scholarly journals Advanced Endoscopic Imaging for Diagnosis of Crohn's Disease

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Helmut Neumann ◽  
Klaus Mönkemüller ◽  
Claudia Günther ◽  
Raja Atreya ◽  
Michael Vieth ◽  
...  
Keyword(s):  
Crohn’S Disease ◽  
Crohn's Disease ◽  
Imaging Techniques ◽  
Mucosal Inflammation ◽  
Confocal Laser Endomicroscopy ◽  
Confocal Laser ◽  
Endoscopic Imaging ◽  
Therapeutic Success ◽  
Tremendous Importance

Endoscopy in IBD has tremendous importance to diagnose inflammatory activity, to evaluate therapeutic success and for the surveillance of colitis associated cancer. Thus it becomes obvious that there is a need for new and more advanced endoscopic imaging techniques for better characterization of mucosal inflammation and early neoplasia detection in IBD. This paper describes the concept of advanced endoscopic imaging for the diagnosis and characterization of Crohn's disease, including magnification endoscopy, chromoendoscopy, balloon-assisted enteroscopy, capsule endoscopy, confocal laser endomicroscopy, and endocytoscopy.

scholarly journals Confocal laser endomicroscopy for characterization of Crohn's disease-associated duodenitis

Endoscopy ◽  
2012 ◽  
Vol 44 (S 02) ◽  
pp. E80-E80 ◽  
Author(s):  
G. Hundorfean ◽  
A. Agaimy ◽  
R. Atreya ◽  
J. Mudter ◽  
M. Neurath ◽  
...  
Keyword(s):  
Crohn’S Disease ◽  
Crohn's Disease ◽  
Confocal Laser Endomicroscopy ◽  
Confocal Laser

scholarly journals Deep Learning Algorithm for the Confirmation of Mucosal Healing in Crohn’s Disease, Based on Confocal Laser Endomicroscopy Images

2021 ◽  
Vol 30 (1) ◽  
pp. 59-65
Author(s):  
Anca Loredana Udristoiu ◽  
Daniela Stefanescu ◽  
Gabriel Gruionu ◽  
Lucian Gheorghe Gruionu ◽  
Andreea Valentina Iacob ◽  
...  
Keyword(s):  
Crohn’S Disease ◽  
Deep Learning ◽  
Crohn's Disease ◽  
Colonic Mucosa ◽  
Mucosal Healing ◽  
Machine Learning Algorithms ◽  
Confocal Laser Endomicroscopy ◽  
Confocal Laser ◽  
Normal Colonic Mucosa ◽  
Test Accuracy

Background and Aims: Mucosal healing (MH) is associated with a stable course of Crohn’s disease (CD) which can be assessed by confocal laser endomicroscopy (CLE). To minimize the operator’s errors and automate assessment of CLE images, we used a deep learning (DL) model for image analysis. We hypothesized that DL combined with convolutional neural networks (CNNs) and long short-term memory (LSTM) can distinguish between normal and inflamed colonic mucosa from CLE images. Methods: The study included 54 patients, 32 with known active CD, and 22 control patients (18 CD patients with MH and four normal mucosa patients with no history of inflammatory bowel diseases). We designed and trained a deep convolutional neural network to detect active CD using 6,205 endomicroscopy images classified as active CD inflammation (3,672 images) and control mucosal healing or no inflammation (2,533 images). CLE imaging was performed on four colorectal areas and the terminal ileum. Gold standard was represented by the histopathological evaluation. The dataset was randomly split in two distinct training and testing datasets: 80% data from each patient were used for training and the remaining 20% for testing. The training dataset consists of 2,892 images with inflammation and 2,189 control images. The testing dataset consists of 780 images with inflammation and 344 control images of the colon. We used a CNN-LSTM model with four convolution layers and one LSTM layer for automatic detection of MH and CD diagnosis from CLE images. Results: CLE investigation reveals normal colonic mucosa with round crypts and inflamed mucosa with irregular crypts and tortuous and dilated blood vessels. Our method obtained a 95.3% test accuracy with a specificity of 92.78% and a sensitivity of 94.6%, with an area under each receiver operating characteristic curves of 0.98. Conclusions: Using machine learning algorithms on CLE images can successfully differentiate between inflammation and normal ileocolonic mucosa and can be used as a computer aided diagnosis for CD. Future clinical studies with a larger patient spectrum will validate our results and improve the CNN-SSTM model.

507 Assessment of Crohn's Disease Activity by Confocal Laser Endomicroscopy

2012 ◽  
Vol 75 (4) ◽  
pp. AB148
Author(s):  
Helmut Neumann ◽  
Michael Vieth ◽  
Raja Atreya ◽  
Martin Grauer ◽  
Jürgen Siebler ◽  
...  
Keyword(s):  
Crohn’S Disease ◽  
Crohn's Disease ◽  
Disease Activity ◽  
Confocal Laser Endomicroscopy ◽  
Confocal Laser ◽  
Crohn’S Disease Activity

Confocal laser endomicroscopy: a novel method for prediction of relapse in Crohn’s disease

Endoscopy ◽  
2015 ◽  
Vol 48 (04) ◽  
pp. 364-372 ◽  
Author(s):  
John Karstensen ◽  
Adrian Săftoiu ◽  
Jørn Brynskov ◽  
Jakob Hendel ◽  
Pia Klausen ◽  
...  
Keyword(s):  
Crohn’S Disease ◽  
Crohn's Disease ◽  
Confocal Laser Endomicroscopy ◽  
Confocal Laser ◽  
Novel Method ◽  
Prediction Of Relapse
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