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A Prospective Study to Evaluate the Diagnostic Accuracy of Computer-aided Diagnosis (CADx) System in Real-time Characterization of Colorectal Neoplasia

A Prospective Study to Evaluate the Diagnostic Accuracy of Computer-aided Diagnosis (CADx) System in Real-time Characterization of Colorectal Neoplasia

Status
Not yet recruiting
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT05414383
Acronym
CADx
Enrollment
510
Registered
2022-06-10
Start date
2024-12-31
Completion date
2025-12-31
Last updated
2024-02-09

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

Conditions

Colorectal Neoplasms

Brief summary

The investigators hypothesize that a newly developed CADx system will have a higher diagnostic accuracy in predicting histopathology of colorectal neoplasia than both expert and junior endoscopists.

Detailed description

Accurate diagnosis and characterization of colorectal polyps is essential before endoscopic resection. Optical diagnosis by enhanced imaging modality (e.g. Narrow Band Imaging, NBI) allows real-time prediction of histopathology. It can assist endoscopists to select the appropriate technique and differentiate between neoplastic or non-neoplastic polyps. Nevertheless, due to the substantial inter-observer variability, the widespread use was limited. Recently, artificial intelligence and computer-aided polyp diagnosis (CADx) systems have evolved rapidly. The major limitation was the heterogeneity from different types of imaging modalities. Endocytoscopic images require extra steps for pre-staining and magnification, which are time consuming and operator dependent. As a result, it limits the generalisability and applicability in real-world settings. A novel CADx system will be developed for real-time histopathological prediction of colorectal neoplasia, by using non-magnified conventional white-light and image enhanced endoscopy (NBI). The diagnostic accuracy of this CADx system will be compared with both expert and junior endoscopists.

Interventions

DEVICECADx

A novel CADx system for real-time histopathological prediction of colorectal neoplasia, by using non-magnified conventional white-light and image enhanced endoscopy.

Sponsors

Nanfang Hospital, Southern Medical University
CollaboratorOTHER
University College, London
CollaboratorOTHER
Chinese University of Hong Kong
Lead SponsorOTHER

Study design

Observational model
COHORT
Time perspective
PROSPECTIVE

Eligibility

Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No

Inclusion criteria

1. They have received colonoscopy for screening, surveillance or symptom investigation; 2. They have endoscopic images and videos captured and stored during colonoscopy which are available to be retrieved; 3. They have histologically proven colorectal neoplasia. 4. Written consent obtained

Exclusion criteria

1. Poor quality endoscopic images and videos defined as: 1. Incomplete visualization of the colorectal neoplasia due to technical reasons (e.g. out-of-focus, motion-blurred or insufficient illumination); 2. Artifacts due to mucus, air bubbles, stool, or blood. 2. Active gastrointestinal bleeding; 3. Fulminant colitis; 4. Obscured view due to poor bowel preparation; 5. Artificial staining of lesion due to chromoendoscopy. 6. Unable to obtain informed consent

Design outcomes

Primary

MeasureTime frameDescription
Diagnostic accuracyDuring the colonoscopyarea under receiver operating characteristic curves, AUROC in prediction of final histopathology

Secondary

MeasureTime frameDescription
SensitivityDuring the colonoscopySensitivity
SpecificityDuring the colonoscopySpecificity
Positive predictive valueDuring the colonoscopyPositive predictive value
Negative predictive valueDuring the colonoscopyNegative predictive value
Diagnostic timeDuring the colonoscopyDiagnostic time

Contacts

Primary ContactFelix Sia
felixsia@cuhk.edu.hk26370428
Backup ContactThomas Lam
thomaslam@cuhk.edu.hk26370428

Outcome results

None listed

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