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Keywords:

  • capsule endoscopy;
  • computer vision analysis;
  • endoluminal image analysis;
  • glucagon;
  • intestinal motor inhibition;
  • machine learning techniques;
  • small bowel motility;
  • small bowel transit

Abstract  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 μg kg−1 bolus followed by a 9.6 μg 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.