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

NCT06791408 | Recruiting

• 1 other identifier: 25K014-001
• Study Type: observational
• Target: 600
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Colorectal Polyp

Outcome Measures

Primary Outcomes (4)

• Negative predictive value

  2025-12-31

• sensitivity

  2025-12-31

• specificity

  2025-12-31

• accurary

  2025-12-31

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

patients with colorectal lesions

You may qualify if:

• colorectal lesions

You may not qualify if:

• 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.

Sponsors & Collaborators

• The First Hospital of Jilin University: lead

Study Sites (1)

First Hospital of Jilin University

Changchun, Jilin, 130021, China

RECRUITING

Study Officials

• Hong Xu, Docror

  The First Hospital of Jilin University

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Mingqing Liu, Docror

CONTACT

+8613204300453; liumq23@mails.jlu.edu.cn

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: January 20, 2025
• First Posted: January 24, 2025
• Study Start: February 5, 2025
• Primary Completion: December 31, 2025
• Study Completion: December 31, 2025
• Last Updated: March 7, 2025

Record last verified: 2025-01

Locations

CN (1)