| Registry | NCT07073430 |
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| Status | Recruiting |
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| Study type | Observational |
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| Enrollment | 4000 (Estimated) |
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| Sponsor | Renmin Hospital of Wuhan University (Other); collaborators: Beijing Friendship Hospital, Captial Medical University, Air Force Military Medical University, China, Sixth Affiliated Hospital, Sun Yat-sen University, Army Medical University, China, Guizhou Provincial People's Hospital, Shengjing Hospital, The Second Medical Center, Chinese PLA General Hospital, Zhejiang University, Shandong University |
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| Conditions | Colorectal Adenoma, Artificial Intelligence (AI) |
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| Interventions | AI models with NBI (Device) |
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| Dates | start 2023-11-28, primary completion 2026-10-31 (estimated), completion 2026-10-31 (estimated), first posted 2025-07-18, last updated 2026-10-01 |
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| Locations | 1 site in China |
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Related Papers Related Patents Related Grants Related Experts
All trials for Colorectal Adenoma All trials for Artificial Intelligence (AI) All trials of AI models with NBI
-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.
Expand all sections · Collapse all
+Arms and interventions
| Arm | Type | Interventions | Description |
|---|
| Traditional colonoscopy examination group | | | the system shows the original colonoscopy video. |
| AI-assisted colonoscopy examination group | | Device: AI models with NBI | 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. |
- AI models with NBI (Device): AI models for detecting intestinal adenoma in magnifying endoscopy with NBI.
+Outcome measures
Primary outcomes
- The accuracy rate of diagnosing adenomas [during endoscopy]: The prediction rate of the interpretable artificial intelligence-assisted diagnosis model for the disease risk level.
Secondary outcomes
- The prediction for the disease risk level [during endoscopy]: The prediction rate of the interpretable artificial intelligence-assisted diagnosis model for the disease risk level.
+Eligibility
Sex: All, ages: 18 Years to any
Inclusion Criteria:
* 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.
Exclusion Criteria:
* 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.
+Locations (1)
| Facility | City | Country | Status |
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| Renmin Hospital of Wuhan University | Wuhan, Hubei | China | Recruiting |
+Publications
- 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. PMID 35521066
- 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. PMID 29454795
- 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. PMID 37141652
- 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. PMID 25204551
- 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. PMID 38206778
- 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. PMID 35576821
- 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. PMID 35089332
- 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. PMID 36639238
- 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. PMID 35304117
- 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. PMID 37536635
- 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. PMID 34530161
- Strum WB. Colorectal Adenomas. N Engl J Med. 2016 Mar 17;374(11):1065-75. doi: 10.1056/NEJMra1513581. No abstract available. PMID 26981936