scholarly journals Artificial intelligence and capsule endoscopy: automatic detection of enteric protruding lesions using a convolutional neural network

2021 ◽  
Author(s):  
Miguel Mascarenhas Saraiva ◽  
Jo�o Afonso ◽  
Tiago Ribeiro ◽  
Jo�o Ferreira ◽  
Hélder Cardoso ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Convolutional Neural Network ◽  
Capsule Endoscopy ◽  
Automatic Detection

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 S558 Artificial Intelligence and Capsule Endoscopy: Automatic Detection of Gastric Vascular Lesions Using a Convolutional Neural Network

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

scholarly journals S1444 Artificial Intelligence and Capsule Endoscopy: Automatic Detection of Gastric Protruding Lesions Using a Convolutional Neural Network

2021 ◽  
Vol 116 (1) ◽  
pp. S663-S663
Author(s):  
Tiago Ribeiro ◽  
Miguel Mascarenhas ◽  
Joao Afonso ◽  
Hélder Cardoso ◽  
Patrícia Andrade ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
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 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.

ID: 3526558 ARTIFICIAL INTELLIGENCE AND COLON CAPSULE ENDOSCOPY: AUTOMATIC DETECTION OF COLONIC PROTUBERANT LESIONS USING A CONVOLUTIONAL NEURAL NETWORK

2021 ◽  
Vol 93 (6) ◽  
pp. AB98-AB99
Author(s):  
Miguel M. Saraiva ◽  
Helder Cardoso ◽  
João Afonso ◽  
João Ferreira ◽  
Patrícia Andrade ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Convolutional Neural Network ◽  
Capsule Endoscopy ◽  
Automatic Detection ◽  
Colon Capsule Endoscopy ◽  
Colon Capsule

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.

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