Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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COMPLETED
373 participants
OBSERVATIONAL
2014-01-05
2020-05-31
Brief Summary
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Aim Our aim was to analyse the accuracy of AIPHP and NICE classification based histology predictions and also to compare the results of the two methods.
Methods We examined colorectal polyps obtained from colonoscopy patients who had polypectomy or endoscopic mucosectomy. Polyps detected by white light colonoscopy were observed then by using NBI at the optical maximum magnificent (60x). The obtained and stored NBI magnifying images were analysed by NICE classification and by AIPHP method parallelly. Pathology examinations were performed blinded to the NICE and AIPHP diagnosis, as well. Our AIPHP software is based on a machine learning method. This program measures five geometrical and colour features on the endoscopic image.
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Detailed Description
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Conditions
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Study Design
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COHORT
PROSPECTIVE
Interventions
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artificial intelligence diagnosis
artificial intelligence prediction of colorectal polyp histology
Eligibility Criteria
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Inclusion Criteria
Exclusion Criteria
18 Years
90 Years
ALL
No
Sponsors
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Petz Aladar County Teaching Hospital
OTHER
Responsible Party
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Other Identifiers
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PetzACTHospital
Identifier Type: -
Identifier Source: org_study_id
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