An observational study in Colonoscopy, Histology and Computer-aided Diagnosis, sponsored by Hospital Clinic of Barcelona. Completed at 1 site in Spain. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2023-01-18.
Sponsored by Hospital Clinic of Barcelona · Observational
Our group, prior to the present study, developed a handcrafted predictive model based on the extraction of surface patterns (textons) with a diagnostic accuracy of over 90%24. This method was validated in a small dataset containing only high-quality images.
Artificial intelligence is expected to improve the accuracy of colorectal polyp optical diagnosis. We propose a hybrid approach combining a Deep learning (DL) system with polyp features indicated by clinicians (HybridAI). A pilot in vivo experiment will carried out.
Optical diagnosis aims to predict the histology of a polyp based on its endoscopic features. This practice could avoid histopathological analysis and reduce the derived costs. Under this premise, the American Society of Gastrointestinal Endoscopy (ASGE), in its Preservation and Incorporation of Valuable endoscopic Innovations (PIVI) statement, established a diagnostic threshold for real-time endoscopic assessment of diminutive polyps. The rationale for its implementation is that the prevalence of advanced histology in polyps \< 5mm is very low (0.5%).
Several studies have demonstrated that optical diagnosis of small polyps is safe and feasible in clinical practice and comparable to the current gold standard, histopathology. However, the accuracy of optical diagnosis has been shown to be insufficient in community-based practices or in non-expert hands and the diagnosis is even more difficult in diminutive polyps \< 3 mm in which the discrepancy between the endoscopic and pathological diagnosis is about 15%.
Artificial Intelligence (AI) has emerged as a help tool for polyp characterization.
Aiming to improve optical diagnosis using AI methods, we propose a hybrid approach that combines DL with characteristics of polyps manually indicated by endoscopists (HybridAI).
657 studies on the registry are indexed under Adenoma; 98 are open to participants now.
This study's enrollment of 90 is below the median of 300 across 226 observational studies indexed under Adenoma.
Browse Adenoma studies →Hospital Clinic of Barcelona is the lead sponsor of 319 studies on the registry; 55 are open to participants now.
Counted across the registry records on this site, refreshed daily.
All patients with polyps of any size/morphology, detected in a routine or screening colonoscopy, that are resected endoscopically and recovered for histological analysis will be included.
The images obtained will be used to expand the database.
Exclusion Criteria:
COLONIC POLYP HISTOLOGY PREDICTION IN WHITE LIGHT IMAGES COMBINING ARTIFICIAL INTELLIGENCE AND CLINICAL INFORMATION
Accuracy of the computer-aided system for predicting polyps histology in real clinical practice
The results of the computer-aided system prediction will be compared with the final pathology report, which is the gold standard
Time frame: One year
Plan to share: No
This study is completed, as verified in Jan 2023. You cannot join it, but the record below documents what was studied.
Get an email when the registry record changes — status, dates, results — or when someone posts here.
Sign in to followQuestions and observations about this study, from anyone following it. Not medical advice, and not a channel to the study team — their contact details are on the registry record.
Sign in to join the discussion. Reading takes no account; posting does. You choose a display name, and a pseudonym is the default.
Nothing here yet. If you are running this trial, taking part in it, or weighing whether to, this is the place to say so.
Hospital Clinic of Barcelona