Clinical Study on the Diagnosis of Colorectal Lesions by Real-time Artificial Intelligence Assisted Endocytoscopy Combined With Narrow Band Imaging

NCT06982885 | Recruiting

• 1 other identifier: 25K190-001
• Study Type: observational
• Target: 500
• Locations: 1 country
• Sites: 1

Brief Summary

Colorectal cancer (colorectal cancer, CRC) is the third most common malignant tumor globally and the second leading cause of cancer-related deaths. Colonoscopy is considered the preferred method for screening colorectal cancer; early detection and removal of colorectal neoplasms can significantly reduce the incidence and mortality of colorectal cancer. To improve the diagnostic accuracy of endoscopy in colorectal lesions, many endoscopic techniques have been applied clinically, such as image-enhanced endoscopy, including narrow band imaging (narrow-band imaging, NBI), magnifying endoscopy, chromoendoscopy, confocal laser endoscopy, and endocytoscopy (EC). However, with the increasing number of endoscopic resections, the costs associated with the pathological diagnosis of resected specimens have risen year by year. In clinical practice, some non-neoplastic colorectal lesions may not require resection, so it is important to differentiate the nature of lesions during colonoscopy. Endocytoscopy is an ultra-high magnification endoscope that, when combined with chemical staining and narrowband imaging techniques, allows the endoscopist to observe the nuclear morphology of colorectal lesions, the shape of glands, and the morphology of microvessels through the naked eye. This approach avoids the need for pathological examination, achieving the goal of real-time biopsy in vivo. However, the accuracy of endocytoscopic image interpretation requires extensive experience to improve judgment, and there is a certain degree of subjectivity and error in the endoscopist's assessment process. Therefore, to address this issue, clinical applications have proposed using artificial intelligence (AI) for computer-aided diagnosis. The investigator's center has previously developed an AI-assisted diagnostic system based on endocytoscopy with NBI to assist in determining the nature of colorectal lesions. However, forward-looking clinical studies are still lacking to verify the effectiveness of this AI-assisted system. Thus, the investigator aim to conduct such clinical research to validate the clinical efficacy of this AI.

Conditions

Endocytoscopy

Keywords

endocytoscopy; artificial intelligence

Outcome Measures

Primary Outcomes (1)

• The accuracy of AI in diagnosing tumor lesions (including sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV)) and high confidence rate were evaluated

  2025-12-31

Secondary Outcomes (6)

• The accuracy and high confidence rate of AI in diagnosing non-neoplastic lesions, adenomas and invasive cancers were evaluated

  2025-12-31

• The accuracy and high confidence rate of AI diagnosis of rectosigmoid adenomas ≤5 mm were evaluated

  2025-12-31

• The influence of lesion location, size and shape on artificial intelligence diagnosis of lesion nature was evaluated.

  2025-12-31

• The accuracy and high confidence rate of artificial intelligence, endoscopists and endoscopists combined with artificial intelligence in diagnosing lesion nature were compared

  2025-12-31

• Compare the time it takes for an endoscopist and an AI to make a diagnosis

  2025-12-31

Interventions

artificial intelligence DIAGNOSTIC_TEST

Artificial intelligence assisted diagnostic system was used to diagnose colorectal lesions

Eligibility Criteria

• Sex: all
• 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 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

MeSH Terms

Interventions

Artificial Intelligence

Intervention Hierarchy (Ancestors)

Algorithms; Mathematical Concepts

Central Study Contacts

Mingqing Liu

CONTACT

15043076005; liumq23@mails.jlu.edu.cn

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Director, Head of Gastroenterology and Endoscopy Center, Principal Investigator, Clinical Professor

Study Record Dates

• First Submitted: May 14, 2025
• First Posted: May 21, 2025
• Study Start: May 19, 2025
• Primary Completion: December 31, 2025
• Study Completion: December 31, 2025
• Last Updated: August 7, 2025

Record last verified: 2025-08

Locations

CN (1)