Artificial Intelligence-Based Automatic Recognition and Localization of Anatomical Sites in Colonoscopy
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.
調査の概要
状態
条件
研究の種類
入学 (推定)
参加基準
適格基準
就学可能な年齢
健康ボランティアの受け入れ
受講資格のある性別
サンプリング方法
調査対象母集団
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.
説明
Inclusion Criteria:
- 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.
Exclusion Criteria:
- 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;
- 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.
研究計画
研究はどのように設計されていますか？
デザインの詳細
- 観測モデル：OTHER
- 時間の展望：OTHER
この研究は何を測定していますか？
主要な結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
|---|---|---|
|
Accuracy
時間枠：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
時間枠：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
時間枠：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
時間枠：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
|
協力者と研究者
スポンサー
研究記録日
主要日程の研究
研究開始 (推定)
一次修了 (推定)
研究の完了 (推定)
試験登録日
最初に提出
QC基準を満たした最初の提出物
最初の投稿 (実際)
学習記録の更新
投稿された最後の更新 (実際)
QC基準を満たした最後の更新が送信されました
最終確認日
詳しくは
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