An observational study in Colonic Diseases, sponsored by Xiuli Zuo. Not yet recruiting. Open to participants aged 18 Years to 75 Years. Per ClinicalTrials.gov, last updated 2026-09-30.
Sponsored by Xiuli Zuo · Observational
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.
142 studies on the registry are indexed under Colonic Diseases; 22 are open to participants now.
This study's planned enrollment of 8,000 is above the median of 170 across 48 observational studies indexed under Colonic Diseases.
Browse Colonic Diseases studies →Xiuli Zuo is the lead sponsor of 10 studies on the registry; 5 are open to participants now.
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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.
Exclusion Criteria:
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.
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.
Time frame: 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.
Time frame: 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).
Time frame: 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.
Time frame: through study completion, an average of 1 year
No study locations are listed for this record.
From the registry record's own update history. This site started tracking changes on Sep 25, 2026; for anything earlier, see the record history on ClinicalTrials.gov ↗
This study is not yet recruiting, as verified in Sep 2026. You cannot join it, but the record below documents what was studied.
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Xiuli Zuo