Capsule ◽  
Mucosal Lesions

Abstract Background Conventional colonoscopy is the standard criterion for the diagnosis and staging of colonic disease, including inflammatory bowel disease (IBD). However, it has the risk of complications, including bleeding and perforation. Colon capsule endoscopy (CCE) is a minimally invasive alternative for patients refusing conventional colonoscopy or for whom the latter is contraindicated. However, reading CCE images is a time-consuming task and prone to diagnostic error. Detection of colonic blood is relevant for the diagnosis and assessment of the activity of IBD, particularly ulcerative colitis. An accurate and early diagnosis is essential as it directs subsequent treatment. Our aim was to develop an Artificial Intelligence (AI) algorithm, based on a multilayer Convolutional Neural Network (CNN), for automatic detection of blood and other significant lesions in the colonic lumen in CCE exams: Methods A total of 24 CCE exams (PillCam COLON 2®) from a single centre, performed between 2010–2020, were analysed. From these exams 7640 images (2915 normal mucosa, 3065 blood and 1660 mucosal lesions) were ultimately extracted. Two image datasets were created for CNN training and testing. These images were inserted in a CNN model with transfer of learning. The output provided by the CNN was compared to the classification provided by a consensus of specialists (Figure 1). Performance marks included sensitivity, specificity, positive and negative predictive values (PPV and NPV, respectively), accuracy and area under the receiving operator characteristics curve (AUROC). Results Blood was detected with a sensitivity and specificity of 97.2% and 99.9%, respectively. The AUROC for detection of blood was 1.00 (Figure 2). Detection of other findings had a sensitivity and specificity of 83.7% and 96.7%, respectively. The overall accuracy of the CNN was of 95.4%. Figure 1 - Output obtained from the application of the convolutional neural network. N: normal mucosa; B - blood or hematic residues; ML – mucosal lesions. Figure 2 – Receiver operating characteristic analyses of the network’s performance in the detection of normal mucosa, blood and colon mucosal lesions. AUROC – are under the receiver operating characteristic curve; N – normal colonic mucosa; B – blood; ML – mucosal lesions. Conclusion We developed a pioneer CNN for CCE which demonstrated high levels of efficiency for the automatic detection of blood and lesions with high clinical significance. The development of tools for automatic detection of these lesions may allow for minimization of diagnostic error and the time spent evaluating these exams.

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

ID: 3526753 DEEP LEARNING AND CAPSULE ENDOSCOPY: AUTOMATIC DETECTION OF ULCERS AND ENTERIC EROSIONS IN CAPSULE ENDOSCOPY USING A CONVOLUTIONAL NEURAL NETWORK

2021 ◽  
Vol 93 (6) ◽  
pp. AB351-AB352
Author(s):  
Miguel M. Saraiva ◽  
Helder Cardoso ◽  
João Afonso ◽  
João Ferreira ◽  
Patrícia Andrade ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Capsule Endoscopy ◽  
Automatic Detection
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