Endoscopy 2019; 51(04): S7
DOI: 10.1055/s-0039-1681191
ESGE Days 2019 oral presentations
Friday, April 5, 2019 08:30 – 10:30: Capsule 1 Club B
Georg Thieme Verlag KG Stuttgart · New York

ENDOCLEAN: AUTOMATIC EVALUATION OF THE CLEANLINESS OF THE SMALL BOWEL IN CAPSULE ENDOSCOPY PROCEDURES

A Nevárez
1   Endoscopy Unit, La Fe University Hospital, Valencia, Spain
,
R Noorda
2   CVBLab Institute for Research and Innovation in Bioengineering, Valencia Polytechnic University, Valencia, Spain
,
V Naranjo
2   CVBLab Institute for Research and Innovation in Bioengineering, Valencia Polytechnic University, Valencia, Spain
,
N Alonso
1   Endoscopy Unit, La Fe University Hospital, Valencia, Spain
,
V Pons
1   Endoscopy Unit, La Fe University Hospital, Valencia, Spain
› Author Affiliations
Further Information

Publication History

Publication Date:
18 March 2019 (online)

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Aims:

Poor visualization of the small bowel due to the presence of intestinal content remains one of the main limitations in capsule endoscopy (CE) procedures. The aim of our study was to develop a tool that can automatically detect intestinal content in CE procedures.

Methods:

We created computer algorithms capable of distinguishing automatically between dirty and clean regions in frames from CE videos. We extracted 563 frame images from 35 different CE videos. Each frame was divided in segments of 64 × 64 pixels, referred to as patches. A total of 55293 patches were annotated by an experienced reader. We assigned the frame images to two different sets: 80% for the training set and 20% for the testing set. We extracted features based on colour and texture for discrimination between clean regions and regions with intestinal content. With frames used for test purposes we calculated accuracy (ACC), sensibility (S) and specificity (SP) in five different models to analyze their performance. We then used the model to predict whether the region is clean or contains intestinal content and also the pixel probability.

Results:

51,04% patches were classified as dirty regions and 48,96% as clean regions. We performed 5 different validation tests to evaluate different algorithms and their performance in predicting a patch as either clean or dirty. We obtained an average accuracy of 87,12%, sensitivity of 89,89% and specificity of 84,50% using Supporting Vector Machine (SVM) classification.

Conclusions:

Using patch probabilities, Endoclean system allows the estimation at a pixel level of the percentage of cleanliness in images of CE videos with high accuracy. With optimization of our results, this tool can be implemented for objective assessment of the quality of mucosal visualization in CE procedures and can later provide the opportunity to compare different types of preparations that can be used to improve the procedure reliability.