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.
Przegląd badań
Status
Warunki
Interwencja / Leczenie
Typ studiów
Zapisy (Szacowany)
Kryteria uczestnictwa
Kryteria kwalifikacji
Wiek uprawniający do nauki
Akceptuje zdrowych ochotników
Płeć kwalifikująca się do nauki
Metoda próbkowania
Badana populacja
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.
Opis
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.
Plan studiów
Jak projektuje się badanie?
Szczegóły projektu
- Modele obserwacyjne: OTHER
- Perspektywy czasowe: OTHER
Co mierzy badanie?
Podstawowe miary wyniku
Miara wyniku |
Opis środka |
Ramy czasowe |
|---|---|---|
|
Accuracy
Ramy czasowe: 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
Ramy czasowe: 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
Ramy czasowe: 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
Ramy czasowe: 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
|
Współpracownicy i badacze
Sponsor
Daty zapisu na studia
Główne daty studiów
Rozpoczęcie studiów (Szacowany)
Zakończenie podstawowe (Szacowany)
Ukończenie studiów (Szacowany)
Daty rejestracji na studia
Pierwszy przesłany
Pierwszy przesłany, który spełnia kryteria kontroli jakości
Pierwszy wysłany (Rzeczywisty)
Aktualizacje rekordów badań
Ostatnia wysłana aktualizacja (Rzeczywisty)
Ostatnia przesłana aktualizacja, która spełniała kryteria kontroli jakości
Ostatnia weryfikacja
Więcej informacji
Terminy związane z tym badaniem
Dodatkowe istotne warunki MeSH
Inne numery identyfikacyjne badania
- KYLL-2026-03-062-1
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