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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.
Full 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.
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Inclusion and exclusion criteria
Inclusion criteria - Patients
Patient's age is 16-80 years, inclusive.
One of the following SB manometry procedures:
Patient was indicated for small bowel manometry based on one or both of the following symptoms:
Patients may have any of the following:
Patients will be tested during period of (non-acute) remission. - Chronic severe GI symptoms with inability to maintain normal body weight: abdominal symptoms (such as nausea, abdominal distension, discomfort or pain) produce inability to maintain a normal body weight as defined by unintentional weight loss of at least 10% of original body weight before onset of symptoms or a BMI < 18.5, while on a normal diet (without oral dietary supplements, enteral or parenteral feeding).
These should be chronic symptoms lasting at least 6 months.
Exclusion criteria - Patients
Inclusion criteria - Healthy Volunteers
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143 participants in 2 patient groups
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Data sourced from clinicaltrials.gov
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