通过增强的人工智能启用分析 (CESCAIL研究) 规模化进行囊内镜输送的研究 (CESCAIL研究)
Ian Io Lei1, Katie Tompkins1, Elizabeth White2
1Department of Gastroenterology, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK.
概括
人工智能 (AI) 工具在提高结肠杆菌检测效率方面表现有前途. 在CESCAIL研究中,正在评估一种基于人工智能的分析工具 (AiSPEED) 与传统的结肠囊内镜 (CCE) 视频分析方法相比.
科学领域:
- 胃肠病学 胃肠病学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 下部胃肠道 (GI) 诊断面临着显著的容量限制,COVID-19大流行加剧了这种情况,导致诊断积压.
- 深度神经网络 (DNN) 和人工智能的进步为自动化和提高囊视频分析效率提供了机会.
- 在小肠囊内镜中的人工智能应用表明,在结肠囊分析中也有类似改进的潜力.
研究的目的:
- 确定人工智能支持的分析工具 (AiSPEED) 的可行性,准确性和生产力,用于在结肠囊内镜 (CCE) 中检测聚.
- 将人工智能分析的性能与涉及临床医师分析 (黄金标准) 的传统护理途径进行比较.
主要方法:
- 一项多中心诊断准确性研究招募了674名参与者 (追溯和前性).
- 结肠囊视频通过两个途径进行分析:人工智能支持的分析和传统的临床医生分析.
- 对准确性 (敏感性,特异性) 和阅读时间 (在预期队列中) 的报告进行比较.
主要成果:
- 该研究目前正在招募参与者,并在多个英国中心收集数据.
- 目前正在验证人工智能工具 (AiSPEED) 用于检测多重体.
- 将收集关于现实世界的性能和路径执行的观察数据.
结论:
- 标准诊断准确性研究对患者没有额外的风险,因为标准护理途径不受影响.
- 人工智能支持的工具有潜力显著提高下肠道肠道诊断服务的效率和准确性.
- 该CESCAIL研究将提供关于人工智能在结肠囊内镜中的实际实用性的关键数据.
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