Prediction of clinical outcome in Crohn's disease with confocal laser endomicroscopy: a prospective, observational, follow-up study

2014 ◽  
Vol 52 (08) ◽  
Author(s):  
GE Tontini ◽  
J Mudter ◽  
M Vieth ◽  
R Atreya ◽  
C Günther ◽  
...  
Keyword(s):  
Crohn’S Disease ◽  
Crohn's Disease ◽  
Clinical Outcome ◽  
Confocal Laser Endomicroscopy ◽  
Confocal Laser ◽  
Follow Up Study

Su1301 Prediction of Clinical Outcome in Crohn's Disease by Using Confocal Laser Endomicroscopy: A Prospective, Observational, Follow-Up Study

2014 ◽  
Vol 146 (5) ◽  
pp. S-430
Author(s):  
Gian Eugenio Tontini ◽  
Jonas Mudter ◽  
Michael Vieth ◽  
Raja Atreya ◽  
Claudia Günther ◽  
...  
Keyword(s):  
Crohn’S Disease ◽  
Crohn's Disease ◽  
Clinical Outcome ◽  
Confocal Laser Endomicroscopy ◽  
Confocal Laser ◽  
Follow Up Study

Bone loss in patients with Crohn's disease: A longterm-follow up study

2001 ◽  
Vol 120 (5) ◽  
pp. A628-A628
Author(s):  
P CLEMENS ◽  
V HAWIG ◽  
M MUELLER ◽  
J SCAENZLIN ◽  
B KLUMP ◽  
...  
Keyword(s):  
Crohn’S Disease ◽  
Crohn's Disease ◽  
Bone Loss ◽  
Follow Up Study

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.

scholarly journals Metronidazole Therapy for Perineal Crohn's Disease: A Follow-Up Study

1982 ◽  
Vol 83 (2) ◽  
pp. 383-387 ◽  
Author(s):  
Lawrence J. Brandt ◽  
Leslie H. Bernstein ◽  
Scott J. Boley ◽  
Michael S. Frank
Keyword(s):  
Crohn’S Disease ◽  
Crohn's Disease ◽  
Follow Up Study

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
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