Development and Validation of an Artificial Intelligence-assisted System for Bowel Cleanliness Assessment Based on Withdrawal Distance Weighting
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 入组人数
- 700
- 试验地点
- 1
- 主要终点
- Prediction accuracy of the models
研究概览
简要总结
To address the limitations of current AI-based systems that rely on the assumption of a "constant withdrawal speed," this study proposes the integration of the UPD-3 endoscopic positioning system. By using colonoscope withdrawal videos in combination with UPD-3 imaging data as training samples, we aim to develop an AI-powered bowel cleanliness assessment system that incorporates "withdrawal distance" as a weighting factor. This approach is expected to yield a more reliable, objective, and clinically applicable intelligent assessment system that better aligns with real-world clinical practice and endoscopists' operational habits.
详细描述
This study developed an intelligent bowel cleanliness assessment system that uses colonoscope withdrawal distance as a weighting factor. The system consists of the following four modules:
- Module 1: Exclusion of Unqualified Frames in Colonoscopy Videos
1.1 A total of 20 randomly selected colonoscope withdrawal videos (from 20 different subjects) were retrospectively collected from the Endoscopy Center database of Huadong Hospital between January 2018 and June 2024. Images were extracted at a rate of 5 frames per second. Clear frames suitable for BBPS scoring and unqualified frames (e.g., blurred, under irrigation, with instrument manipulation, images from the small intestine, outside the patient, or chromoendoscopy images) were manually labeled.
1.2 The labeled images were split into training and validation sets at a 7:3 ratio. A Transformer-based AI classification model was trained on the training set and validated on the validation set.
1.3 An additional 10 independent colonoscope withdrawal videos (from 10 different subjects) were retrospectively collected using the same method for image extraction and manual labeling. These served as an external validation set to assess the accuracy of the AI model in classifying qualified vs. unqualified frames, thereby evaluating its clinical applicability. 2. Module 2: BBPS 0-3 Scoring for Qualified Colonoscopy Images
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Retrospective
入排标准
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Clear colonoscopy images suitable for BBPS scoring
- •Complete and clear colonoscopy videos suitable for BBPS scoring
- •Clear colonoscopy videos with a stable UPD-3 positioning system, without signal drift, disappearance, or other disruptions
排除标准
- •Blurred colonoscopy images
- •Colonoscopy images taken from the small intestine or outside the patient's body
- •Colonoscopy images captured during irrigation or instrument manipulation
- •Colonoscopy images obtained during chromoendoscopy
- •Colonoscopy videos that do not contain the complete withdrawal process
- •Videos in which the UPD-3 colonoscopic positioning system exhibited signal drift, disappearance, or other instability
结局指标
主要结局
Prediction accuracy of the models
时间窗: Immediately after models development
次要结局
未报告次要终点