Colonic DiseasesNCT07849881
Artificial Intelligence-Based Automatic Recognition and Localization of Anatomical Sites in Colonoscopy
An observational study of Colonic Diseases, run by Xiuli Zuo.
Trial summary
This study aims to develop, validate and evaluate an artificial intelligence-based system for real-time automatic localization of colorectal anatomical sites during colonoscopy and detection of colonoscopic inspection extent, so as to improve examination quality and the precision of lesion management. Read more about this study
- Who is it for?
- Ages 18 to 75 years
- Conditions the study is for
- Colonic Diseases
- What is being tested?
- Artificial intelligence-based colonoscopic anatomical site recognition system
- How many people
- 8,000 participants
- Where
- No sites listed yet
- Study dates
- 1 Oct 2026to 1 Mar 2028
- How long will I take part?
- 1 Day
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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 75 years
- All sexes
- Adult, Older adult
Study population: Participants aged 18-75 years scheduled for colonoscopy, with successful cecal intubation and adequate bowel preparation. Colonoscopic videos are collected to validate an AI system for real-time anatomical site recognition and localization.
Who may be able to join
- Aged 18-75 years, male or female;
- Undergoing colonoscopy;
- Successful cecal intubation with the endoscope reaching the ileocecal valve;
- Adequate colonic cleansing, with a Boston Bowel Preparation Scale (BBPS) score ≥2 points in each of the three colonic segments.
Who cannot join
- Reduced life expectancy;
- Active gastrointestinal bleeding;
- History of lower gastrointestinal tract surgery;
- Previous history of colorectal cancer or adenomas;
- Prior history of sessile serrated polyps greater than 10 mm in diameter;
- Ongoing chemotherapy or radiotherapy;
Show all 13Show fewer
- Diagnosis of inflammatory bowel disease;
- Intestinal diseases identified during the current examination, including colorectal cancer, colorectal polyps and inflammatory bowel disease;
- Pregnant or lactating women, and participants unwilling to use contraception during the study;
- Other behaviours that may increase disease risk, such as heavy alcohol consumption and drug abuse;
- Participants unable or unwilling to provide written informed consent;
- Unclear colonoscopy videos;
- Incomplete colonoscopy videos.
About this study
This study aims to develop, validate and evaluate an artificial intelligence-based system for real-time automatic localization of colorectal anatomical sites during colonoscopy and detection of colonoscopic inspection extent, so as to improve examination quality and the precision of lesion management. The hypothesis of this study is that a colonoscopic image recognition model built on deep convolutional neural networks can accurately extract intestinal anatomical features from endoscopic video frames. In prospective and retrospective independent validation cohorts, the artificial intelligence model is hypothesized to achieve an overall accuracy of no less than 85% for the multi-class anatomical site localization task across colonic segments, with an expected sensitivity of 90% and specificity of 85%. The model predictions are expected to be in high agreement with assessments by senior endoscopists, with an anticipated Kappa coefficient of 0.80. This will verify the technical feasibility and accuracy of deep learning-driven real-time anatomical localization during colonoscopy.
- Conditions
- Colonic Diseases
- Being tested
- Artificial intelligence-based colonoscopic anatomical site recognition system
- Medical subject headings
- Colonic Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artificial intelligence-based colonoscopic anatomical site recognition systemOther
This artificial intelligence system performs real-time automatic recognition and localization of colorectal anatomical sites and detects the colonoscopic inspection extent during colonoscopy. The model is built with deep convolutional neural networks and validated using endoscopic video frames.
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy
The percentage of samples in which the artificial intelligence model correctly identifies the corresponding intestinal anatomical sites across all endoscopic video frames, relative to the total sample size enrolled in the study. This metric reflects the overall classification accuracy of the model.
Measured: through study completion, an average of 1 year
Sensitivity
The proportion of all true positive samples annotated as a specific intestinal segment by endoscopy experts that are correctly identified as this segment by the artificial intelligence model. This metric primarily evaluates the model's ability to avoid missed detection of the target anatomical site.
Measured: through study completion, an average of 1 year
F1-score
To address the problem of "class imbalance" caused by variable colonoscopy examination time across different intestinal segments, this study adopts the F1-score for comprehensive evaluation. The calculation is performed as follows: first, the precision for a specific intestinal segment is obtained; then the harmonic mean of this precision and sensitivity is calculated. To assess overall performance, the arithmetic mean of the F1-scores for all intestinal segment classifications is finally computed (i.e., macro-averaged F1-score).
Measured: through study completion, an average of 1 year
Macro-AUC
The receiver operating characteristic (ROC) curve reflects the model's overall discriminative performance under different decision thresholds. For this multi-class classification task, the calculation proceeds as follows: each specific intestinal segment is treated as the positive class one by one; the corresponding ROC curve is plotted and the area under the curve (AUC) is calculated for each segment. Thereafter, the arithmetic mean of AUC values across all anatomical categories is computed to obtain the macro-averaged AUC, which serves as the composite metric for evaluating the overall multi-class discriminative ability of the model.
Measured: through study completion, an average of 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted30 Sep 2026
- Study start1 Oct 2026 (estimated)
- Primary completion1 Jan 2028 (estimated)
- Study completion1 Mar 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Colonic DiseasesStudies 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
- Other
- Time perspective
- Other
- Enrolment
- 8,000 participants (estimated)
- Target duration
- 1 Day
Sponsor and investigators
- Lead sponsor
- Xiuli Zuo
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Xiuli Zuo, Shandong University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07849881
- Sponsor's protocol ID
- KYLL-2026-03-062-1
- Status last verified
- Sep 2026
- First submitted
- 6 Sep 2026
- Last updated
- 30 Sep 2026
- Results posted
- No
Study at a glance
From the registry record, last updated by the sponsor on 30 Sep 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence-Based Automatic Recognition and Localization of Anatomical Sites in Colonoscopy
- ClinicalTrials.gov ID
- NCT07849881
- Acronym / sponsor ID
- KYLL-2026-03-062-1
- Conditions
- Colonic Diseases
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 8,000 participants (estimated)
- Lead sponsor
- Xiuli ZuoUniversity, hospital or other
- Study start
- 1 Oct 2026 (estimated)
- Primary completion
- 1 Jan 2028 (estimated)
- Study completion
- 1 Mar 2028 (estimated)
- First posted
- 30 Sep 2026 (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.
This study aims to develop, validate and evaluate an artificial intelligence-based system for real-time automatic localization of colorectal anatomical sites during colonoscopy and detection of colonoscopic inspection extent, so as to improve examination quality and the precision of lesion management. The hypothesis of this study is that a colonoscopic image recognition model built on deep convolutional neural networks can accurately extract intestinal anatomical features from endoscopic video frames. In prospective and retrospective independent validation cohorts, the artificial intelligence model is hypothesized to achieve an overall accuracy of no less than 85% for the multi-class anatomical site localization task across colonic segments, with an expected sensitivity of 90% and specificity of 85%. The model predictions are expected to be in high agreement with assessments by senior endoscopists, with an anticipated Kappa coefficient of 0.80. This will verify the technical feasibility and accuracy of deep learning-driven real-time anatomical localization during colonoscopy.
Study design
- Observational model
- Other
- Time perspective
- Other
- Target follow-up
- 1 Day
Who may qualify
- Ages
- 18 to 75 years
- Sex
- All
- Age groups
- Adult, Older adult
- Study population
- Participants aged 18-75 years scheduled for colonoscopy, with successful cecal intubation and adequate bowel preparation. Colonoscopic videos are collected to validate an AI system for real-time anatomical site recognition and localization.
Key inclusion criteria (4 listed)
- Aged 18-75 years, male or female;
- Undergoing colonoscopy;
- Successful cecal intubation with the endoscope reaching the ileocecal valve;
- Adequate colonic cleansing, with a Boston Bowel Preparation Scale (BBPS) score ≥2 points in each of the three colonic segments.
Key exclusion criteria (13 listed)
- Reduced life expectancy;
- Active gastrointestinal bleeding;
- History of lower gastrointestinal tract surgery;
- Previous history of colorectal cancer or adenomas;
- Prior history of sessile serrated polyps greater than 10 mm in diameter;
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 |
|---|---|---|
| Artificial intelligence-based colonoscopic anatomical site recognition systemThis artificial intelligence system performs real-time automatic recognition and localization of colorectal anatomical sites and detects the colonoscopic inspection extent during colonoscopy. The model is built with deep convolutional neural networks and validated using endoscopic video frames. | Other | – |
Primary outcome
Accuracy
Time frame: through study completion, an average of 1 year
The percentage of samples in which the artificial intelligence model correctly identifies the corresponding intestinal anatomical sites across all endoscopic video frames, relative to the total sample size enrolled in the study. This metric reflects the overall classification accuracy of the model.
Sensitivity
Time frame: through study completion, an average of 1 year
The proportion of all true positive samples annotated as a specific intestinal segment by endoscopy experts that are correctly identified as this segment by the artificial intelligence model. This metric primarily evaluates the model's ability to avoid missed detection of the target anatomical site.
F1-score
Time frame: through study completion, an average of 1 year
To address the problem of "class imbalance" caused by variable colonoscopy examination time across different intestinal segments, this study adopts the F1-score for comprehensive evaluation. The calculation is performed as follows: first, the precision for a specific intestinal segment is obtained; then the harmonic mean of this precision and sensitivity is calculated. To assess overall performance, the arithmetic mean of the F1-scores for all intestinal segment classifications is finally computed (i.e., macro-averaged F1-score).
Macro-AUC
Time frame: through study completion, an average of 1 year
The receiver operating characteristic (ROC) curve reflects the model's overall discriminative performance under different decision thresholds. For this multi-class classification task, the calculation proceeds as follows: each specific intestinal segment is treated as the positive class one by one; the corresponding ROC curve is plotted and the area under the curve (AUC) is calculated for each segment. Thereafter, the arithmetic mean of AUC values across all anatomical categories is computed to obtain the macro-averaged AUC, which serves as the composite metric for evaluating the overall multi-class discriminative ability of the model.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 30 Sep 2026.
Related trials for Colonic Diseases
Active, not recruitingObservational study
An Observational Prospective Long-term Exposure Registry of Adult Patients With Moderate-to-Severe Ulcerative Colitis
Ulcerative Colitis
RecruitingPhase 1/2
Safety, Dosage, and Microbiome Engraftment Study of MGT-006 for Ulcerative Colitis (ROOT Study)
Ulcerative Colitis (UC), Ulcerative Colitis, Active Moderate
Recruiting
IBS Skin Patch Test Food Allergy Study
Irritable Bowel Syndrome
RecruitingPhase 4
A Remotely Conducted Study to Assess Concentration of TREMFYA in Breast Milk of Lactating Women Who Are Receiving TREMFYA Therapeutically
Psoriasis, Colitis, Ulcerative
Enrolling by invitationPhase 2
Herbal Supplement (Biocidin REMOVE) for Bacterial Overgrowth in Irritable Bowel Syndrome
Irritable Bowel Syndrome (IBS), Small Intestinal Bacterial Overgrowth Syndrome (SIBO)
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.
The record lists no contact. Use the official record to reach the sponsor.
Questions about this trial
What is this clinical trial studying?
An observational study of Colonic Diseases, run by Xiuli Zuo.
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 4 inclusion and 13 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: opening soon, as of the sponsor's last update on 30 Sep 2026. Status can change between updates, so confirm with the study contact before making plans.
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?
This study is still under way. Results are not available in the source record yet.
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?
Accuracy (time frame: through study completion, an average of 1 year). The study lists 4 primary outcomes in all. 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 30 Sep 2026.