Intestinal Abnormalities Clinical Trial
Official title:
Intestinal Motility Evaluation by Endoluminal Image Analysis Using Capsule Endoscopy (CE-EIA): a Multi-center Clinical Trial
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.
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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