BACKGROUND & AIMS: Evaluation of small bowel motility by intestinal manometry is invasive and requires expertise for interpretation. Our aim was to use capsule technology for evaluation of small bowel motor function based on a fully computerized image analysis program. METHODS: Thirty-six consecutive patients with severe intestinal motor disorders (19 fulfilling manometric criteria of intestinal dysmotility and 17 not) and 50 healthy subjects received the endoscopic capsule (Pillcam; Given Imaging, Yokneam, Israel). Endoluminal image analysis was performed with a computer vision program specifically developed for the detection of contractile patterns (phasic luminal closure and radial wrinkles by wall texture analysis), noncontractile patterns (tunnel and wall appearance by Laplacian filtering), intestinal content (by color decomposition analysis), and endoluminal motion (by chromatic stability). Automatic classification of normal and abnormal intestinal motility was performed by means of a machine-learning technique. RESULTS: As compared with healthy subjects, patients exhibited less contractile activity (25% less phasic luminal closures, P < .05) and more noncontractile patterns (151% more tunnel pattern, P < .05), static sequences (56% more static images, P < .01), and turbid intestinal content (94% more static turbid images, P < .01). On cross validation, the classifier identified as abnormal all but 1 patient with manometric criteria of dysmotility and as normal all healthy subjects. Out of the 17 patients without manometric criteria of dysmotility, 11 were identified as abnormal and 6 as normal. CONCLUSIONS: Our study shows that endoluminal image analysis, by means of computer vision and machine-learning techniques, constitutes a reliable, noninvasive, and automated diagnostic test of intestinal motor disorders.
BACKGROUND & AIMS: Evaluation of small bowel motility by intestinal manometry is invasive and requires expertise for interpretation. Our aim was to use capsule technology for evaluation of small bowel motor function based on a fully computerized image analysis program. METHODS: Thirty-six consecutive patients with severe intestinal motor disorders (19 fulfilling manometric criteria of intestinal dysmotility and 17 not) and 50 healthy subjects received the endoscopic capsule (Pillcam; Given Imaging, Yokneam, Israel). Endoluminal image analysis was performed with a computer vision program specifically developed for the detection of contractile patterns (phasic luminal closure and radial wrinkles by wall texture analysis), noncontractile patterns (tunnel and wall appearance by Laplacian filtering), intestinal content (by color decomposition analysis), and endoluminal motion (by chromatic stability). Automatic classification of normal and abnormal intestinal motility was performed by means of a machine-learning technique. RESULTS: As compared with healthy subjects, patients exhibited less contractile activity (25% less phasic luminal closures, P < .05) and more noncontractile patterns (151% more tunnel pattern, P < .05), static sequences (56% more static images, P < .01), and turbid intestinal content (94% more static turbid images, P < .01). On cross validation, the classifier identified as abnormal all but 1 patient with manometric criteria of dysmotility and as normal all healthy subjects. Out of the 17 patients without manometric criteria of dysmotility, 11 were identified as abnormal and 6 as normal. CONCLUSIONS: Our study shows that endoluminal image analysis, by means of computer vision and machine-learning techniques, constitutes a reliable, noninvasive, and automated diagnostic test of intestinal motor disorders.
Authors: Edward J Ciaccio; Christina A Tennyson; Govind Bhagat; Suzanne K Lewis; Peter H Green Journal: Dig Dis Sci Date: 2012-05-30 Impact factor: 3.199