Evaluation of Artificial Intelligence System in Diagnosis of Colorectal Tubular Adenoma Lesions
NCT ID: NCT07073430
Last Updated: 2025-07-18
Study Results
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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RECRUITING
4200 participants
OBSERVATIONAL
2023-11-01
2026-10-31
Brief Summary
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Detailed Description
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Conditions
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Study Design
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COHORT
PROSPECTIVE
Study Groups
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Traditional colonoscopy examination group
the system shows the original colonoscopy video.
No interventions assigned to this group
AI-assisted colonoscopy examination group
the system presents the detected polyp location with a hollow blue alert box directly on a high definition monitor,marking whether it is an adenoma or not and the probability of it.
AI models with NBI
AI models for detecting intestinal adenoma in magnifying endoscopy with NBI.
Interventions
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AI models with NBI
AI models for detecting intestinal adenoma in magnifying endoscopy with NBI.
Eligibility Criteria
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Inclusion Criteria
* Voluntarily sign the informed consent form
* Promise to abide by the research procedures and cooperate in the implementation of the entire research process.
Exclusion Criteria
* 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.
18 Years
ALL
No
Sponsors
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Beijing Friendship Hospital, Captial Medical University
UNKNOWN
Air Force Military Medical University, China
OTHER
The Sixth Affiliated Hospital, Sun Yat-sen University
UNKNOWN
Army Medical University, China
OTHER
Guizhou Provincial People's Hospital
OTHER
Shengjing Hospital
OTHER
Zhejiang University
OTHER
Shandong University
OTHER
The Second Medical Center of The General Hospital of the People's Liberation Army
UNKNOWN
Renmin Hospital of Wuhan University
OTHER
Responsible Party
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ChenMingkai
Professor
Principal Investigators
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Mingkai Chen, PHD
Role: PRINCIPAL_INVESTIGATOR
Renmin Hospital of Wuhan University
Locations
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Renmin Hospital of Wuhan University
Wuhan, Hubei, China
Countries
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Central Contacts
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Facility Contacts
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References
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Strum WB. Colorectal Adenomas. N Engl J Med. 2016 Mar 17;374(11):1065-75. doi: 10.1056/NEJMra1513581. No abstract available.
Glissen Brown JR, Mansour NM, Wang P, Chuchuca MA, Minchenberg SB, Chandnani M, Liu L, Gross SA, Sengupta N, Berzin TM. Deep Learning Computer-aided Polyp Detection Reduces Adenoma Miss Rate: A United States Multi-center Randomized Tandem Colonoscopy Study (CADeT-CS Trial). Clin Gastroenterol Hepatol. 2022 Jul;20(7):1499-1507.e4. doi: 10.1016/j.cgh.2021.09.009. Epub 2021 Sep 14.
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.
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.
Haight TJ, Eshaghi A. Deep Learning Algorithms for Brain Imaging: From Black Box to Clinical Toolbox? Neurology. 2023 Mar 21;100(12):549-550. doi: 10.1212/WNL.0000000000206808. Epub 2023 Jan 13. No abstract available.
Stahlschmidt SR, Ulfenborg B, Synnergren J. Multimodal deep learning for biomedical data fusion: a review. Brief Bioinform. 2022 Mar 10;23(2):bbab569. doi: 10.1093/bib/bbab569.
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.
Wang Y, Zhen L, Tan TE, Fu H, Feng Y, Wang Z, Xu X, Goh RSM, Ng Y, Calhoun C, Tan GSW, Sun JK, Liu Y, Ting DSW. Geometric Correspondence-Based Multimodal Learning for Ophthalmic Image Analysis. IEEE Trans Med Imaging. 2024 May;43(5):1945-1957. doi: 10.1109/TMI.2024.3352602. Epub 2024 May 2.
Tempany CM, Jayender J, Kapur T, Bueno R, Golby A, Agar N, Jolesz FA. Multimodal imaging for improved diagnosis and treatment of cancers. Cancer. 2015 Mar 15;121(6):817-27. doi: 10.1002/cncr.29012. Epub 2014 Sep 9.
Zhou T, Cheng Q, Lu H, Li Q, Zhang X, Qiu S. Deep learning methods for medical image fusion: A review. Comput Biol Med. 2023 Jun;160:106959. doi: 10.1016/j.compbiomed.2023.106959. Epub 2023 Apr 20.
Dekker E, Rex DK. Advances in CRC Prevention: Screening and Surveillance. Gastroenterology. 2018 May;154(7):1970-1984. doi: 10.1053/j.gastro.2018.01.069. Epub 2018 Feb 15.
Li J, Zhu Y, Dong Z, He X, Xu M, Liu J, Zhang M, Tao X, Du H, Chen D, Huang L, Shang R, Zhang L, Luo R, Zhou W, Deng Y, Huang X, Li Y, Chen B, Gong R, Zhang C, Li X, Wu L, Yu H. Development and validation of a feature extraction-based logical anthropomorphic diagnostic system for early gastric cancer: A case-control study. EClinicalMedicine. 2022 Mar 30;46:101366. doi: 10.1016/j.eclinm.2022.101366. eCollection 2022 Apr.
Other Identifiers
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WDRY2024-K153
Identifier Type: -
Identifier Source: org_study_id
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