Establishing key research questions for the implementation of artificial intelligence in colonoscopy: a modified Delphi method.
Establishing key research questions for the implementation of artificial intelligence in colonoscopy: a modified Delphi method.
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建立人工智能在结肠镜检查中实施的关键研究问题：一种改进的德尔菲方法。
DOI：
10.1055/a-1306-7590
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发表时间：
2021-09
期刊：
影响因子：
9.3
通讯作者：
Lovat LB
中科院分区：
文献类型：
--
作者：
Ahmad OF;Mori Y;Misawa M;Kudo SE;Anderson JT;Bernal J;Berzin TM;Bisschops R;Byrne MF;Chen PJ;East JE;Eelbode T;Elson DS;Gurudu SR;Histace A;Karnes WE;Repici A;Singh R;Valdastri P;Wallace MB;Wang P;Stoyanov D;Lovat LB
人工智能（AI）在结肠镜检查中的研究进展迅速，但广泛的临床应用尚未成为现实。我们的目标是确定最重要的实施研究重点。方法采用已建立的改进德尔菲法确定研究重点。来自9个国家的15名国际专家，包括内窥镜专家和转化计算机科学家/工程师，参与了为期9个月的在线调查。与人工智能在结肠镜检查中的应用相关的问题在第一轮中作为长列表生成，然后在随后的两轮中得分，以确定前10个研究问题。结果排名前10位的问题被分为5个主题。主题1：临床试验设计/终点（4个问题），涉及息肉检测和表征的最佳试验设计，确定评估人工智能的最佳终点，并展示对间隔期癌症发病率的影响。主题2：技术发展（3个问题），包括改进对更具挑战性和晚期病变的检测，减少假阳性率，并最大限度地减少潜伏期。主题3：临床采用/整合（1个问题），将检测和表征有效地结合到一个工作流程中。主题4：数据访问/注释（1个问题），涉及更有效或自动化的数据注释方法，以减轻人类专家的负担。主题5：监管审批（1个问题），与提高监管审批流程效率有关。这是首次报道的人工智能在结肠镜检查中的国际研究优先级设定。研究结果应作为指导未来研究的框架，与关键利益相关者一起加速人工智能在内窥镜检查中的临床应用。
Background Artificial intelligence (AI) research in colonoscopy is progressing rapidly but widespread clinical implementation is not yet a reality. We aimed to identify the top implementation research priorities. Methods An established modified Delphi approach for research priority setting was used. Fifteen international experts, including endoscopists and translational computer scientists/engineers, from nine countries participated in an online survey over 9 months. Questions related to AI implementation in colonoscopy were generated as a long-list in the first round, and then scored in two subsequent rounds to identify the top 10 research questions. Results The top 10 ranked questions were categorized into five themes. Theme 1: clinical trial design/end points (4 questions), related to optimum trial designs for polyp detection and characterization, determining the optimal end points for evaluation of AI, and demonstrating impact on interval cancer rates. Theme 2: technological developments (3 questions), including improving detection of more challenging and advanced lesions, reduction of false-positive rates, and minimizing latency. Theme 3: clinical adoption/integration (1 question), concerning the effective combination of detection and characterization into one workflow. Theme 4: data access/annotation (1 question), concerning more efficient or automated data annotation methods to reduce the burden on human experts. Theme 5: regulatory approval (1 question), related to making regulatory approval processes more efficient. Conclusions This is the first reported international research priority setting exercise for AI in colonoscopy. The study findings should be used as a framework to guide future research with key stakeholders to accelerate the clinical implementation of AI in endoscopy.
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影响因子：
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通讯作者：
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