Colorectal UlcersNCT06208982
A Computer-aided (CADx）System in Real-time Characterization of Colorectal Ulcerative Diseases
An observational study of Colorectal Ulcers and Computer-aided System, run by Nanfang Hospital, Southern Medical University at 1 site in China.
Trial summary
The goal of this observational study is to construct and validate a Computer-aided (CADx）System in Real-time Characterization of Colorectal Ulcrerative Diseases. The main question it aims to answer are to demonstrate whether the newly developed CADx system has a high-level diagnostic accuracy in predicting… Read more about this study
- Who is it for?
- Ages 18 to 100 years
- Conditions the study is for
- Colorectal Ulcers, Computer-aided System
- What is being tested?
- Colonoscopy images and videos
- How many people
- 1,000 participants
- Where
- 1 siteChina
- Study dates
- 1 Jun 2023to Jun 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 100 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: They underwent endoscopic examination and are found to have ulcerative lesions in the large intestine.
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
Nanfang Hospital, Southern Medical University
Guangzhou, Undefined, undefined
Xiaobei Luo, PhD, 17688881428, luoxiaobei63@126.com
About this study
Official title: Construction and Validation of a Computer-aided (CADx）System in Real-time Characterization of Colorectal Ulcerative Diseases: A Multicenter, Retrospective Study
The goal of this observational study is to construct and validate a Computer-aided (CADx）System in Real-time Characterization of Colorectal Ulcrerative Diseases. 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, retrospective study. The study retrospectively collected colonoscopy images and videos of colorectal ulcers (including colorectal cancer, Crohn's disease, Ulcerative colitis, Intestinal tuberculosis and ischemic enteritis). A training cohort will be developed from majority of the included cases, followed by a validation cohort with the remaining cases. A CADx system in real-time characterization of colonic ulcer diseases was constructed using artificial intelligence to extract endoscopic features from the training set. Subsequently, the performance of the CADx system was preliminarily tested through the validation set.
Read the detailed description
The goal of this observational study is to construct and validate a Computer-aided (CADx）System in Real-time Characterization of Colorectal Ulcrerative Diseases. Endoscopic photos and videos will be retrieved from existing database in the study centers. 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:
- colorectal cancer (CA);
- Crohn's disease (CD);
- Ulcerative colitis (UC);
- intestinal tuberculosis (ITB);
- 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.
- Conditions
- Colorectal Ulcers, Computer-aided System
- Being tested
- Colonoscopy images and videos
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Colonoscopy images and videosDiagnostic test
A CADx system in real-time characterization of colonic ulcer diseases was constructed using artificial intelligence to extract endoscopic features from the training set. Subsequently, the performance of the CADx system was preliminarily tested through the validation set.
| Study group | Role | What they receive |
|---|---|---|
| Colorectal ulcers | Colonoscopy images and videos of colorectal ulcers. Receives: Diagnostic Test: Colonoscopy images and videos |
What is being measured
The primary outcome is the main question the study is designed to answer.
The diagnostic accuracy
The diagnostic accuracy (area under receiver operating characteristic curves, AUROC) of newly developed CADx system in prediction of Characterization of Colorectal Ulcerative Diseases.
Measured: From 2023-06 to 2024-06
Secondary outcomes (2)
Sensitivity
The diagnostic sensitivity of newly developed CADx system in prediction of Characterization of Colorectal Ulcerative Diseases.
Measured: From 2023-06 to 2024-06
Specificity
The diagnostic specificity of newly developed CADx system in prediction of Characterization of Colorectal Ulcerative Diseases.
Measured: From 2023-06 to 2024-06
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted17 Jan 2024
- Study start1 Jun 2023
- Primary completionJun 2024 (estimated)
- Study completionJun 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Colorectal UlcersStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 1,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Nanfang Hospital, Southern Medical University
- Sponsor type
- University, hospital or other
- Collaborators
- Prince of Wales Hospital, Shatin, Hong Kong, ZhuHai Hospital, Wuxi People's Hospital, The University of Hong Kong-Shenzhen Hospital
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06208982
- Sponsor's protocol ID
- NFEC-2023-330
- Status last verified
- Jan 2024
- First submitted
- 7 Jan 2024
- Last updated
- 17 Jan 2024
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 17 Jan 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Construction and Validation of a Computer-aided (CADx）System in Real-time Characterization of Colorectal Ulcerative Diseases: A Multicenter, Retrospective Study
- ClinicalTrials.gov ID
- NCT06208982
- Acronym / sponsor ID
- NFEC-2023-330
- Conditions
- Colorectal Ulcers, Computer-aided System
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 1,000 participants (estimated)
- Lead sponsor
- Nanfang Hospital, Southern Medical UniversityUniversity, hospital or other
- Collaborators
- Prince of Wales Hospital, Shatin, Hong Kong, ZhuHai Hospital, Wuxi People's Hospital, The University of Hong Kong-Shenzhen Hospital
- Study start
- 1 Jun 2023 (actual)
- Primary completion
- Jun 2024 (estimated)
- Study completion
- Jun 2024 (estimated)
- First posted
- 17 Jan 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
The goal of this observational study is to construct and validate a Computer-aided (CADx）System in Real-time Characterization of Colorectal Ulcrerative Diseases. 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, retrospective study. The study retrospectively collected colonoscopy images and videos of colorectal ulcers (including colorectal cancer, Crohn's disease, Ulcerative colitis, Intestinal tuberculosis and ischemic enteritis). A training cohort will be developed from majority of the included cases, followed by a validation cohort with the remaining cases. A CADx system in real-time characterization of colonic ulcer diseases was constructed using artificial intelligence to extract endoscopic features from the training set. Subsequently, the performance of the CADx system was preliminarily tested through the validation set.
Detailed description
The goal of this observational study is to construct and validate a Computer-aided (CADx）System in Real-time Characterization of Colorectal Ulcrerative Diseases. Endoscopic photos and videos will be retrieved from existing database in the study centers. 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:
- colorectal cancer (CA);
- Crohn's disease (CD);
- Ulcerative colitis (UC);
- intestinal tuberculosis (ITB);
- 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.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Colorectal ulcers | – | Diagnostic Test: Colonoscopy images and videosColonoscopy images and videos of colorectal ulcers. |
Who may qualify
- Ages
- 18 to 100 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- They underwent endoscopic examination and are found to have ulcerative lesions in the large intestine.
Key inclusion criteria (3 listed)
- 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.
Key exclusion criteria (3 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Colonoscopy images and videosA CADx system in real-time characterization of colonic ulcer diseases was constructed using artificial intelligence to extract endoscopic features from the training set. Subsequently, the performance of the CADx system was preliminarily tested through the validation set. | Diagnostic test | Colorectal ulcers |
Primary outcome
The diagnostic accuracy
Time frame: From 2023-06 to 2024-06
The diagnostic accuracy (area under receiver operating characteristic curves, AUROC) of newly developed CADx system in prediction of Characterization of Colorectal Ulcerative Diseases.
2 secondary outcomes
Sensitivity
Time frame: From 2023-06 to 2024-06
Specificity
Time frame: From 2023-06 to 2024-06
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 17 Jan 2024.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Colorectal Ulcers and Computer-aided System, run by Nanfang Hospital, Southern Medical University at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 17 Jan 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Xiaobei Luo, PhD (17688881428, luoxiaobei63@126.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The diagnostic accuracy (time frame: From 2023-06 to 2024-06). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 17 Jan 2024.