© 2015 The American Physiological Society. We have previously developed an original method to evaluate small bowel motor function based on computer vision analysis of endoluminal images obtained by capsule endoscopy. Our aim was to demonstrate intestinal motor abnormalities in patients with functional bowel disorders by endoluminal vision analysis. Patients with functional bowel disorders (n ± 205) and healthy subjects (n ± 136) ingested the endoscopic capsule (Pillcam-SB2, Given-Imaging) after overnight fast and 45 min after gastric exit of the capsule a liquid meal (300 ml, 1 kcal/ml) was administered. Endoluminal image analysis was performed by computer vision and machine learning techniques to define the normal range and to identify clusters of abnormal function. After training the algorithm, we used 196 patients and 48 healthy subjects, completely naive, as test set. In the test set, 51 patients (26%) were detected outside the normal range (P ± 0.001 vs. 3 healthy subjects) and clustered into hypo- and hyperdynamic subgroups compared with healthy subjects. Patients with hypodynamic behavior (n ± 38) exhibited less luminal closure sequences (41 ± 2% of the recording time vs. 61 ± 2%; P ± 0.001) and more static sequences (38 ± 3 vs. 20 ± 2%; P ± 0.001); in contrast, patients with hyperdynamic behavior (n ± 13) had an increased proportion of luminal closure sequences (73 ± 4 vs. 61 ± 2%; P ± 0.029) and more high-motion sequences (3 ± 1 vs. 0.5 ± 0.1%; P ± 0.001). Applying an original methodology, we have developed a novel classification of functional gut disorders based on objective, physiological criteria of small bowel function.
|Journal||American Journal of Physiology - Gastrointestinal and Liver Physiology|
|Publication status||Published - 15 Sep 2015|
- Capsule endoscopy
- Computer vision analysis
- Functional bowel disorders
- Intestinal motility
- Machine learning