Clinical trial NCT06709209: AI-driven Narrow-band Imaging Score for Disease Assessment and Outcome Prediction in Ulcerative Colitis
Official title: A Novel Simplified Endoscopic Score aiMed at Evaluating Ulcerative cOlitis Activity Through TXI, RDI and NBI Vascular Assessment and at prEdicting Clinical Outcome and Its Applicability in an arTificial Intelligence System: the MONET Study
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Ulcerative Colitis (UC)
Keywords: Ulcerative Colitis
Virtual Chromoendoscopy
Artificial Intelligence
Disease Assessment
Outcome Prediction
Lead sponsor: University College Cork
Sponsor class: OTHER
Collaborators: name: Olympus
First posted: 2024-11-29
Enrollment: 300
Sex: ALL
Minimum age (years): 18
Maximum age (years): 75
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: Males and females between 18 and 75 years old with an established diagnosis of UC, who have undergone colonoscopy for disease assessment and surveillance will be consecutively recruited after obtaining written consent. The sample size in 300.

Brief summary: This international multicentre prospective study aims to develop a new simple score using enhanced endoscopic techniques which focus on the vascular features of the colon and reliably distinguish between a quiescent and a mild inflammation in ulcerative colitis (UC). The diagnostic performance of the new score in defining disease activity/remission compared to existing endoscopic and histological scores and predict long-term clinical outcomes will be evaluated. The study also aims to adapt current artificial intelligence (AI) algorithms for enhanced endoscopic techniques to improve standardization in UC disease assessment and outcome prediction.

Detailed description: This is a multicentre prospective international study. This study aims at developing a new simple endoscopic score using white light endoscopy - high definition (WLE-HD), Texture and colour enhancement imaging (TXI), red dichromatic imaging (RDI) and narrow-band imaging (NBI) modes, focusing on vascular features to distinguish between quiescent versus patchy mild Ulcerative Colitis. It will evaluate the new score's diagnostic performance in defining disease activity/remission compared to existing endoscopic and histological scores and predict long-term clinical outcomes. Finally, it also aims to develop and adapt existing artificial intelligence (AI) algorithms according to WLE-HD, TXI, RDI and NBI to grade and standardize endoscopic and histological disease assessment and predict long-term clinical outcomes.

The study will be divided in several phases:

* In the first phase, the score will be developed on the first 30 consecutive virtual electronic chromoendoscopy (VCE) videos (using TXI-RDI and NBI) of UC patients, with different grade of disease activity. Experts in inflammatory bowel disease (IBD) endoscopy will review images and videos from recruited patients to define the endoscopic mucosal and vascular features of the new score. These will be used for a stepwise discussion. A round table discussion using modified Delphi method will be conducted by experts worldwide to ensure equal participation and identify the best component descriptors of endoscopic vascular healing. The components that will achieve 100% consensus will be selected, and the most important endoscopy predictive variables will be confirmed by using a machine learning technique. Finally, a new endoscopic score will be generated. This should be reproducible, valid and responsive.
* In the second phase, the new endoscopic scoring system will be validated in a large cohort of UC patients, focusing on patients with quiescent disease versus patchy mild colitis. Diagnostic accuracy, interobserver agreement and ability to predict clinical outcome according to the new endoscopic score focused on vascular features assessed with VCE will be evaluated
* In the third phase, the reproducibility of the new endoscopic scoring system will be evaluated among gastroenterologists with different levels of experience through a short survey and a computerised training module.
* In the fourth phase, new and existing AI algorithms will be developed and adapted to these endoscopic videos and histological images to grade and standardize endoscopic and histological disease assessment and predict long-term clinical outcome in UC.

Eligibility criteria: Inclusion Criteria:

* Adult patients aged 18 to 75 years old
* Established diagnosis of UC (for at least six months in duration), independently from their active treatment
* Undergoing endoscopy for disease activity assessment or cancer surveillance.

Exclusion Criteria:

* Contraindications to endoscopy (including toxic megacolon) and biopsies (including severe coagulopathy/thrombocytopenia)
* Poor bowel preparation (defined as total BBPS \<6 or BBPS \<2 in observed segment for sigmoidoscopy)
* Significant co-morbidities limiting life expectancy and conferring high risk of endoscopy
* Pregnant and breast-feeding subjects
* Inability to provide informed consent
* If the participant has been in a recent experimental trial, these must have been completed not less than thirty days prior to this study

Primary outcomes:
measure: Diagnostic performance of the new scoring system
timeFrame: 6 months
Secondary outcomes:
measure: Correlation with existing score
timeFrame: 2 years
measure: AI development
timeFrame: 2 years
Other outcomes:
Locations:
city: Leuven
country: Belgium
facility: University of Leuven
city: Luneburg
country: Germany
facility: Klinikum Luneburg
city: Cork
country: Ireland
facility: University College Cork
state: Co Cork
city: Feltre
country: Italy
facility: Ospedale S. Maria del Prato
state: Belluno
city: Bari
country: Italy
facility: University of Bari
city: Milan
country: Italy
facility: Istituto Clinico Humanitas
city: Milan
country: Italy
facility: University Vita-Salute San Raffaele
city: Naples
country: Italy
facility: University of Naples
city: Pavia
country: Italy
facility: University of Pavia
city: Tokyo
country: Japan
facility: Showa University
city: Singapore
country: Singapore
facility: Changi General Hospital
Study officials:
affiliation: University College Cork
name: Marietta Iacucci, MD
role: PRINCIPAL_INVESTIGATOR
References:
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