Colorectal PolypNCT06791395
Real-time AI-assisted Endocyroscopy for the Diagnosis of Colorectal Lesions
An observational study of Colorectal Polyp and Colorectal Neoplasms, run by The First Hospital of Jilin University at 3 sites in China.
Trial summary
Colorectal cancer (CRC) is the third most common malignancy and the second leading cause of cancer-related death worldwide. Colonoscopy is considered the preferred method of screening for colorectal cancer, and early and resection detection of colorectal neoplastic lesions can significantly reduce colorectal cancer… Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Colorectal Polyp, Colorectal Neoplasms
- What is being tested?
- Computer-aided diagnosis (CADx) support tool
- How many people
- 570 participants
- Where
- 3 sitesChina
- Study dates
- 5 Feb 2025to 29 Dec 2025
- 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 Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: Patients with one or more colorectal lesions detected during endocytoscopy will be included in the study. The rest of the inclusion and exclusion criteria are as described.
Who may be able to join
- Patients who have at least one colorectal lesion detected during endocytoscopy
- Consent obtained for the study
Who cannot join
- lesions lacking high-quality images;
- Inflammatory bowel disease, familial adenomatous polyposis and other special diseases;
- Submucosal tumors;
- Pathological diagnosis of inflammatory polyps, Peutz-Jeghers polyps, juvenile polyps, lymphoma and other special pathological types.
Trial locations and contacts
3 sites in 1 country.
China3 sites
The First Hospital of Jilin University
Changchun, Jilin, 130021
Shandong Second Provincial General Hospital
Jinan, Shandong, 250000
Meihekou Central Hospital
Meihekou, Jilin, 135000
About this study
Official title: Real-time AI-assisted Endocytoscopy for the Diagnosis of Colorectal Lesions: a Multi-center, Prospective Clinical Study
Colorectal cancer (CRC) is the third most common malignancy and the second leading cause of cancer-related death worldwide. Colonoscopy is considered the preferred method of screening for colorectal cancer, and early and resection detection of colorectal neoplastic lesions can significantly reduce colorectal cancer morbidity and mortality. In order to improve the diagnostic accuracy of endoscopy for colorectal lesions, many endoscopic techniques, such as image-enhanced endoscopy, including narrow band imaging (narrow-band imaging, NBI), magnifying endoscopy, pigment endoscopy, confocal laser endoscopy, and endocytoscopy(EC), are applied clinically. However, with the increasing number of endoscopic resection, the costs associated with the pathological diagnosis of endoscopic resection and resection specimens increase year by year. In clinical practice, some non-neoplastic colorectal lesions may not require resection, so it is important to identify the nature of the lesion during colonoscopy. Leveraging deep neural networks, AI systems support both computer-aided detection (CADe) and computer-aided classification (CADx). CADe specifically focuses on identifying polyps in colonoscopy, with the goal of reducing adenoma miss rates. Hovever, CADx can predict the pathology of the lesion based on the surface condition of the lesion. Endocytoscopy is a kind of ultra-high magnification endoscopy. But it is not something that can be easily mastered by endoscopic doctors. The investigators have previously developed an artificial intelligence system that can assist in endocytoscopy. The investigators plan to conduct a prospective, multicenter clinical trial to verify the accuracy of this CADx in predicting the histological characteristics of colorectal lesions during real-time endocytoscopy.
Read the detailed description
Colonoscopy is currently the gold standard of screening for CRC. Endocytoscopy is a kind of ultra-high magnification endoscopy. Combined with chemical staining and narrow band imaging technology, endoscopists can observe the cell nucleus morphology, gland tube morphology and microvascular morphology with the naked eye, so as to avoid pathological examination and realize the purpose of real-time biopsy in the body. However, the judgment of endocytoscopic images needs a lot of experience to improve the judgment accuracy. Moreover, endoscopists have certain subjective judgments and errors in the process of judging the results. Therefore, artificial intelligence (AI) is proposed for computer-assisted diagnosis in clinic to solve this problem. At present, the available artificial intelligence systems for assisting endoscopists in diagnosing using endocytoscopy are still relatively scarce, and they are based on traditional machine learning methods, which still have certain limitations in terms of accuracy. With the continuous development of computer vision technology, deep learning has been widely applied in the development of endoscopic assistance diagnosis systems. Therefore, the investigators developed a CADx system trained using deep learning to assist endoscopists in making diagnoses when using endocytoscopy. This CADx system can predict the captured endocytoscopy images in real time and display the prediction results, which can assist endoscopists in providing diagnostic references.
However, currently this CADx technology has not yet undergone prospective clinical validation in the clinical setting. The investigators plan to conduct a prospective clinical trial to validate the accuracy of CADx for prediction of colorectal lesions histology in real-time endocytoscopy. This study will prospectively collect the lesions that meet the inclusion and exclusion criteria. After the endoscopists make the diagnosis through endoscopic images and CADx and then undergo endoscopic resection or surgical resection followed by pathological diagnosis, they will compare the artificial intelligence diagnosis results with the gold standard pathological results, and summarize the diagnostic accuracy of this artificial intelligence-assisted diagnostic system for the colorectal lesions.
The investigators plan to conduct a prospective, multi-centre clinical trial to validate the accuracy of CADx support for prediction of colorectal lesions histology in real-time endocytoscopy.
- Conditions
- Colorectal Polyp, Colorectal Neoplasms
- Being tested
- Computer-aided diagnosis (CADx) support tool
- Medical subject headings
- Colorectal Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Computer-aided diagnosis (CADx) support toolDiagnostic test
The CADx support tool will display the prediction results when the endoscopists press the keys on the fixed keyboard. This is performed after the endoscopists first makes an optical prediction of colorectal lesion histology using endocytoscopy as described. The CADx support tool will make a prediction of colorectal lesion histology.
| Study group | Role | What they receive |
|---|---|---|
| Patients with one or more colorectao lesion detected | During colonoscopy, the endoscopist inspect for the presence of colorectal lesions as per routine clinical practice with the CADx turned off. When a colorectal lesion is encountered, the endoscopist will make a prediction on the histology based on the endoscopic diagnosis. Following this, the CADx will be triggered and display the endoscopic image captured by the endoscopist. and the endoscopist will take note of the CADx prediction for the same image.
In addition, other lesion features such as the size and location will be recorded, which is similar to what is performed in routine clinical practice. The lesion will be endoscopic resected or surgery and sent for pathological examination, which will form the "gold standard" for the diagnosis of polyp histology. Receives: Diagnostic Test: Computer-aided diagnosis (CADx) support tool |
What is being measured
The primary outcome is the main question the study is designed to answer.
To evaluate the diagnostic performance of the CAD-stained in diagnosing neoplastic lesions in a clinical setting.
The diagnostic performance will be calculated for comparison with final histology as the gold standard for diagnosis
Measured: 11 months
Secondary outcomes (4)
To evaluate the diagnostic performance of the CAD-NBI in diagnosing neoplastic lesions in a clinical setting.
The diagnostic performance will be calculated for comparison with final histology as the gold standard for diagnosis
Measured: 11 months
To compare the diagnostic performance of CAD-NBI and CAD-stained in colorectal neoplastic lesions different colorectal lesions.
The diagnostic performance of the CAD-NBI and CAD-stained will be calculated for comparison with final histology as the gold standard for diagnosis
Measured: 11 months
To evaluate the diagnostic performance of CAD-NBI and CAD-stained in the diagnosis of neoplastic DRSPs with high confidence
The diagnostic performance of CAD-NBI and CAD-stained will be calculated for comparison with final histology as the gold standard for diagnosis
Measured: 11 months
To evaluate the agreement of post-polypectomy surveillance intervals based on CAD-NBI and CAD-stained predictions with histopathological diagnosis
The agreement of post-polypectomy surveillance intervals based on CAD-NBI and CAD-stained predictions with The pathological diagnosis was made according to the guidelines
Measured: 11 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted24 Jan 2025
- Study start5 Feb 2025
- Primary completion29 Dec 2025
- Study completion29 Dec 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Colorectal PolypStudies of the same condition, open and under way.
- More trials run by The First Hospital of Jilin UniversityThe sponsor's other registered studies.
- 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
- Case only
- Time perspective
- Prospective
- Enrolment
- 570 participants (actual)
Sponsor and investigators
- Lead sponsor
- The First Hospital of Jilin University
- Sponsor type
- University, hospital or other
- Collaborators
- Shandong Provincial Hospital, Meihekou Central Hospital
- Responsible party
- Principal investigator, Hong Xu, The First Hospital of Jilin University
- Investigators
- Hong Xu, Docror, Principal investigator, The First Hospital of Jilin University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06791395
- Sponsor's protocol ID
- 25K013-001
- Status last verified
- Apr 2026
- First submitted
- 20 Jan 2025
- Last updated
- 13 Apr 2026
- Results posted
- No
- 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 13 Apr 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Real-time AI-assisted Endocytoscopy for the Diagnosis of Colorectal Lesions: a Multi-center, Prospective Clinical Study
- ClinicalTrials.gov ID
- NCT06791395
- Acronym / sponsor ID
- 25K013-001
- Conditions
- Colorectal Polyp, Colorectal Neoplasms
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 570 participants (actual)
- Lead sponsor
- The First Hospital of Jilin UniversityUniversity, hospital or other
- Collaborators
- Shandong Provincial Hospital, Meihekou Central Hospital
- Study start
- 5 Feb 2025 (actual)
- Primary completion
- 29 Dec 2025 (actual)
- Study completion
- 29 Dec 2025 (actual)
- First posted
- 24 Jan 2025 (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.
Colorectal cancer (CRC) is the third most common malignancy and the second leading cause of cancer-related death worldwide. Colonoscopy is considered the preferred method of screening for colorectal cancer, and early and resection detection of colorectal neoplastic lesions can significantly reduce colorectal cancer morbidity and mortality. In order to improve the diagnostic accuracy of endoscopy for colorectal lesions, many endoscopic techniques, such as image-enhanced endoscopy, including narrow band imaging (narrow-band imaging, NBI), magnifying endoscopy, pigment endoscopy, confocal laser endoscopy, and endocytoscopy(EC), are applied clinically. However, with the increasing number of endoscopic resection, the costs associated with the pathological diagnosis of endoscopic resection and resection specimens increase year by year. In clinical practice, some non-neoplastic colorectal lesions may not require resection, so it is important to identify the nature of the lesion during colonoscopy. Leveraging deep neural networks, AI systems support both computer-aided detection (CADe) and computer-aided classification (CADx). CADe specifically focuses on identifying polyps in colonoscopy, with the goal of reducing adenoma miss rates. Hovever, CADx can predict the pathology of the lesion based on the surface condition of the lesion. Endocytoscopy is a kind of ultra-high magnification endoscopy. But it is not something that can be easily mastered by endoscopic doctors. The investigators have previously developed an artificial intelligence system that can assist in endocytoscopy. The investigators plan to conduct a prospective, multicenter clinical trial to verify the accuracy of this CADx in predicting the histological characteristics of colorectal lesions during real-time endocytoscopy.
Detailed description
Colonoscopy is currently the gold standard of screening for CRC. Endocytoscopy is a kind of ultra-high magnification endoscopy. Combined with chemical staining and narrow band imaging technology, endoscopists can observe the cell nucleus morphology, gland tube morphology and microvascular morphology with the naked eye, so as to avoid pathological examination and realize the purpose of real-time biopsy in the body. However, the judgment of endocytoscopic images needs a lot of experience to improve the judgment accuracy. Moreover, endoscopists have certain subjective judgments and errors in the process of judging the results. Therefore, artificial intelligence (AI) is proposed for computer-assisted diagnosis in clinic to solve this problem. At present, the available artificial intelligence systems for assisting endoscopists in diagnosing using endocytoscopy are still relatively scarce, and they are based on traditional machine learning methods, which still have certain limitations in terms of accuracy. With the continuous development of computer vision technology, deep learning has been widely applied in the development of endoscopic assistance diagnosis systems. Therefore, the investigators developed a CADx system trained using deep learning to assist endoscopists in making diagnoses when using endocytoscopy. This CADx system can predict the captured endocytoscopy images in real time and display the prediction results, which can assist endoscopists in providing diagnostic references.
However, currently this CADx technology has not yet undergone prospective clinical validation in the clinical setting. The investigators plan to conduct a prospective clinical trial to validate the accuracy of CADx for prediction of colorectal lesions histology in real-time endocytoscopy. This study will prospectively collect the lesions that meet the inclusion and exclusion criteria. After the endoscopists make the diagnosis through endoscopic images and CADx and then undergo endoscopic resection or surgical resection followed by pathological diagnosis, they will compare the artificial intelligence diagnosis results with the gold standard pathological results, and summarize the diagnostic accuracy of this artificial intelligence-assisted diagnostic system for the colorectal lesions.
The investigators plan to conduct a prospective, multi-centre clinical trial to validate the accuracy of CADx support for prediction of colorectal lesions histology in real-time endocytoscopy.
Study design
- Observational model
- Case only
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Patients with one or more colorectao lesion detected | – | Diagnostic Test: Computer-aided diagnosis (CADx) support toolDuring colonoscopy, the endoscopist inspect for the presence of colorectal lesions as per routine clinical practice with the CADx turned off. When a colorectal lesion is encountered, the endoscopist will make a prediction on the histology based on the endoscopic diagnosis. Following this, the CADx will be triggered and display the endoscopic image captured by the endoscopist. and the endoscopist will take note of the CADx prediction for the same image. In addition, other lesion features such as the size and location will be recorded, which is similar to what is performed in routine clinical practice. The lesion will be endoscopic resected or surgery and sent for pathological examination, which will form the "gold standard" for the diagnosis of polyp histology. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Patients with one or more colorectal lesions detected during endocytoscopy will be included in the study. The rest of the inclusion and exclusion criteria are as described.
Key inclusion criteria (2 listed)
- Patients who have at least one colorectal lesion detected during endocytoscopy
- Consent obtained for the study
Key exclusion criteria (4 listed)
- lesions lacking high-quality images;
- Inflammatory bowel disease, familial adenomatous polyposis and other special diseases;
- Submucosal tumors;
- Pathological diagnosis of inflammatory polyps, Peutz-Jeghers polyps, juvenile polyps, lymphoma and other special pathological types.
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 |
|---|---|---|
| Computer-aided diagnosis (CADx) support toolThe CADx support tool will display the prediction results when the endoscopists press the keys on the fixed keyboard. This is performed after the endoscopists first makes an optical prediction of colorectal lesion histology using endocytoscopy as described. The CADx support tool will make a prediction of colorectal lesion histology. | Diagnostic test | Patients with one or more colorectao lesion detected |
Primary outcome
To evaluate the diagnostic performance of the CAD-stained in diagnosing neoplastic lesions in a clinical setting.
Time frame: 11 months
The diagnostic performance will be calculated for comparison with final histology as the gold standard for diagnosis
4 secondary outcomes
To evaluate the diagnostic performance of the CAD-NBI in diagnosing neoplastic lesions in a clinical setting.
Time frame: 11 months
To compare the diagnostic performance of CAD-NBI and CAD-stained in colorectal neoplastic lesions different colorectal lesions.
Time frame: 11 months
To evaluate the diagnostic performance of CAD-NBI and CAD-stained in the diagnosis of neoplastic DRSPs with high confidence
Time frame: 11 months
To evaluate the agreement of post-polypectomy surveillance intervals based on CAD-NBI and CAD-stained predictions with histopathological diagnosis
Time frame: 11 months
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 13 Apr 2026.
- 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.
Hong Xu, Docror principal investigator
The First Hospital of Jilin University
3 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Colorectal Polyp and Colorectal Neoplasms, run by The First Hospital of Jilin University at 3 sites 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 2 inclusion and 4 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: finished, as of the sponsor's last update on 13 Apr 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 3 sites 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
To evaluate the diagnostic performance of the CAD-stained in diagnosing neoplastic lesions in a clinical setting. (time frame: 11 months). 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 13 Apr 2026; AzTrials last refreshed its copy on 8 Nov 2026.