Artificial Intelligence-Based Automatic Recognition and Localization of Anatomical Sites in Colonoscopy

September 24, 2026 updated by: Xiuli Zuo

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 Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

Study Type

Observational

Enrollment (Estimated)

8000

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years to 75 years (Adult, Older Adult)

Accepts Healthy Volunteers

N/A

Genders Eligible for Study

All

Sampling Method

Non-Probability Sample

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.

Description

Inclusion Criteria:

  1. Aged 18-75 years, male or female;
  2. Undergoing colonoscopy;
  3. Successful cecal intubation with the endoscope reaching the ileocecal valve;
  4. Adequate colonic cleansing, with a Boston Bowel Preparation Scale (BBPS) score ≥2 points in each of the three colonic segments.

Exclusion Criteria:

  1. Reduced life expectancy;
  2. Active gastrointestinal bleeding;
  3. History of lower gastrointestinal tract surgery;
  4. Previous history of colorectal cancer or adenomas;
  5. Prior history of sessile serrated polyps greater than 10 mm in diameter;
  6. Ongoing chemotherapy or radiotherapy;
  7. Diagnosis of inflammatory bowel disease;
  8. Intestinal diseases identified during the current examination, including colorectal cancer, colorectal polyps and inflammatory bowel disease;
  9. Pregnant or lactating women, and participants unwilling to use contraception during the study;
  10. Other behaviours that may increase disease risk, such as heavy alcohol consumption and drug abuse;
  11. Participants unable or unwilling to provide written informed consent;
  12. Unclear colonoscopy videos;
  13. Incomplete colonoscopy videos.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Observational Models: OTHER
  • Time Perspectives: OTHER

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
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.

through study completion, an average of 1 year
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.

through study completion, an average of 1 year
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).

through study completion, an average of 1 year
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.

through study completion, an average of 1 year

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

October 1, 2026

Primary Completion (Estimated)

January 1, 2028

Study Completion (Estimated)

March 1, 2028

Study Registration Dates

First Submitted

September 6, 2026

First Submitted That Met QC Criteria

September 24, 2026

First Posted (Actual)

September 30, 2026

Study Record Updates

Last Update Posted (Actual)

September 30, 2026

Last Update Submitted That Met QC Criteria

September 24, 2026

Last Verified

September 1, 2026

More Information

Terms related to this study

Other Study ID Numbers

  • KYLL-2026-03-062-1

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.