臨床試験情報

臨床試験ID : UMIN000044748
情報提供元 : 大学病院医療情報ネットワーク研究センター
試験名 : Artificial Intelligence in Colonoscopy for Cancer Prevention -a Randomized Health Service Implementation Trial-
試験の概要 : Removal of adenomas during colonoscopy reduces colorectal cancer incidence and mortality. We and others have recently developed artificial intelligence (AI) systems which aim to optimise colonoscopy quality by increasing the adenoma detection rate (ADR). Meta-analyses of randomized trials at dedicated centres suggests that use of AI in colonoscopy increases ADR of from 20% to 30%, but does not significantly increase detection rates of advanced adenomas. These trials, however, enrolled mainly patients with clinical symptoms. Thus, the unsolved key question is whether the reported increase in adenoma detection is reproducible in population-based cancer screening programs. The ACCEPT trial aims at addressing this important knowledge gap.

基本情報       患者さん一人一人の状況に応じた判断が必要ですので、詳しくは診療を担当している医師にご相談ください

対象疾患名 Colorectal neoplasm
試験のホームページURL

実施施設&進捗状況

試験実施施設
試験のフェーズ 該当せず
試験進捗状況 一般募集中
公開日・最終情報更新日 2021/07/03

試験に参加できる条件

年齢・性別 20歳 ~ 男女両方
選択基準 Eligible for inclusion are participants in the national cancer screening programmes in Norway and Poland who undergo either primary colonoscopy screening or colonoscopy after a positive faecal immunochemical screening test.
除外基準 Exclusion criteria for the screening programmes are applied in the present study (e.g., individuals with previous history of colorectal cancer).

問い合わせ先

実施責任組織 University of Oslo
問い合わせ先組織名 University of Oslo
部署名・担当者名 Clinical Effectiveness Research Group
電話・Email 0047-40894135 ibusiginjp@gmail.com

評価&介入

試験の種類 該当無し
介入の目的 予防・検診・検査
介入の詳細 Real-time use of AI for polyp detection during colonoscopyColonoscopy without use of AI
主要な評価項目・方法 1. Adenoma detection rate (ADR)2. Advanced adenoma detection rate (A-ADR)
副次的な評価項目・方法 1. Proportion of endoscopists with ADRs 25% or higher2. Serrated polyp detection rate3. Mean number of adenomas per colonoscopy4. Learning effect of using AI during colonoscopy5. Benefits from the use of AI according to the expertise6. Comparison of different AI systems7. Colonoscopy performance (e.g., cecal intubation rate, insertion and withdrawal time).8. Rate of adverse events during and within 30 days after colonoscopy9. Incidence of interval colorectal cancers in 10-year follow-up10. Mortality from interval colorectal cancers in 15-year follow-up11. All-cause mortality in 15-year follow-up12. Mortality due to causes other than colorectal cancer in 15-year follow-up13. Cost-effectiveness of using AI in a cancer screening programme for colorectal cancer.14. Retrospective analysis of recorded videos for individuals who have interval cancer or relevant lesions detected after colonoscopy screening.

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