Colorectal Adenoma
Conditions
Keywords
AI system, Colonoscope, Guided Biopsy
Brief summary
Colorectal adenoma is a common disease and frequently-occurring disease in gastroenterology. With the continuous progress of colonoscopy equipment and the gradual improvement of endoscopic accessories, especially the development of chromo-endoscopy and magnifying endoscopy. The observation of the surface structure and capillary morphology of colorectal adenomas can realize optical biopsy. Currently, most clinical endoscopic diagnosis of colorectal diseases is biopsy under colonoscopy, and further treatment options are determined based on the pathological results of the biopsy. The problem is that the pathological diagnosis of some preoperative biopsy is not completely consistent with the pathological diagnosis of postoperative large specimens. Previous studies have found that the pathological diagnosis accuracy rate of preoperative biopsy is only 66-75%, so there is a certain degree of subjectivity in relying solely on colonoscopy white light biopsy. Based on the previous work, the research team has initially established an intelligent recognition model for colorectal adenoma classification (low-grade intraepithelial neoplasia, high-grade intraepithelial neoplasia), and formed a colorectal adenoma of a certain size with annotated endoscopic image data set. Using the YOLO-V4 algorithm, under the Darknet framework, to train an artificial intelligence (AI) system which specifically for adenoma recognition and diagnosis, its accuracy rate has reached more than 90%. This study intends to increase the sample size based on the previous work, and further improve the accuracy of the classification and diagnosis of the AI system, so as to guide the endoscopist to perform targeted biopsy and improve the accuracy of preoperative biopsy.
Interventions
PROCEDUREAI-assisted guided biopsy
The surface of the adenoma was classified and identified by the AI system, and different areas of the adenoma were marked by distribution to guide the endoscopist for biopsy to obtain the poorly differentiated portion of the lesion.
Sponsors
Beijing Tsinghua Chang Gung Hospital
Study design
Observational model
CASE_CONTROL
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
30 Years to 75 Years
Healthy volunteers
No
Inclusion criteria
* Age between 30-75; * Those who have no mental abnormality and can conduct questionnaire surveys; * BBPS ≥ 6; * Colorectal advanced adenoma, and admitted for complete resection with EMR and ESD; * Provide the relevant information required by this study and sign the informed consent.
Exclusion criteria
* Those who cannot provide the relevant information required by this research; * Patients with inflammatory bowel disease; * Those with a history of liver cirrhosis, uncontrolled hypertension, history of myocardial infarction, cardiac insufficiency, renal insufficiency, respiratory failure, diabetic ketosis and electrolyte imbalance and other serious diseases; * Those who cannot stop antiplatelet drugs or anticoagulant drugs; * Those who have not completed full colonoscopy; * Pregnant women.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| The accuracy of AI | June 2023 | Concordance rate between biopsy and postoperative pathology |
| The accuracy of expert with or without AI | June 2023 | Concordance rate between expert experience and postoperative pathology |
| The accuracy of non-expert with or without AI | June 2023 | Concordance rate between non-expert experience and postoperative pathology |
Countries
China
Contacts
Primary ContactRuigang Wang
Backup ContactXuan Jiang
Outcome results
None listed