Real-time Diagnosis of Diminutive Colorectal Polyps Using AI

Real Time Computer-aided Diagnosis (CADx) of Diminutive Colorectal Polyps Using Artificial Intelligence

Status

UNKNOWN

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT05349110

Acronym

COMET-OPTICAL

Enrollment

105

Registered

2022-04-27

Start date

2021-08-20

Completion date

2022-12-31

Last updated

2022-05-05

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Colorectal Neoplasms, Colorectal Polyp

Keywords

Artificial intelligence, Computer-aided diagnosis (CADx)

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.

Interventions

DEVICEComputer-aided diagnosis (CADx) systems

* AI4CRP (artificial intelligence for colorectal polyps), a CNN based computer-aided diagnosis system for diagnosis of colorectal polyps (COMET-OPTICAL research group); * CAD EYE, a computer-aided diagnosis system for diagnosis of colorectal polyps (Fujifilm® Corporation, Tokyo, Japan).

Study design

Observational model

COHORT

Time perspective

PROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to No maximum

Healthy volunteers

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.

Design outcomes

Primary

Measure	Time frame	Description
Technical feasibility of real-time use of AI4CRP.	6 months	The technical feasibility of real-time use of AI4CRP in the endoscopy suite regarding a proper reception of the video output from the local endoscopy processor towards AI4CRP (in high definition quality, without any delays in time).
User interface feasibility of real-time use of AI4CRP.	6 months	The user interface feasibility of real-time use of AI4CRP in the endoscopy suite regarding a correct alignment of the user interface of AI4CRP with the video output from the local endoscopy system (resizing image pixels and anonymization).
The diagnostic accuracy of AI4CRP per image modality (HDWL, BLI, LCI, i-scan).	1 year	The real-time diagnostic accuracy of AI4CRP per image modality (HDWL, BLI, LCI, i-scan). Diagnostic accuracy defined as the percentage of correctly optically diagnosed colorectal polyps.
The sensitivity of AI4CRP per image modality (HDWL, BLI, LCI, i-scan).	1 year	The real-time sensitivity of AI4CRP per image modality (HDWL, BLI, LCI, i-scan).
The specificity of AI4CRP per image modality (HDWL, BLI, LCI, i-scan).	1 year	The real-time specificity of AI4CRP per image modality (HDWL, BLI, LCI, i-scan).
The negative predictive value of AI4CRP per image modality (HDWL, BLI, LCI, i-scan).	1 year	The real-time negative predictive value of AI4CRP per image modality (HDWL, BLI, LCI, i-scan).
The positive predictive value of AI4CRP per image modality (HDWL, BLI, LCI, i-scan).	1 year	The real-time positive predictive value of AI4CRP per image modality (HDWL, BLI, LCI, i-scan).
The Area Under ROC Curve (AUC) of AI4CRP per image modality (HDWL, BLI, LCI, i-scan).	1 year	The real-time Area Under ROC Curve (AUC) of AI4CRP per image modality (HDWL, BLI, LCI, i-scan).

Secondary

Countries

Netherlands

Contacts

Primary ContactQuirine van der Zander, Drs MD

q.vanderzander@maastrichtuniversity.nl031433882241

Backup ContactErik Schoon, Prof Dr MD

erik.schoon@catharinaziekenhuis.nl031433882241

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026

Measure	Time frame	Description
The specificity of CAD EYE in BLI mode, per polyp.	1 year	The real-time specificity of CAD EYE in BLI mode, per polyp.
The negative predictive value of CAD EYE in BLI mode, per polyp.	1 year	The real-time negative predictive value of CAD EYE in BLI mode, per polyp.
The positive predictive value of CAD EYE in BLI mode, per polyp.	1 year	The real-time positive predictive value of CAD EYE in BLI mode, per polyp.
The Area Under ROC Curve (AUC) of CAD EYE in BLI mode, per polyp.	1 year	The real-time Area Under ROC Curve (AUC) of CAD EYE in BLI mode, per polyp.
The diagnostic accuracy of AI4CRP per patient.	1 year	The real-time diagnostic accuracy of AI4CRP per patient (in case of multiple polyps per patient).
The diagnostic accuracy of CAD EYE per patient.	1 year	The real-time diagnostic accuracy of CAD EYE per patient (in case of multiple polyps per patient).
The diagnostic accuracy of AI4CRP per polyp.	1 year	The real-time diagnostic accuracy of AI4CRP per polyp (comprising the combination of different imaging modalities).
The difference in diagnostic accuracy of endoscopists per polyp before and after AI.	1 year	The difference in real-time diagnostic accuracy of endoscopists per polyp before and after AI.
The difference in sensitivity of endoscopists per polyp before and after AI.	1 year	The difference in real-time sensitivity of endoscopists per polyp before and after AI.
The difference in specificity of endoscopists per polyp before and after AI.	1 year	The difference in real-time specificity of endoscopists per polyp before and after AI.
The difference in negative predictive value of endoscopists per polyp before and after AI.	1 year	The difference in real-time negative predictive value of endoscopists per polyp before and after AI.
The difference in positive predictive value of endoscopists per polyp before and after AI.	1 year	The difference in real-time positive predictive value of endoscopists per polyp before and after AI.
The agreement in surveillance interval based on optical diagnosis and histopathology.	1 year	The agreement in surveillance interval based on optical diagnosis of diminutive colorectal polyps and histopathology of small and large colorectal polyps, compared to the surveillance interval based on histopathology of all colorectal polyps (diminutive, small, and large).
The localization score of AI4CRP.	1 year	The localization score of AI4CRP regarding the number of images in which the heatmap produced by AI4CRP pointed out the area of interest (scale: correct, incorrect, or partly correct area of interest).
The sensitivity of AI4CRP per polyp.	1 year	The real-time sensitivity of AI4CRP per polyp (comprising the combination of different imaging modalities).
The specificity of AI4CRP per polyp.	1 year	The real-time specificity of AI4CRP per polyp (comprising the combination of different imaging modalities).
The negative predictive value of AI4CRP per polyp.	1 year	The real-time negative predictive value of AI4CRP per polyp (comprising the combination of different imaging modalities).
The positive predictive value of AI4CRP per polyp.	1 year	The real-time positive predictive value of AI4CRP per polyp (comprising the combination of different imaging modalities).
The Area Under ROC Curve (AUC) of AI4CRP per polyp.	1 year	The real-time Area Under ROC Curve (AUC) of AI4CRP per polyp (comprising the combination of different imaging modalities).
The diagnostic accuracy of CAD EYE in BLI mode, per polyp.	1 year	The real-time diagnostic accuracy of CAD EYE in BLI mode, per polyp.
The sensitivity of CAD EYE in BLI mode, per polyp.	1 year	The real-time sensitivity of CAD EYE in BLI mode, per polyp.