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Fosca De Iorio; Carolina Malagelada; Fernando Azpiroz; M. Maluenda; C. Violanti; Laura Igual; Jordi Vitria; Juan R. Malagelada |
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Title |
Intestinal motor activity, endoluminal motion and transit |
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Journal Article |
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2009 |
Publication ![sorted by Publication field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Neurogastroenterology & Motility |
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NEUMOT |
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21 |
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12 |
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1264–e119 |
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A programme for evaluation of intestinal motility has been recently developed based on endoluminal image analysis using computer vision methodology and machine learning techniques. Our aim was to determine the effect of intestinal muscle inhibition on wall motion, dynamics of luminal content and transit in the small bowel. Fourteen healthy subjects ingested the endoscopic capsule (Pillcam, Given Imaging) in fasting conditions. Seven of them received glucagon (4.8 microg kg(-1) bolus followed by a 9.6 microg kg(-1) h(-1) infusion during 1 h) and in the other seven, fasting activity was recorded, as controls. This dose of glucagon has previously shown to inhibit both tonic and phasic intestinal motor activity. Endoluminal image and displacement was analyzed by means of a computer vision programme specifically developed for the evaluation of muscular activity (contractile and non-contractile patterns), intestinal contents, endoluminal motion and transit. Thirty-minute periods before, during and after glucagon infusion were analyzed and compared with equivalent periods in controls. No differences were found in the parameters measured during the baseline (pretest) periods when comparing glucagon and control experiments. During glucagon infusion, there was a significant reduction in contractile activity (0.2 +/- 0.1 vs 4.2 +/- 0.9 luminal closures per min, P < 0.05; 0.4 +/- 0.1 vs 3.4 +/- 1.2% of images with radial wrinkles, P < 0.05) and a significant reduction of endoluminal motion (82 +/- 9 vs 21 +/- 10% of static images, P < 0.05). Endoluminal image analysis, by means of computer vision and machine learning techniques, can reliably detect reduced intestinal muscle activity and motion. |
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OR;MILAB;MV |
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BCNPCL @ bcnpcl @ DMA2009 |
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1251 |
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Carolina Malagelada; F.De Lorio; Santiago Segui; S. Mendez; Michal Drozdzal; Jordi Vitria; Petia Radeva; J.Santos; Anna Accarino; Juan R. Malagelada; Fernando Azpiroz |
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Title |
Functional gut disorders or disordered gut function? Small bowel dysmotility evidenced by an original technique |
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Journal Article |
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2012 |
Publication ![sorted by Publication field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Neurogastroenterology & Motility |
Abbreviated Journal |
NEUMOT |
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24 |
Issue |
3 |
Pages |
223-230 |
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capsule endoscopy;computer vision analysis;machine learning technique;small bowel motility |
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JCR Impact Factor 2010: 3.349
Background This study aimed to determine the proportion of cases with abnormal intestinal motility among patients with functional bowel disorders. To this end, we applied an original method, previously developed in our laboratory, for analysis of endoluminal images obtained by capsule endoscopy. This novel technology is based on computer vision and machine learning techniques.
Methods The endoscopic capsule (Pillcam SB1; Given Imaging, Yokneam, Israel) was administered to 80 patients with functional bowel disorders and 70 healthy subjects. Endoluminal image analysis was performed with a computer vision program developed for the evaluation of contractile events (luminal occlusions and radial wrinkles), non-contractile patterns (open tunnel and smooth wall patterns), type of content (secretions, chyme) and motion of wall and contents. Normality range and discrimination of abnormal cases were established by a machine learning technique. Specifically, an iterative classifier (one-class support vector machine) was applied in a random population of 50 healthy subjects as a training set and the remaining subjects (20 healthy subjects and 80 patients) as a test set.
Key Results The classifier identified as abnormal 29% of patients with functional diseases of the bowel (23 of 80), and as normal 97% of healthy subjects (68 of 70) (P < 0.05 by chi-squared test). Patients identified as abnormal clustered in two groups, which exhibited either a hyper- or a hypodynamic motility pattern. The motor behavior was unrelated to clinical features.
Conclusions & Inferences With appropriate methodology, abnormal intestinal motility can be demonstrated in a significant proportion of patients with functional bowel disorders, implying a pathologic disturbance of gut physiology. |
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Wiley Online Library |
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Admin @ si @ MLS2012 |
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1830 |
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Bogdan Raducanu; Jordi Vitria |
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Learning to Learn: From Smarts Machines to Intelligent Machines |
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2008 |
Publication ![sorted by Publication field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Patter Recognition Letters |
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PRL |
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29 |
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8 |
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1024–1032 |
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BCNPCL @ bcnpcl @ RaV2008a |
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950 |
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Agata Lapedriza; Santiago Segui; David Masip; Jordi Vitria |
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A Sparse Bayesian Approach for Joint Feature Selection and Classifier Learning |
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2008 |
Publication ![sorted by Publication field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Pattern Analysis and Applications, Special Issue: Non–Parametric Distance–Based Classification Techniques and Their Applications, |
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11 |
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3-4 |
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299-308 |
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BCNPCL @ bcnpcl @ LSM2008 |
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996 |
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David Masip; Ludmila I. Kuncheva; Jordi Vitria |
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An ensemble-based method for linear feature extraction for two-class problems |
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2005 |
Publication ![sorted by Publication field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Pattern Analysis and Applications, 8(3): 227–237 (IF: 0.782) |
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BCNPCL @ bcnpcl @ MKV2005 |
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613 |
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