Colorectal AdenomaNCT07073430
Development and Demonstration of a Precision Prevention and Treatment System for Colorectal Tubular Adenomas Lesions
An observational study of Colorectal Adenoma and Artificial Intelligence (AI), run by Renmin Hospital of Wuhan University at 1 site in China.
Trial 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… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Colorectal Adenoma, Artificial Intelligence (AI)
- What is being tested?
- AI models with NBI
- Type of treatment
- Medical device
- How many people
- 4,000 participants
- Where
- 1 siteChina
- Study dates
- 28 Nov 2023to 31 Oct 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients withonic col adenomas or polyps
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site, 1 recruiting
- Recruiting
Renmin Hospital of Wuhan University
Wuhan, Hubei
Renmin Hospital of Wuhan University, 13720330580, kaimingchen@163.com
About this study
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
- Colorectal Adenoma, Artificial Intelligence (AI)
- Being tested
- AI models with NBI
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI models with NBIDevice
AI models for detecting intestinal adenoma in magnifying endoscopy with NBI.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Device: AI models with NBI |
What is being measured
The primary outcome is the main question the study is designed to answer.
The accuracy rate of diagnosing adenomas
The prediction rate of the interpretable artificial intelligence-assisted diagnosis model for the disease risk level.
Measured: 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.
Measured: during endoscopy
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted18 Jul 2025
- Study start28 Nov 2023
- Primary completion31 Oct 2026 (estimated)
- Study completion31 Oct 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (12)Papers the sponsor has linked to this study.
- Other trials for Colorectal AdenomaStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case crossover
- Time perspective
- Prospective
- Enrolment
- 4,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Renmin Hospital of Wuhan University
- Sponsor type
- University, hospital or 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
- Responsible party
- Principal investigator, ChenMingkai, Wuhan University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07073430
- Sponsor's protocol ID
- WDRY2024-K135
- Status last verified
- Mar 2026
- First submitted
- 9 Jul 2025
- Last updated
- 1 Oct 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
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 35521066Dekker 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 29454795Zhou 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 37141652Tempany 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 25204551Wang 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 38206778van 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 35576821Stahlschmidt 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 35089332Haight 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 36639238Wallace 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 35304117Yao 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 37536635Glissen 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 34530161Strum WB. Colorectal Adenomas. N Engl J Med. 2016 Mar 17;374(11):1065-75. doi: 10.1056/NEJMra1513581. No abstract available.
PubMed 26981936
Study at a glance
From the registry record, last updated by the sponsor on 1 Oct 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development and Demonstration of a Precision Prevention and Treatment System for Colorectal Tubular Adenomas Lesions
- ClinicalTrials.gov ID
- NCT07073430
- Acronym / sponsor ID
- WDRY2024-K135
- Conditions
- Colorectal Adenoma, Artificial Intelligence (AI)
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 4,000 participants (estimated)
- Lead sponsor
- Renmin Hospital of Wuhan UniversityUniversity, hospital or 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
- Study start
- 28 Nov 2023 (actual)
- Primary completion
- 31 Oct 2026 (estimated)
- Study completion
- 31 Oct 2026 (estimated)
- First posted
- 18 Jul 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Case crossover
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Traditional colonoscopy examination group | – | –the system shows the original colonoscopy video. |
| AI-assisted colonoscopy examination group | – | Device: AI models with NBIThe 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients withonic col adenomas or polyps
Key inclusion criteria (3 listed)
- 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.
Key exclusion criteria (7 listed)
- 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;
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI models with NBIAI models for detecting intestinal adenoma in magnifying endoscopy with NBI. | Device | AI-assisted colonoscopy examination group |
Primary outcome
The accuracy rate of diagnosing adenomas
Time frame: during endoscopy
The prediction rate of the interpretable artificial intelligence-assisted diagnosis model for the disease risk level.
1 secondary outcome
The prediction for the disease risk level
Time frame: during endoscopy
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 1 Oct 2026.
- Background: PubMed 35521066Li 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.
- Background: PubMed 29454795Dekker 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.
- Background: PubMed 37141652Zhou 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.
- Background: PubMed 25204551Tempany 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.
- Background: PubMed 38206778Wang 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.
- Background: PubMed 35576821van 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.
- Background: PubMed 35089332Stahlschmidt 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.
- Background: PubMed 36639238Haight 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.
- Background: PubMed 35304117Wallace 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.
- Background: PubMed 37536635Yao 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.
- Background: PubMed 34530161Glissen 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.
- Background: PubMed 26981936Strum WB. Colorectal Adenomas. N Engl J Med. 2016 Mar 17;374(11):1065-75. doi: 10.1056/NEJMra1513581. No abstract available.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Colorectal Adenoma and Artificial Intelligence (AI), run by Renmin Hospital of Wuhan University at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 7 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 1 Oct 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Mingkai Chen (13720330580, kaimingchen@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The accuracy rate of diagnosing adenomas (time frame: during endoscopy). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07073430, which also lists 12 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 1 Oct 2026.