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RecruitingNCT07073430Updated Oct 1, 2026

Development and Demonstration of a Precision Prevention and Treatment System for Colorectal Tubular Adenomas Lesions

An observational study in Colorectal Adenoma and Artificial Intelligence (AI), sponsored by Renmin Hospital of Wuhan University. Recruiting at 1 site in China. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2026-10-01.

Sponsored by Renmin Hospital of Wuhan University · Observational

From the registry’s dates

  • Started Nov 2023; still recruiting 2 years 10 months later.
Study type
Observational
Model
Case-crossover
Time perspective
Prospective
Enrollment
4,000
Ages
18 Years and older
Sex
All
01

Study 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.

02

Conditions studied

  • Colorectal Adenoma
  • Artificial Intelligence (AI)
03

In context

Lead sponsor

Renmin Hospital of Wuhan University is the lead sponsor of 83 studies on the registry; 27 are open to participants now.

Counted across the registry records on this site, refreshed daily.

04

Who can participate

Ages eligible
18 Years and older
Sexes eligible
All
Accepts healthy volunteers
No
Sampling method
Non-probability sample

Study population

Patients withonic col adenomas or polyps

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

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.
05

Study design

Observational model
Case-crossover
Time perspective
Prospective
Enrollment
4,000 participants (estimated)
Patient registry
No

Groups and cohorts

  • 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

  • DeviceAI models with NBI

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

06

What researchers measure

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.

    Time frame: during endoscopy

Secondary outcomes

  1. The prediction for the disease risk level

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

    Time frame: during endoscopy

07

Study locations

1 of 1 sites recruiting
  • Renmin Hospital of Wuhan University
    Wuhan, Hubei, China
    Recruiting
08

References and documents

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. PubMed 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. PubMed 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. PubMed 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. PubMed 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. PubMed 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. PubMed 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. PubMed 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. PubMed 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. PubMed 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. PubMed 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. PubMed 34530161 ↗
  • Strum WB. Colorectal Adenomas. N Engl J Med. 2016 Mar 17;374(11):1065-75. doi: 10.1056/NEJMra1513581. No abstract available. PubMed 26981936 ↗

Individual participant data

Plan to share: Undecided

09

Updates

2 registry updates since Sep 25, 2026
Minor edits
Nothing that changes what the study is or who can join. Edited: identifiers and verification date
2 updates, last Oct 1, 2026
Show all 2 updates
  1. Oct 1, 2026
    Minor edits only
    + 1 other change: identifiers
  2. Sep 30, 2026
    Minor edits only
    + 2 other changes: identifiers and verification date

From the registry record's own update history. This site started tracking changes on Sep 25, 2026; for anything earlier, see the record history on ClinicalTrials.gov ↗

10

Registry details

Key details

Study ID
NCT07073430
Lead sponsor
Renmin Hospital of Wuhan University
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
Responsible party
ChenMingkai (Professor, Wuhan University) — Principal investigator
First posted
Jul 18, 2025
Start date
Nov 28, 2023
Primary completion
Oct 31, 2026 (estimated)
Completion
Oct 31, 2026 (estimated)
Last update
Oct 1, 2026

Study contacts

Mingkai Chen
Contact
kaimingchen@163.com
13720330580

Oversight

Data monitoring committee
No
FDA-regulated drug
No
FDA-regulated device
No
View the source record on ClinicalTrials.gov ↗

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