Clinical trial NCT05349110: Real-time Diagnosis of Diminutive Colorectal Polyps Using AI
Official title: Real Time Computer-aided Diagnosis (CADx) of Diminutive Colorectal Polyps Using Artificial Intelligence
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Colorectal Neoplasms
Colorectal Polyp
Keywords: Artificial intelligence
Computer-aided diagnosis (CADx)
Lead sponsor: Maastricht University Medical Center
Sponsor class: OTHER
Collaborators: name: Catharina Ziekenhuis Eindhoven
name: Eindhoven University of Technology
First posted: 2022-04-27
Enrollment: 105
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patient receiving a colonoscopy because of regular care will be considered eligible for inclusion if at least one diminutive colorectal polyp is encountered during the colonoscopy. Patients receive an endoscopic procedure in the context of the Dutch national screening program, because of gastrointestinal symptoms, or because of follow-up of previously diagnosed bowel diseases.

Brief summary: Correct endoscopic prediction of the histopathology and differentiation between benign, pre-malignant, and malignant colorectal polyps (optical diagnosis) remains difficult. Artificial intelligence has great potential in image analysis in gastrointestinal endoscopy. Aim of this study is to investigate the real-time diagnostic performance of AI4CRP for the classification of diminutive colorectal polyps, and to compare it with the real-time diagnostic performance of commercially available CADx systems.

Detailed description: Correct endoscopic prediction of the histopathology and differentiation between benign, pre-malignant, and malignant colorectal polyps (optical diagnosis) remains difficult. Despite additional training, even experienced endoscopists continue to fail meeting international thresholds set for safe implementation of treatment strategies based on optical diagnosis.

Multiple machine learning techniques - computer-aided diagnosis (CADx) systems - have been developed for applications in medical imaging within colonoscopy and can improve endoscopic classification of colorectal polyps.

Aim of this study is to explore the feasibility of the workflow using AI4CRP (a CNN based CADx system) real-time in the endoscopy suite, and to investigate the real-time diagnostic performance of AI4CRP for the diagnosis of diminutive (\<5mm) colorectal polyps. Secondary, the real-time performance of commercially available CADx systems will be investigated and compared with AI4CRP performance.

Eligibility criteria: Inclusion Criteria:

* Age \>18 years;
* Patients with at least one colorectal polyps encountered during colonoscopy;
* Patients referred for a colonoscopy by the Dutch bowel cancer screening program, patients undergoing a colonoscopy for endoscopic surveillance, or patients undergoing a colonoscopy because of complaints;
* Written informed consent.

Exclusion Criteria:

* Patients with prior history of inflammatory bowel diseases (IBD) or polyposis syndromes;
* Patients with inadequate bowel preparations after adequate washing, suctioning, and cleaning manoeuvres have been performed by the endoscopist;
* Patients undergoing an emergency colonoscopy;
* Written objection in the patient file for participation in scientific research.

Primary outcomes:
measure: Technical feasibility of real-time use of AI4CRP.
timeFrame: 6 months
measure: User interface feasibility of real-time use of AI4CRP.
timeFrame: 6 months
measure: The diagnostic accuracy of AI4CRP per image modality (HDWL, BLI, LCI, i-scan).
timeFrame: 1 year
measure: The sensitivity of AI4CRP per image modality (HDWL, BLI, LCI, i-scan).
timeFrame: 1 year
measure: The specificity of AI4CRP per image modality (HDWL, BLI, LCI, i-scan).
timeFrame: 1 year
measure: The negative predictive value of AI4CRP per image modality (HDWL, BLI, LCI, i-scan).
timeFrame: 1 year
measure: The positive predictive value of AI4CRP per image modality (HDWL, BLI, LCI, i-scan).
timeFrame: 1 year
measure: The Area Under ROC Curve (AUC) of AI4CRP per image modality (HDWL, BLI, LCI, i-scan).
timeFrame: 1 year
Secondary outcomes:
measure: The diagnostic accuracy of AI4CRP per polyp.
timeFrame: 1 year
measure: The sensitivity of AI4CRP per polyp.
timeFrame: 1 year
measure: The specificity of AI4CRP per polyp.
timeFrame: 1 year
measure: The negative predictive value of AI4CRP per polyp.
timeFrame: 1 year
measure: The positive predictive value of AI4CRP per polyp.
timeFrame: 1 year
measure: The Area Under ROC Curve (AUC) of AI4CRP per polyp.
timeFrame: 1 year
measure: The diagnostic accuracy of CAD EYE in BLI mode, per polyp.
timeFrame: 1 year
measure: The sensitivity of CAD EYE in BLI mode, per polyp.
timeFrame: 1 year
measure: The specificity of CAD EYE in BLI mode, per polyp.
timeFrame: 1 year
measure: The negative predictive value of CAD EYE in BLI mode, per polyp.
timeFrame: 1 year
measure: The positive predictive value of CAD EYE in BLI mode, per polyp.
timeFrame: 1 year
measure: The Area Under ROC Curve (AUC) of CAD EYE in BLI mode, per polyp.
timeFrame: 1 year
measure: The diagnostic accuracy of AI4CRP per patient.
timeFrame: 1 year
measure: The diagnostic accuracy of CAD EYE per patient.
timeFrame: 1 year
measure: The localization score of AI4CRP.
timeFrame: 1 year
measure: The difference in diagnostic accuracy of endoscopists per polyp before and after AI.
timeFrame: 1 year
measure: The difference in sensitivity of endoscopists per polyp before and after AI.
timeFrame: 1 year
measure: The difference in specificity of endoscopists per polyp before and after AI.
timeFrame: 1 year
measure: The difference in negative predictive value of endoscopists per polyp before and after AI.
timeFrame: 1 year
measure: The difference in positive predictive value of endoscopists per polyp before and after AI.
timeFrame: 1 year
measure: The agreement in surveillance interval based on optical diagnosis and histopathology.
timeFrame: 1 year
Other outcomes:
Locations:
city: Maastricht
country: Netherlands
facility: Maastricht University Medical Center
state: Limburg
city: Eindhoven
country: Netherlands
facility: Catharina Ziekenhuis Eindhoven
state: Noord-Brabant
Study officials:
affiliation: Maastricht Universitair Medisch Centrum
name: Erik Schoon, Prof Dr MD
role: PRINCIPAL_INVESTIGATOR
References: