Diagnosis of Intestinal Motility by Image Analysis Using Capsule Endoscopy

Not Applicable

Terminated

Conditions: Intestinal Abnormalities

Interventions: Device: PillCam SB2

Registration Number: NCT01085513

Lead Sponsor: Medtronic - MITG

Brief Summary: Conventional intestinal manometry is the current gold standard for the evaluation of intestinal motility, and identifies patterns of intestinal dysmotility. However intestinal manometry involves intestinal intubation with consequent discomfort for the patients, and requires considerable technical expertise and knowledge for interpretation of the data. Hence, to date this method has limited indications and is restricted to very few referral centers around the world.

A novel method for evaluation of intestinal motility has been developed based on endoluminal image analysis using the endoscopic PillCam capsule, In contrast to manometry, this technique is minimally invasive, the technical aspects are simple, and the analysis is fully automated by a computer program.

The technique has been validated in a group of patients with intestinal dysmotility and healthy subjects, and has demonstrated over 90% sensitivity and specificity.

This technique needs now to be validated in a large multinational population, to further develop a robust discrimination algorithm for widespread diagnostic application. Furthermore, whereas manometry only recognizes neuropathic, myopathic and obstructive motor patterns, endoluminal image analysis may identify different categories of patients depending on the clinical presentation and the etiologic factors involved.

This study is designed to provide evidence that the algorithm, using images created by PillCam SB2 capsules, is at least as good as small bowel manometry in diagnosing severe dysmotility.

Detailed Description: Conventional intestinal manometry is the current gold standard for the evaluation of intestinal motility1,2,3,11, and identifies patterns of intestinal dysmotility4,8,9. However intestinal manometry involves intestinal intubation with consequent discomfort for the patients. Furthermore, it requires considerable technical expertise and knowledge for interpretation of the data. Hence, to date this method has limited indications and is restricted to very few referral centers around the world6,7,10,13.

Vall d'Hebron Hospital, in collaboration with CVC (Barcelona, Spain) have recently developed a minimally invasive method for evaluation of intestinal motility based on endoluminal image analysis using the endoscopic PillCam capsule. In contrast to manometry, this technique is minimally invasive, the technical aspects are simple, and the analysis is performed fully automated by a computer program. Both the technical procedure of the test and the endoluminal image analysis program has been developed by a multidisciplinary medical-engineering team in the Autonomous University of Barcelona over the past 5 years. The technique has been validated in a group of patients with intestinal dysmotility and healthy subjects, and has demonstrated over 90% sensitivity and specificity.

In brief, the technique works as follows. In each study a series of features are analyzed: contractile patterns (contractions evaluated as a diaphragmatic occlusion of the lumen and by the presence of a radial wrinkle pattern), non contractile patterns (wall and tunnel patterns), luminal content (turbid pattern), endoluminal motion, and capsule displacement.

The program is based on an automated learning method (machine learning technique). Data from patients and healthy subjects are used as a training set. Based on these data, the program develops the function that best discriminates both groups. The performance of the system has been validated using the leave-one-out method that uses all but one as training set and evaluates the left-out example.

This technique needs now to be validated in a large multinational population. Using this expanded pool of studies as a learning set, a robust discrimination algorithm will be developed, that can be made available for widespread diagnostic application. Furthermore, whereas manometry only recognizes neuropathic, myopathic5,12 and obstructive motor patterns, endoluminal image analysis may identify different categories of patients depending on the clinical presentation and the etiologic factors involved.

This study is designed to provide evidence that the algorithm, using images created by PillCam SB2 capsules, is at least as good as small bowel manometry in diagnosing severe dysmotility.

Recruitment & Eligibility

Status: TERMINATED

Sex: All

Target Recruitment: 143

Inclusion Criteria

Not provided

Exclusion Criteria

Not provided

Study & Design

Study Type: INTERVENTIONAL

Study Design: PARALLEL

Arm && Interventions

Group	Intervention	Description
Patients	PillCam SB2	Patients previously indicated for manometry
Healthy volunteers	PillCam SB2	Healthy volunteers

Primary Outcome Measures

Name	Time	Method
To Evaluate Accuracy of Endoluminal Image Analysis by Capsule Endoscopy in Detecting Severe Dysmotility	Up to 7 months	Patients were indicated for small bowel manometry based on one or both of the following symptoms: * Pseudo obstruction * Unintentional body weight loss

Secondary Outcome Measures

Name	Time	Method
To Assess Correlation Between Patient Clinical Symptoms and CE-EIA	Up to 7 months	The study was terminated without achieving the needed sample size due to very low recruitment rate. Therefore, no statistical analysis has been performed to achieve the secondary outcome.

Trial Locations

Locations (6): Internal Medicine & Gastroenterology, University of Bologna,
🇮🇹
Bologna, Italy
Departement of Internal Medicine, Sahlgrenska Universitetss
🇸🇪
Göteborg, Sweden
Mayo Clinic,
🇺🇸
Rochester, Minnesota, United States
Division of Gastroenterology and Hepatology Mayo Clinic
🇺🇸
Scottsdale, Arizona, United States
Autonomous University of Barcelona
🇪🇸
Barcelona, Spain
Center for GI Research, KU Leuven,
🇧🇪
Leuven, Belgium