- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT06207825
Accuracy of Computer-aided (CADx) System in Real-time Characterization of Colorectal Ulcerative Diseases
A Prospective Study to Evaluate the Diagnostic Accuracy of Computer-aided (CADx) System in Real-time Characterization of Colorectal Ulcerative Diseases
The goal of this observational study is to test the diagnostic accuracy of the newly developed CADx system in predicting the histopathology of colorectal ulcers when compared to expert endoscopists. The main question it aims to answer are to demonstrate whether the newly developed CADx system has a high-level diagnostic accuracy in predicting characterization of colorectal ulcerative diseases.
It is a multi-center study with two phases. The first retrospective phase is the development and validation of a CADx system by feature extraction from endoscopic photos and videos. The second prospective phase is the evaluation and comparison of the diagnostic accuracy between the CADx system, expert endoscopists and junior endoscopists.
Study Overview
Status
Conditions
Intervention / Treatment
Detailed Description
In the first retrospective phase of study, our primary aim is to develop and validate a CADx system. Endoscopic photos and videos will be retrieved from existing database in the study centers (Nanfang Hospital). For each colorectal ulcer, different endoscopic views will be captures.Relevant baseline demographics, laboratory reports, imaging reports, endoscopy reports and histopathology results will be collected for analysis. The location, size and morphology of each colonic lesion will be recorded. The diagnosis of all colorectal ulcerative disease was comprehensively evaluated by independent pathologists and gastroenterologists. In our study, we will focus on the following subtypes of colorectal ulcerative lesions:
1)colorectal cancer (CA);2)Crohn's disease (CD);3)Ulcerative colitis (UC);4)intestinal tuberculosis (ITB);5) ischemic colitis (IC).
All data will be de-identified before central processing to ensure confidentiality. A project-specific serial number will be used to represent each individual subject. All clinical data and de-identified endoscopic images will be kept confidential and will not be shared with any third party.
A training cohort will be developed from majority of the included cases, followed by a validation cohort with the remaining cases. The endoscopic images and videos will be prepared to train the convoluted neural network and recurrent neural network by selecting appropriate regions of interest (ROI). Multiple ROI within the same colorectal ulcerative disease will be collected to reduce selection bias. Annotation and validation of endoscopic images will be performed by research team. The images will be further segmented into tiles of the same size for further processing. Deep learning algorithms will be applied to learn and extract features on the image and video data. We will develop the recurrent convolutional network to leverage the complementary information of visual and temporal features extracted from the video. Validation data are also created under the same principle which enable cross-validation for model accuracy.
In the second prospective phase of study, we aim to compare the diagnostic accuracy between the CADx system, expert endoscopists and junior endoscopists. A set of test images and videos will be collected prospectively from other subjects, according to the previous eligibility criteria, followed by random allocation of computer-generated sequence.
Two expert endoscopists (with more than 5 years of experience in colonoscopy and a total number of procedures more than 1,000) and two junior endoscopists (with less than 3 years of experience in colonoscopy and a total number of procedures less than 500), who are blinded to the final diagnostic result, will be invited to classify the test set images and videos according to the pre-defined subtypes. All endoscopists will assess the test set data independently in a real-time basis. On the other hand, the CADx system will scan the test set images and videos independently. The prediction of ulcer subtypes will be recorded. The formal diagnostic report after evaluation by independent pathologists and gastroenterologists will be regarded as the ground truth.
Study Type
Enrollment (Estimated)
Contacts and Locations
Study Contact
- Name: Xiaobei Luo, PhD
- Phone Number: 17688881428
- Email: luoxiaobei63@126.com
Participation Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
Inclusion Criteria:
- They underwent endoscopic examination and are found to have ulcerative lesions in the large intestine;
- They have endoscopic images and videos captured and stored during colonoscopy which are available to be retrieved;
- They have the complete medical records and clear diagnosis.
Exclusion Criteria:
1)Poor quality endoscopic images and videos defined as:
- Incomplete visualization of the colorectal ulcer due to technical reasons (e.g. out-of-focus, motion-blurred or insufficient illumination);
- Artifacts due to mucus, air bubbles, stool, or blood. 2)Obscured view due to poor bowel preparation; 3)Incomplete medical record; 4)Prior history of intestinal resection, fistula, or anastomosis.
Study Plan
How is the study designed?
Design Details
Cohorts and Interventions
Group / Cohort |
Intervention / Treatment |
---|---|
Test cohort
A set of test images and videos will be collected prospectively from other subjects, according to the eligibility criteria, followed by random allocation of computer-generated sequence.Two expert endoscopists (with more than 5 years of experience in colonoscopy and a total number of procedures more than 1,000) and two junior endoscopists (with less than 3 years of experience in colonoscopy and a total number of procedures less than 500), who are blinded to the final diagnostic result, will be invited to classify the test set images and videos according to the pre-defined subtypes.
All endoscopists will assess the test set data independently in a real-time basis.
On the other hand, the CADx system will scan the test set images and videos independently.
The prediction of ulcer subtypes will be recorded.
The formal diagnostic report after evaluation by independent pathologists and gastroenterologists will be regarded as the ground truth.
|
A set of test images and videos will be collected prospectively from other subjects, according to the eligibility criteria, followed by random allocation of computer-generated sequence.
|
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
The diagnostic accuracy
Time Frame: From 2023-07 to 2026-04
|
The diagnostic accuracy (area under receiver operating characteristic curves, AUROC) of newly developed CADx system in prediction of final histopathology.
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From 2023-07 to 2026-04
|
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
Sensitivity
Time Frame: From 2023-07 to 2026-04
|
The diagnostic sensitivity of newly developed CADx system in prediction of final histopathology.
|
From 2023-07 to 2026-04
|
Specificity
Time Frame: From 2023-07 to 2026-04
|
The diagnostic specificity of newly developed CADx system in prediction of final histopathology.
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From 2023-07 to 2026-04
|
Positive predictive value
Time Frame: From 2023-07 to 2026-04
|
Positive predictive value of newly developed CADx system in prediction of final histopathology.
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From 2023-07 to 2026-04
|
Negative predictive value
Time Frame: From 2023-07 to 2026-04
|
Negative predictive value of newly developed CADx system in prediction of final histopathology.
|
From 2023-07 to 2026-04
|
Collaborators and Investigators
Study record dates
Study Major Dates
Study Start (Estimated)
Primary Completion (Estimated)
Study Completion (Estimated)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Estimated)
Study Record Updates
Last Update Posted (Estimated)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
- NFEC-2023-312
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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