Evaluation of a CAM System for Colorectal Polyp Size Measurement
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: Polyp size measurement using autonomous AI measurement or AI-assisted human measurement with the CAM system.
- Who it may be relevant to
- Registry conditions: Colorectal Polyp, Colorectal Adenoma. Basic parameters: 18 years — 85 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- China
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Performance Evaluation of a Computer-aided Measuring System for Colorectal Polyp Size Measurement: a Prospective Study
Overview
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.
Interventions
- Diagnostic test Polyp size measurement using autonomous AI measurement or AI-assisted human 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
Primary outcome measures
- Evaluation of the computer-aided measuring (CAM) system [Time frame: 7 days]
Secondary outcome measures (5)
- 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]
Eligibility criteria
Inclusion criteria
- 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.
Exclusion criteria
- 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.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Allocation
- Randomized
- Model
- Factorial
- Masking
- Single blind
- Primary purpose
- Diagnostic
Study locations
China · 1 center
- Changhai Hospital, Naval Medical University — Shanghai
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. PMID 38230766
- Sung 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. PMID 33538338
- Wang 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. PMID 37827513
- Abdelrahim 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. PMID 34266967
- Djinbachian 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. PMID 38331204
- Liu 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. PMID 37235969
Identifiers
NCT: NCT06715384 · CHEC2024-389