Colorectal PolypCAMNCT06715384
Evaluation of a CAM System for Colorectal Polyp Size Measurement
A study testing Polyp size measurement using autonomous AI measurement or AI-assisted human measurement with the CAM system in people with Colorectal Polyp and Colorectal Adenoma, run by Changhai Hospital at 1 site in China.
Trial summary
Accurate polyp size measurements are essential for risk stratification, selection of polypectomy techniques, and surveillance interval assignments. Evidence indicated that the clinical implementation of artificial intelligence is an optimal tool to improve the measurement of polyps during colonoscopy. Read more about this study
- Who is it for?
- Ages 18 to 85 years
- Conditions the study is for
- Colorectal Polyp, Colorectal Adenoma
- What is being tested?
- Polyp size measurement using autonomous AI measurement or AI-assisted human measurement with the CAM system
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 168 participants
- Where
- 1 siteChina
- Study dates
- 15 Dec 2024to 30 Apr 2025
- Phase
- Interventional
- 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 to 85 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Adults aged 18-75, any gender; 76-85 years eligible case-by-case based on health status.
- Colonoscopy screening, surveillance, or diagnostic participants.
- Informed consent obtained.
Who cannot join
- Anticoagulant use (e.g., aspirin, warfarin) within 7 days prior to colonoscopy or coagulopathy.
- Inflammatory bowel disease.
- Aronchick score >3 at entry.
- Incomplete Case Report Form (CRF) data.
- Emergency colonoscopy.
- Pregnancy or lactation.
- Gastrointestinal obstruction.
- Refusal to participate.
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
Changhai Hospital, Naval Medical University
Shanghai, Shanghai Municipality, 200433
Cheng-Long Wang, MD, +86-18257806772, sunshinewangcl@126.com
Xiang-Yu Sui, MD, +86-19916549745, sui.xiangyu@hotmail.com
About this study
Official title: Performance Evaluation of a Computer-aided Measuring System for Colorectal Polyp Size Measurement: a Prospective Study
Accurate polyp size measurements are essential for risk stratification, selection of polypectomy techniques, and surveillance interval assignments. Evidence indicated that the clinical implementation of artificial intelligence is an optimal tool to improve the measurement of polyps during colonoscopy. This study aimed to evaluate the performance of a computer-aided measuring (CAM) system (EndoDASS) and compare its accuracy with routine sizing methods during real-time colonoscopy.
Read the detailed description
This study will be conducted in 2 phases: the first phase will evaluate the value of the application of the computer-aided measuring (CAM) system for polyp size measurement accuracy in a small sample of clinical videos, recording the corresponding clinical videos for CAM measurements after measuring polyp size using real-time visual assessment and non-scaled device (open biopsy forceps or snare) assessment, and comparing the different methods of polyp size measurement through a video-based analysis [Autonomous artificial intelligence (AI) measurement, AI-assisted human measurement, non-scaled device assessment, and visual assessment] with different groups of endoscopists ( experts, gastroenterologists, surgeons, fellows) evaluating the corresponding clinical videos during real-time measurements, to obtain pilot data on the relative accuracy of polyp size measurement when using the CAM system, to test the feasibility of size measurement of freshly resected polyp specimens and to determine the sample size and reference for evaluating the CAM system in the subsequent phases of a randomized controlled trial of the study. The second phase will assess the value of using the AI-assisted human measurement versus the non-scaled device assessment for polyp size measurement accuracy in a clinical randomized controlled trial using a prospective, multicenter, patient-single-blind, randomized controlled trial design in which subjects are randomly assigned to the CAM group and the non-scaled device measurement group in a 1:1 ratio. Each patient will have a maximum of 3 polyps included in the study.
- Conditions
- Colorectal Polyp, Colorectal Adenoma
- Being tested
- Polyp size measurement using autonomous AI measurement or AI-assisted human measurement with the CAM system
- Keywords
- Artificial Intelligence, Polyp Size, Deep Learning, Colonoscopy, Depth Map
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Polyp size measurement using autonomous AI measurement or AI-assisted human measurement with the CAM systemDiagnostic test
Also called Polyp size measurement with the CAM system
The study of real-time polyp size measurement using the CAM system will be conducted in two phases. Phase I (pilot phase, n=24 polyps, about 27 patients) will be used to assess the feasibility of applying the CAM system in real-time in a clinical video in order to obtain pilot data on the relative accuracy of assessing polyp sizes using autonomous AI measurement and AI-assisted human measurement and to determine the relative accuracy of assessing polyp size in Phase II of the study ( Randomized Controlled Trial) to assess the sample size and reference standards for the CAM system. The sample size for the second phase of the randomized controlled trial (comparing AI-assisted human measurement to non-scaled device) will be based on pilot data in order to compare the relative accuracy of polyp size measurement with AI-assisted human measurement versus non-scaled device in clinical practice.
| Study group | Role | What they receive |
|---|---|---|
| Polyp size measurement with Artificial intelligence (AI) | Experimental | Colonoscopies will be performed according to the standard of care. Patients will have colonoscopies performed using the computer-aided measuring (CAM) system. All measured fresh polyp specimens will be immediately sized in vitro as the ground truth. Receives: Diagnostic Test: Polyp size measurement using autonomous AI measurement or AI-assisted human measurement with the CAM system |
| Polyp size measurement without AI | No intervention | Colonoscopies will be performed according to the standard of care without the CAM system. Patients will undergo a standard colonoscopy. Polyp size measurement will be assessed visually and non-scaled device (open biopsy forceps or snare). All measured fresh polyp specimens will be immediately sized in vitro as the ground truth. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Evaluation of the computer-aided measuring (CAM) system
To evaluate the relative accuracies of the computer-aided measuring (CAM) system measurement (Autonomous AI measurement and AI-assisted human measurement), non-scaled device (open biopsy forceps or snare) assessment or visual assessment for measuring polyp size compared to with measurement of freshly resected polyp specimens by a digital vernier caliper as reference standard. Relative accuracy is defined as "[1-(CAM system measurement - ground truth measurement)/ground truth measurement] x100%". Scale: 0-100. Higher score is a better outcome.
Measured: 7 days
Secondary outcomes (5)
video-based analysis
Compare in video-based analysis with different endoscopists (experts, gastroenterologists, surgeons, fellows) the relative size measurements accuracy of CAM system measurement with visual and non-scaled device assessment. Relative accuracy is defined as "[1-(CAM system measurement ground truth measurement)/ground truth measurement] x100%". Scale: 0-100. Higher score is a better outcome.
Measured: 7 days
Reliability between CAM system measurement and ground truth measurement
Reliability between CAM system measurement, non-scaled device assessment, visual assessment and ground truth measurement is evaluate by concordance correlation coefficient (CCC). CCC is a statistical measure used to evaluate the agreement between two continuous variables.The CCC considers both the correlation and the bias between the two variables. It ranges from -1 to 1；1: Perfect agreement. 0: No agreement. Negative values: Indicate that the variables diverge.
Measured: 7 days
Time taken for polyp size measurement
Measurement time of the AI-assisted human measurement, non-scaled device assessment, and visual estimation refers to the time taken by endoscopists using various measurement methods, starting from when a polyp appears in the field of view for measurement until the report of the polyp size is completed.
Measured: 7 days
Percentage differences between the AI-assisted human measurement and non-scaled device assessment
Calculate percentage differences between the AI-assisted human measurement and non-scaled device assessment.
Measured: 7 days
Instances of overestimation or underestimation by the AI-assisted human measurement and non-scaled device assessment
Evaluate the instances of overestimation or underestimation by the AI-assisted human measurement and non-scaled device assessment.
Measured: 7 days
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted4 Dec 2024
- Study start15 Dec 2024
- Primary completion1 Apr 2025 (estimated)
- Study completion30 Apr 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (32)Papers the sponsor has linked to this study.
- Other trials for Colorectal PolypStudies of the same condition, open and under way.
- More trials run by Changhai HospitalThe sponsor's other registered studies.
- 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
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Factorial
- Model description
- prospective, multi-endoscopist, single-center or multi- centers
- Masking
- Single blind
- Who is masked
- Participant
- Enrolment
- 168 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Changhai Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Zhaoshen Li, Changhai Hospital
- Investigators
- Sheng-Bing Zhao, MD, Principal investigator, Department of Gastroenterology, National Clinical Research Center for Digestive Diseases, Changhai Hospital, Naval Medical University, Shanghai 200433, China.Yu Bai, MD, Study director, Department of Gastroenterology, National Clinical Research Center for Digestive Diseases, Changhai Hospital, Naval Medical University, Shanghai 200433, China.En-Da Yu, MBBS, Study director, Department of Colorectal Surgery and Gastrointestinal Endoscopy Center, Changhai hospital, Naval Medical University, Shanghai 200433, China.Zhao-Shen Li, MD, PhD, Study chair, Department of Gastroenterology, National Clinical Research Center for Digestive Diseases, Changhai Hospital, Naval Medical University, Shanghai 200433, China.
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06715384
- Sponsor's protocol ID
- CHEC2024-389
- Acronym
- CAM
- Status last verified
- Nov 2024
- First submitted
- 26 Nov 2024
- Last updated
- 19 Feb 2025
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: IPD data can be shared with investigators who propose to use the data under the approval of an independent review committee. Data can only be used for the goals specified in the proposal. Data sharing will be implemented between 6-18 months after article publication with a data-sharing agreement signed. Yu Bai can be contacted at the corresponding email.
Publications
Siegel RL, Giaquinto AN, Jemal A. Cancer statistics, 2024. CA Cancer J Clin. 2024 Jan-Feb;74(1):12-49. doi: 10.3322/caac.21820. Epub 2024 Jan 17.
PubMed 38230766Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021 May;71(3):209-249. doi: 10.3322/caac.21660. Epub 2021 Feb 4.
PubMed 33538338Wang J, Chen X, Wu L, et al. An artificial intelligence-based system for measuring the size of gastrointestinal lesions under endoscopy (with video). Chinese Journal of Digestive Endoscopy. 2022;39(12):965-971.
Wu Y, Shih FY, Wang C, et al. The Deep Hybrid Neural Network and an Application on Polyp Detection. Intern J Pattern Recognit Artif Intell. 2024;38(04).
Wang J, Li Y, Chen B, Cheng D, Liao F, Tan T, Xu Q, Liu Z, Huang Y, Zhu C, Cao W, Yao L, Wu Z, Wu L, Zhang C, Xiao B, Xu M, Liu J, Li S, Yu H. A real-time deep learning-based system for colorectal polyp size estimation by white-light endoscopy: development and multicenter prospective validation. Endoscopy. 2024 Apr;56(4):260-270. doi: 10.1055/a-2189-7036. Epub 2023 Oct 12.
PubMed 37827513Abdelrahim M, Saiga H, Maeda N, Hossain E, Ikeda H, Bhandari P. Automated sizing of colorectal polyps using computer vision. Gut. 2022 Jan;71(1):7-9. doi: 10.1136/gutjnl-2021-324510. Epub 2021 Jul 15. No abstract available.
PubMed 34266967Djinbachian R, Haumesser C, Taghiakbari M, Pohl H, Barkun A, Sidani S, Liu Chen Kiow J, Panzini B, Bouchard S, Deslandres E, Alj A, von Renteln D. Autonomous Artificial Intelligence vs Artificial Intelligence-Assisted Human Optical Diagnosis of Colorectal Polyps: A Randomized Controlled Trial. Gastroenterology. 2024 Jul;167(2):392-399.e2. doi: 10.1053/j.gastro.2024.01.044. Epub 2024 Feb 7.
PubMed 38331204Liu Y, Zuo S. Self-supervised monocular depth estimation for gastrointestinal endoscopy. Comput Methods Programs Biomed. 2023 Aug;238:107619. doi: 10.1016/j.cmpb.2023.107619. Epub 2023 May 19.
PubMed 37235969Su R, Liu J, Wu B, Xie Y, Zhang Y, Zhang W, Zhang Y, Wan M, Tian Z, Hu Y. Accurate measurement of colorectal polyps using computer-aided analysis. Eur J Gastroenterol Hepatol. 2021 May 1;33(5):701-708. doi: 10.1097/MEG.0000000000002162.
PubMed 33787542Zuo W, Dai Y, Huang X, Peng RQ, Li X, Liu H. Evaluation of the competence of an artificial intelligence-assisted colonoscopy system in clinical practice: A post hoc analysis. Front Med (Lausanne). 2023 Apr 6;10:1158574. doi: 10.3389/fmed.2023.1158574. eCollection 2023.
PubMed 37089592Sudarevic B, Sodmann P, Kafetzis I, Troya J, Lux TJ, Sassmannshausen Z, Herlod K, Schmidt SA, Brand M, Schottker K, Zoller WG, Meining A, Hann A. Artificial intelligence-based polyp size measurement in gastrointestinal endoscopy using the auxiliary waterjet as a reference. Endoscopy. 2023 Sep;55(9):871-876. doi: 10.1055/a-2077-7398. Epub 2023 Apr 20.
PubMed 37080235Kwak MS, Cha JM, Jeon JW, Yoon JY, Park JW. Artificial intelligence-based measurement outperforms current methods for colorectal polyp size measurement. Dig Endosc. 2022 Sep;34(6):1188-1195. doi: 10.1111/den.14318. Epub 2022 May 19.
PubMed 35385184Plumb AA, Nickerson C, Wooldrage K, Bassett P, Taylor SA, Altman D, Atkin W, Halligan S. Terminal digit preference biases polyp size measurements at endoscopy, computed tomographic colonography, and histopathology. Endoscopy. 2016 Oct;48(10):899-908. doi: 10.1055/s-0042-108727. Epub 2016 Jul 21.
PubMed 27441685Ahmad I, Levine JB, Anderson JC. Endoscopic Measurement of Colorectal Polyps: How Do We Measure Up? Gastroenterology. 2016 Mar;150(3):769-71. doi: 10.1053/j.gastro.2016.01.020. Epub 2016 Jan 25. No abstract available.
PubMed 26820916Eichenseer PJ, Dhanekula R, Jakate S, Mobarhan S, Melson JE. Endoscopic mis-sizing of polyps changes colorectal cancer surveillance recommendations. Dis Colon Rectum. 2013 Mar;56(3):315-21. doi: 10.1097/DCR.0b013e31826dd138.
PubMed 23392145
Study at a glance
From the registry record, last updated by the sponsor on 19 Feb 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Performance Evaluation of a Computer-aided Measuring System for Colorectal Polyp Size Measurement: a Prospective Study
- ClinicalTrials.gov ID
- NCT06715384
- Acronym / sponsor ID
- CAM / CHEC2024-389
- Conditions
- Colorectal Polyp, Colorectal Adenoma
- Keywords
- Artificial Intelligence, Polyp Size, Deep Learning, Colonoscopy, Depth Map
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 168 participants (estimated)
- Lead sponsor
- Changhai HospitalUniversity, hospital or other
- Study start
- 15 Dec 2024 (actual)
- Primary completion
- 1 Apr 2025 (estimated)
- Study completion
- 30 Apr 2025 (estimated)
- First posted
- 4 Dec 2024 (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.
Accurate polyp size measurements are essential for risk stratification, selection of polypectomy techniques, and surveillance interval assignments. Evidence indicated that the clinical implementation of artificial intelligence is an optimal tool to improve the measurement of polyps during colonoscopy. This study aimed to evaluate the performance of a computer-aided measuring (CAM) system (EndoDASS) and compare its accuracy with routine sizing methods during real-time colonoscopy.
Detailed description
This study will be conducted in 2 phases: the first phase will evaluate the value of the application of the computer-aided measuring (CAM) system for polyp size measurement accuracy in a small sample of clinical videos, recording the corresponding clinical videos for CAM measurements after measuring polyp size using real-time visual assessment and non-scaled device (open biopsy forceps or snare) assessment, and comparing the different methods of polyp size measurement through a video-based analysis [Autonomous artificial intelligence (AI) measurement, AI-assisted human measurement, non-scaled device assessment, and visual assessment] with different groups of endoscopists ( experts, gastroenterologists, surgeons, fellows) evaluating the corresponding clinical videos during real-time measurements, to obtain pilot data on the relative accuracy of polyp size measurement when using the CAM system, to test the feasibility of size measurement of freshly resected polyp specimens and to determine the sample size and reference for evaluating the CAM system in the subsequent phases of a randomized controlled trial of the study. The second phase will assess the value of using the AI-assisted human measurement versus the non-scaled device assessment for polyp size measurement accuracy in a clinical randomized controlled trial using a prospective, multicenter, patient-single-blind, randomized controlled trial design in which subjects are randomly assigned to the CAM group and the non-scaled device measurement group in a 1:1 ratio. Each patient will have a maximum of 3 polyps included in the study.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Factorial
- Masking
- Single blind (participant)
- Number of arms
- 2
- Model description
- prospective, multi-endoscopist, single-center or multi- centers
| Arm | Type | Receives |
|---|---|---|
| Polyp size measurement with Artificial intelligence (AI) | Experimental | Diagnostic Test: Polyp size measurement using autonomous AI measurement or AI-assisted human measurement with the CAM systemColonoscopies will be performed according to the standard of care. Patients will have colonoscopies performed using the computer-aided measuring (CAM) system. All measured fresh polyp specimens will be immediately sized in vitro as the ground truth. |
| Polyp size measurement without AI | No intervention | –Colonoscopies will be performed according to the standard of care without the CAM system. Patients will undergo a standard colonoscopy. Polyp size measurement will be assessed visually and non-scaled device (open biopsy forceps or snare). All measured fresh polyp specimens will be immediately sized in vitro as the ground truth. |
Who may qualify
- Ages
- 18 to 85 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (3 listed)
- Adults aged 18-75, any gender; 76-85 years eligible case-by-case based on health status.
- Colonoscopy screening, surveillance, or diagnostic participants.
- Informed consent obtained.
Key exclusion criteria (8 listed)
- Anticoagulant use (e.g., aspirin, warfarin) within 7 days prior to colonoscopy or coagulopathy.
- Inflammatory bowel disease.
- Aronchick score >3 at entry.
- Incomplete Case Report Form (CRF) data.
- Emergency colonoscopy.
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 |
|---|---|---|
| Polyp size measurement using autonomous AI measurement or AI-assisted human measurement with the CAM systemAlso: Polyp size measurement with the CAM systemThe study of real-time polyp size measurement using the CAM system will be conducted in two phases. Phase I (pilot phase, n=24 polyps, about 27 patients) will be used to assess the feasibility of applying the CAM system in real-time in a clinical video in order to obtain pilot data on the relative accuracy of assessing polyp sizes using autonomous AI measurement and AI-assisted human measurement and to determine the relative accuracy of assessing polyp size in Phase II of the study ( Randomized Controlled Trial) to assess the sample size and reference standards for the CAM system. The sample size for the second phase of the randomized controlled trial (comparing AI-assisted human measurement to non-scaled device) will be based on pilot data in order to compare the relative accuracy of polyp size measurement with AI-assisted human measurement versus non-scaled device in clinical practice. | Diagnostic test | Polyp size measurement with Artificial intelligence (AI) |
Primary outcome
Evaluation of the computer-aided measuring (CAM) system
Time frame: 7 days
To evaluate the relative accuracies of the computer-aided measuring (CAM) system measurement (Autonomous AI measurement and AI-assisted human measurement), non-scaled device (open biopsy forceps or snare) assessment or visual assessment for measuring polyp size compared to with measurement of freshly resected polyp specimens by a digital vernier caliper as reference standard. Relative accuracy is defined as "[1-(CAM system measurement - ground truth measurement)/ground truth measurement] x100%". Scale: 0-100. Higher score is a better outcome.
5 secondary outcomes
video-based analysis
Time frame: 7 days
Reliability between CAM system measurement and ground truth measurement
Time frame: 7 days
Time taken for polyp size measurement
Time frame: 7 days
Percentage differences between the AI-assisted human measurement and non-scaled device assessment
Time frame: 7 days
Instances of overestimation or underestimation by the AI-assisted human measurement and non-scaled device assessment
Time frame: 7 days
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 19 Feb 2025.
- Background: PubMed 38230766Siegel RL, Giaquinto AN, Jemal A. Cancer statistics, 2024. CA Cancer J Clin. 2024 Jan-Feb;74(1):12-49. doi: 10.3322/caac.21820. Epub 2024 Jan 17.
- Background: PubMed 33538338Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021 May;71(3):209-249. doi: 10.3322/caac.21660. Epub 2021 Feb 4.
- BackgroundWang J, Chen X, Wu L, et al. An artificial intelligence-based system for measuring the size of gastrointestinal lesions under endoscopy (with video). Chinese Journal of Digestive Endoscopy. 2022;39(12):965-971.
- BackgroundWu Y, Shih FY, Wang C, et al. The Deep Hybrid Neural Network and an Application on Polyp Detection. Intern J Pattern Recognit Artif Intell. 2024;38(04).
- Background: PubMed 37827513Wang J, Li Y, Chen B, Cheng D, Liao F, Tan T, Xu Q, Liu Z, Huang Y, Zhu C, Cao W, Yao L, Wu Z, Wu L, Zhang C, Xiao B, Xu M, Liu J, Li S, Yu H. A real-time deep learning-based system for colorectal polyp size estimation by white-light endoscopy: development and multicenter prospective validation. Endoscopy. 2024 Apr;56(4):260-270. doi: 10.1055/a-2189-7036. Epub 2023 Oct 12.
- Background: PubMed 34266967Abdelrahim M, Saiga H, Maeda N, Hossain E, Ikeda H, Bhandari P. Automated sizing of colorectal polyps using computer vision. Gut. 2022 Jan;71(1):7-9. doi: 10.1136/gutjnl-2021-324510. Epub 2021 Jul 15. No abstract available.
- Background: PubMed 38331204Djinbachian R, Haumesser C, Taghiakbari M, Pohl H, Barkun A, Sidani S, Liu Chen Kiow J, Panzini B, Bouchard S, Deslandres E, Alj A, von Renteln D. Autonomous Artificial Intelligence vs Artificial Intelligence-Assisted Human Optical Diagnosis of Colorectal Polyps: A Randomized Controlled Trial. Gastroenterology. 2024 Jul;167(2):392-399.e2. doi: 10.1053/j.gastro.2024.01.044. Epub 2024 Feb 7.
- Background: PubMed 37235969Liu Y, Zuo S. Self-supervised monocular depth estimation for gastrointestinal endoscopy. Comput Methods Programs Biomed. 2023 Aug;238:107619. doi: 10.1016/j.cmpb.2023.107619. Epub 2023 May 19.
- Background: PubMed 33787542Su R, Liu J, Wu B, Xie Y, Zhang Y, Zhang W, Zhang Y, Wan M, Tian Z, Hu Y. Accurate measurement of colorectal polyps using computer-aided analysis. Eur J Gastroenterol Hepatol. 2021 May 1;33(5):701-708. doi: 10.1097/MEG.0000000000002162.
- Background: PubMed 37089592Zuo W, Dai Y, Huang X, Peng RQ, Li X, Liu H. Evaluation of the competence of an artificial intelligence-assisted colonoscopy system in clinical practice: A post hoc analysis. Front Med (Lausanne). 2023 Apr 6;10:1158574. doi: 10.3389/fmed.2023.1158574. eCollection 2023.
- Background: PubMed 37080235Sudarevic B, Sodmann P, Kafetzis I, Troya J, Lux TJ, Sassmannshausen Z, Herlod K, Schmidt SA, Brand M, Schottker K, Zoller WG, Meining A, Hann A. Artificial intelligence-based polyp size measurement in gastrointestinal endoscopy using the auxiliary waterjet as a reference. Endoscopy. 2023 Sep;55(9):871-876. doi: 10.1055/a-2077-7398. Epub 2023 Apr 20.
- Background: PubMed 35385184Kwak MS, Cha JM, Jeon JW, Yoon JY, Park JW. Artificial intelligence-based measurement outperforms current methods for colorectal polyp size measurement. Dig Endosc. 2022 Sep;34(6):1188-1195. doi: 10.1111/den.14318. Epub 2022 May 19.
- Individual participant dataSharing planned: Yes. IPD data can be shared with investigators who propose to use the data under the approval of an independent review committee. Data can only be used for the goals specified in the proposal. Data sharing will be implemented between 6-18 months after article publication with a data-sharing agreement signed. Yu Bai can be contacted at the corresponding email.
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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.
Sheng-Bing Zhao, MD principal investigator
Department of Gastroenterology, National Clinical Research Center for Digestive Diseases, Changhai Hospital, Naval Medical University, Shanghai 200433, China.
Yu Bai, MD study director
Department of Gastroenterology, National Clinical Research Center for Digestive Diseases, Changhai Hospital, Naval Medical University, Shanghai 200433, China.
En-Da Yu, MBBS study director
Department of Colorectal Surgery and Gastrointestinal Endoscopy Center, Changhai hospital, Naval Medical University, Shanghai 200433, China.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Polyp size measurement using autonomous AI measurement or AI-assisted human measurement with the CAM system in people with Colorectal Polyp and Colorectal Adenoma, run by Changhai Hospital 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 8 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 19 Feb 2025. 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 Cheng-Long Wang, MD (+86-18257806772, sunshinewangcl@126.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Evaluation of the computer-aided measuring (CAM) system (time frame: 7 days). 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 NCT06715384, which also lists 32 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 19 Feb 2025.