A Machine Learning Framework Using SOMs: Applications in the Intestinal Motility Assessment
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- Vilariño F., Spyridonos P., Vitrià J., Malagelada C., Radeva P. (2006) A Machine Learning Framework Using SOMs: Applications in the Intestinal Motility Assessment. In: Martínez-Trinidad J.F., Carrasco Ochoa J.A., Kittler J. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2006. Lecture Notes in Computer Science, vol 4225. Springer, Berlin, Heidelberg
Small Bowel Motility Assessment by means of Wireless Capsule Video Endoscopy constitutes a novel clinical methodology in which a capsule with a micro-camera attached to it is swallowed by the patient, emitting a RF signal which is recorded as a video of its trip throughout the gut. In order to overcome the main drawbacks associated with this technique -mainly related to the large amount of visualization time required-, our efforts have been focused on the development of a machine learning system, built up in sequential stages, which provides the specialists with the useful part of the video, rejecting those parts not valid for analysis. We successfully used Self Organized Maps in a general semi-supervised framework with the aim of tackling the different learning stages of our system. The analysis of the diverse types of images and the automatic detection of intestinal contractions is performed under the perspective of intestinal motility assessment in a clinical environment.
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