title: New insight into intestinal motor function via noninvasive endoluminal image analysis.
abstract: OBJECTIVE
  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.affiliation: Digestive System Research Unit, University Hospital Vall d'Hebron, Barcelona, Spain.
authors.forename: Carolina
authors.initials: C
authors.lastname: Malagelada
authors.forename: Fosca
authors.initials: F
authors.lastname: De Iorio
authors.forename: Fernando
authors.initials: F
authors.lastname: Azpiroz
authors.forename: Anna
authors.initials: A
authors.lastname: Accarino
authors.forename: Santi
authors.initials: S
authors.lastname: Segui
authors.forename: Petia
authors.initials: P
authors.lastname: Radeva
authors.forename: Juan-R
authors.initials: JR
authors.lastname: Malagelada
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000465:Algorithms; D001185:Artificial Intelligence; D053705:Capsule Endoscopes / Q000592:standards*; D016145:Endoscopy, Digestive System / Q000295:instrumentation* / Q000379:methods / Q000592:standards*; D005260:Female; D005769:Gastrointestinal Motility; D006801:Humans; D007091:Image Processing, Computer-Assisted; D007410:Intestinal Diseases / Q000175:diagnosis* / Q000503:physiopathology*; D007421:Intestine, Small / Q000503:physiopathology; D008297:Male; D008365:Manometry; D008875:Middle Aged; D009391:Nephelometry and Turbidimetry; D015203:Reproducibility of Results