Application Evaluation Research on the Artificial Intelligence-assisted Support System for the Diagnosis of Colorectal Tubular Adenoma Lesions

NCT07073430 | Recruiting

• 1 other identifier: WDRY2024-K153
• Study Type: observational
• Target: 4,000
• Locations: 1 country
• Sites: 1

Brief Summary

This study is a prospective,multi-center and observational clinical study.Investigators would like to innovatively construct a "trinity" database of colorectal tubular adenomas based on white light - magnifying chromo - pathological images.It simulates the decision - making logic of doctors, and based on the multimodal endoscopic LAFEQ method previously proposed, develop a multimodal deep - learning diagnostic model for colon adenomas and an interpretable risk prediction model for intestinal adenomas. While achieving high - precision auxiliary treatment decisions, clearly present the decision - making basis, and break through the limitation of poor interpretability of previous medical imaging AI models.

Conditions

Artificial Intelligence (AI); Colorectal Adenoma

Outcome Measures

Primary Outcomes (1)

• The accuracy rate of diagnosing adenomas

  The prediction rate of the interpretable artificial intelligence-assisted diagnosis model for the disease risk level.

  during endoscopy

Secondary Outcomes (1)

• The prediction for the disease risk level

  during endoscopy

Study Arms (2)

Traditional colonoscopy examination group

the system shows the original colonoscopy video.

AI-assisted colonoscopy examination group

The system will present the detected polyp positions as hollow blue and set an alarm box directly on the high-definition monitor to mark whether it is a polyp. Hollow red is used to set an alarm box directly on the high-definition monitor to mark whether it is an adenoma.

Device: AI models with NBI

Interventions

AI models with NBI DEVICE

AI models for detecting intestinal adenoma in magnifying endoscopy with NBI.

AI-assisted colonoscopy examination group

Eligibility Criteria

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

Study Population

Patients withonic col adenomas or polyps

You may qualify if:

• Patients aged ≥ 18 years, who need to undergo colonoscopy, regardless of gender.
• Voluntarily sign the informed consent form
• Promise to abide by the research procedures and cooperate in the implementation of the entire research process.

You may not qualify if:

• Patients who has a history of abdominal or pelvic surgery or radiotherapy in the past;
• Patients who has definite active lower gastrointestinal bleeding.
• Existing or suspected hereditary colorectal polyposis, inflammatory bowel disease;
• Uncontrolled hypertension (systolic blood pressure \> 160 mmHg or diastolic blood pressure \> 95 mmHg after standardized treatment)
• There is a history of stroke, coronary artery disease, or vascular disease;
• Pregnant;
• Intestinal preparation cannot be carried out.

Sponsors & Collaborators

• Renmin Hospital of Wuhan University: lead
• Air Force Military Medical University, China: collaborator
• The Sixth Affiliated Hospital, Sun Yat-sen University: collaborator
• Army Medical University, China: collaborator
• Guizhou Provincial People's Hospital: collaborator
• Shengjing Hospital: collaborator
• Zhejiang University: collaborator
• Shandong University: collaborator
• Beijing Friendship Hospital, Captial Medical University: collaborator
• The Second Medical Center, Chinese PLA General Hospital: collaborator

Study Sites (1)

Renmin Hospital of Wuhan University

Wuhan, Hubei, China

RECRUITING

Related Publications (12)

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• van der Velden BHM, Kuijf HJ, Gilhuijs KGA, Viergever MA. Explainable artificial intelligence (XAI) in deep learning-based medical image analysis. Med Image Anal. 2022 Jul;79:102470. doi: 10.1016/j.media.2022.102470. Epub 2022 May 4.

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• Wallace MB, Sharma P, Bhandari P, East J, Antonelli G, Lorenzetti R, Vieth M, Speranza I, Spadaccini M, Desai M, Lukens FJ, Babameto G, Batista D, Singh D, Palmer W, Ramirez F, Palmer R, Lunsford T, Ruff K, Bird-Liebermann E, Ciofoaia V, Arndtz S, Cangemi D, Puddick K, Derfus G, Johal AS, Barawi M, Longo L, Moro L, Repici A, Hassan C. Impact of Artificial Intelligence on Miss Rate of Colorectal Neoplasia. Gastroenterology. 2022 Jul;163(1):295-304.e5. doi: 10.1053/j.gastro.2022.03.007. Epub 2022 Mar 15.

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• Yao L, Li X, Wu Z, Wang J, Luo C, Chen B, Luo R, Zhang L, Zhang C, Tan X, Lu Z, Zhu C, Huang Y, Tan T, Liu Z, Li Y, Li S, Yu H. Effect of artificial intelligence on novice-performed colonoscopy: a multicenter randomized controlled tandem study. Gastrointest Endosc. 2024 Jan;99(1):91-99.e9. doi: 10.1016/j.gie.2023.07.044. Epub 2023 Aug 1.

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MeSH Terms

Interventions

Narrow Band Imaging

Intervention Hierarchy (Ancestors)

Optical Imaging; Diagnostic Imaging; Diagnostic Techniques and Procedures; Diagnosis; Investigative Techniques

Central Study Contacts

Mingkai Chen

CONTACT

13720330580; kaimingchen@163.com

Study Design

• Study Type: observational
• Observational Model: CASE CROSSOVER
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: July 9, 2025
• First Posted: July 18, 2025
• Study Start: November 28, 2023
• Primary Completion (Estimated): October 31, 2026
• Study Completion (Estimated): October 31, 2026
• Last Updated: March 25, 2026

Record last verified: 2025-04

Locations

CN (1)