Clinical Study on the Accuracy of Real-time AI-assisted Endocytoscopy in the Diagnosis of Colorectal Diminutive Polyps

NCT ID: NCT06791408

Last Updated: 2025-03-07

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

RECRUITING

Total Enrollment

600 participants

Study Classification

OBSERVATIONAL

Study Start Date

2025-02-05

Study Completion Date

2025-12-31

Brief Summary

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Colorectal cancer (CRC) is the third most common malignant tumor in the world and the second largest cause of cancer-related death \[1\]. Colonoscopy is considered the preferred method of screening for colorectal cancer, and early and resectable detection of colorectal neoplastic lesions can significantly reduce colorectal cancer morbidity and mortality. In recent years, with the continuous development of endoscopic diagnostic techniques and the standardization and strengthening of endoscopist training, the detection rate of colorectal polyps has increased year by year. As the number of endoscopic excisions increases, the costs associated with endoscopic excision and pathological diagnosis of excised specimens increase year by year. Research results showed that about 90% of the detected polyps were small polyps (6-9 mm) and diminutive polyps (≤5 mm), and nearly half of them were non-neoplastic polyps, so endoscopic resection and histopathological examination were not required \[2, 3\]. In order to reduce unnecessary pathological examination and endoscopic treatment, the American Society of Digestive Endoscopy proposed PIVI strategies: "excise and discard" and "diagnose and do not excise" strategies.

Endocytoscopy is a kind of ultra-high magnification endoscopy. Combined with chemical staining and narrow-band imaging technology, endoscopists can observe and judge the nuclear morphology, glandular duct morphology and microvascular morphology of colorectal lesions by naked eye, thus realizing the purpose of real-time biopsy in vivo. However, it takes a lot of experience accumulation to improve the judgment accuracy of endoscopy images, and endoscopy doctors have certain subjective judgments and errors in the process of judging results. Therefore, in order to solve this problem, Artificial Intelligence (AI) is proposed clinically. Our center has developed an artificial intelligence assisted diagnosis system based on endocytoscopy to assist endocytoscopy in judging the nature of colorectal lesions. However, whether this artificial intelligence assisted diagnosis system is accurate in judging the nature of colorectal diminutive polyps and is suitable for widespread promotion and application of PIVI strategy lacks relevant clinical data. This study intends to carry out this clinical study to verify the diagnostic accuracy of this artificial intelligence in the diagnosis of colorectal diminutive polyps.

Detailed Description

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Conditions

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Colorectal Polyp

Study Design

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Observational Model Type

CASE_ONLY

Study Time Perspective

PROSPECTIVE

Eligibility Criteria

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Inclusion Criteria

* colorectal lesions

Exclusion Criteria

* lesions lacking high-quality images;
* Inflammatory bowel disease, familial adenomatous polyposis and other special diseases;
* submucosal tumors;
* Pathological diagnosis of inflammatory polyps, Peutz-Jeghers polyps, juvenile polyps, lymphoma and other pathological types.
Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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The First Hospital of Jilin University

OTHER

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Principal Investigators

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Hong Xu, Docror

Role: PRINCIPAL_INVESTIGATOR

The First Hospital of Jilin University

Locations

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First Hospital of Jilin University

Changchun, Jilin, China

Site Status RECRUITING

Countries

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China

Central Contacts

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Mingqing Liu, Docror

Role: CONTACT

+8613204300453

Facility Contacts

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Mingqing Liu

Role: primary

+8613204300453

Other Identifiers

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25K014-001

Identifier Type: -

Identifier Source: org_study_id

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