Evaluation of a CAM System for Colorectal Polyp Size Measurement

Официальное название

Performance Evaluation of a Computer-aided Measuring System for Colorectal Polyp Size Measurement: a Prospective Study

Обзор

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.

Подробное описание

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.

Вмешательства

• Диагностический тест 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

Первичные конечные точки

• Evaluation of the computer-aided measuring (CAM) system [Срок оценки: 7 days]

Вторичные конечные точки (5)

• video-based analysis [Срок оценки: 7 days]
• Reliability between CAM system measurement and ground truth measurement [Срок оценки: 7 days]
• Time taken for polyp size measurement [Срок оценки: 7 days]
• Percentage differences between the AI-assisted human measurement and non-scaled device assessment [Срок оценки: 7 days]
• Instances of overestimation or underestimation by the AI-assisted human measurement and non-scaled device assessment [Срок оценки: 7 days]

Критерии участия

Критерии включения

• 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.

Критерии исключения

• 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.

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Нет

Дизайн исследования

Распределение

Рандомизированное

Модель

Факторный дизайн

Маскирование

Простое слепое

Основная цель

Диагностика

Центры проведения

Китай · 1 центр

• Changhai Hospital, Naval Medical University — Шанхай

Публикации

• 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

Идентификаторы

NCT: NCT06715384 · CHEC2024-389

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

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