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Research on Endoscopic Precision Biopsy.

NCT05261932 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 40

Last updated 2022-03-02

No results posted yet for this study

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.

Conditions

  • Colorectal Adenoma

Interventions

PROCEDURE

AI-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 & Collaborators

  • Beijing Tsinghua Chang Gung Hospital

    lead OTHER

Principal Investigators

  • Ruigang Wang · Beijing Tsinghua Changgeng Hospital

Eligibility

Min Age
30 Years
Max Age
75 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2021-11-26
Primary Completion
2023-06-01
Completion
2023-11-30

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05261932 on ClinicalTrials.gov