In Vivo Computer-aided Prediction of Polyp Histology on White Light Colonoscopy

Study Results

Results pending

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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Recruitment Status

COMPLETED

Total Enrollment

90 participants

Study Classification

OBSERVATIONAL

Study Start Date

2019-01-01

Study Completion Date

2022-12-31

Brief Summary

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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.

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Detailed Description

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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).

Conditions

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Colonoscopy Histology Computer-aided Diagnosis Artificial Intelligence Colorectal Polyp Adenoma Colon Polyp Hyperplastic Polyp

Study Design

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Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Interventions

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AUTOMATED POLYP CLASSIFICATION

COLONIC POLYP HISTOLOGY PREDICTION IN WHITE LIGHT IMAGES COMBINING ARTIFICIAL INTELLIGENCE AND CLINICAL INFORMATION

Intervention Type OTHER

Eligibility Criteria

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Inclusion Criteria

* Age \> 18 years
* Approval of participation in the study. Signature of informed consent
* Patients with at least one polyp of any size/morphology diagnosed in a routine or screening colonoscopy
* Endoscopies performed with high definition endoscopes

Exclusion Criteria

* Age \<18 years
* Refusal to participate in the study
* Polyps partially resected in a previous endoscopy
* Patients with inflammatory disease
* Impossibility to wash remains of stool or mucus on the surface of the polyp

Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No