AI System for Detection and Characterization of Chronic Enteropathies
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- This is an observational study: the protocol does not assign a study treatment.
- Who it may be relevant to
- Registry conditions: Celiac Disease, Small Bowel Mucosal Atrophy or Lesions, Non-celiac Enteropathies. Basic parameters: from 18 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Italy
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Development and Validation of an Artificial Intelligence System for Detection and Characterization of Small Bowel Mucosal Atrophy in Celiac Disease and Non-Celiac Enteropathies: A Multicenter Observational Study
Overview
Coeliac disease (CD) is an immune-mediated enteropathy leading to small intestinal mucosal atrophy. Diagnosis relies on serology and duodenal biopsies, but it can be complicated by patchy lesions and differential diagnosis with Non-Celiac Enteropathies (NCEs). This multicenter observational study aims to develop and validate an Artificial Intelligence (AI) system to detect and characterize small bowel mucosal atrophy and other pathological findings using endoscopic imaging. The study involves a retrospective phase for training the AI model and a prospective phase to validate its diagnostic accuracy compared to standard human assessment.
Detailed description
The study is a multicenter observational non-profit study with a total expected duration of 36 months. It aims to address the challenges in diagnosing CD and NCEs, specifically the subjective nature of endoscopic evaluation and inter-observer variability.
The study proceeds in two phases:
1. Model Development (Retrospective): Training of Deep Learning algorithms on anonymized endoscopic images/videos to identify mucosal atrophy and other lesions (e.g., angiodysplasia, ulcers, polyps). 2. Validation (Prospective): Enrolling patients undergoing small bowel endoscopy to validate the AI system's performance.
The system aims to provide analysis to assist endoscopists, reducing missed lesions and improving diagnostic accuracy.
Validation of the AI system will be performed offline on recorded anonymized endoscopy videos collected prospectively during the validation phase.
Primary outcome measures
- Diagnostic Performance of the AI System [Time frame: Through study completion (36 months)]
Secondary outcome measures (2)
- Comparison of Diagnostic Performance (No AI-assistance vs AI-assisted) [Time frame: Through study completion (36 months)]
- Inter-observer Agreement [Time frame: Through study completion (36 months)]
Eligibility criteria
Inclusion criteria
- Adult patients (age ≥18 years).
- Patients undergoing endoscopic investigation of the small bowel (endoscopy or capsule endoscopy)
Exclusion criteria
- Inability to provide informed consent.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Cohort
Study locations
Italy · 1 center
- ICS Maugeri IRCCS — Pavia
Identifiers
NCT: NCT07387185 · CTSPV36-24