人工智能能在评估性结肠炎的粘膜愈合时取代内镜医生吗? 一个系统的审查和诊断测试准确性的元分析
Alessandro Rimondi1, Klaus Gottlieb2, Edward J Despott1
1Royal Free Unit for Endoscopy, The Royal Free Hospital and University College London Institute for Liver and Digestive Health, Hampstead, London, United Kingdom.
人工智能 (AI) 在评估性结肠炎 (UC) 患者的粘膜愈合方面具有很高的潜力. 建议进行标准化的AI培训,以减少数据异质性,并提高临床试验中的诊断性能.
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:
- 粘膜愈合 (MH) 是炎症性肠病 (IBD) 的一个关键治疗目标.
- 准确评估内镜炎症对于IBD的临床决策至关重要.
- 目前用于分级内镜炎症的方法面临着观察者间协议和中央阅读要求的挑战.
研究的目的:
- 系统地审查和元分析AI系统在评估性结肠炎 (UC) 中MH的诊断性能.
- 确定AI是否可以取代内镜师在IBD临床试验中评估MH.
主要方法:
- 在2022年12月1日之前对EMBASE和MEDLINE数据库进行系统审查.
- 用诊断几率比率 (DOR) 对评估UCAI的研究进行元分析.
- 使用 QUADAS-2 工具评估的偏差风险.
主要成果:
- 在元分析中包含了12项研究,评估了深度学习算法 (CNN) 用于UC的MH评估.
- 人工智能在评估固定图像 (灵敏度0.91,特异性0.89) 和视频 (灵敏度0.86,特异性0.91) 中表现出很高的性能.
- 观察到中等到高的异质性,可能会限制证据的质量.
结论:
- 人工智能系统具有显著的潜力,可以在UC中准确检测MH.
- 建议采用标准化和共享的人工智能培训方法,以减轻异质性并提高系统可靠性.
- 人工智能有望改善IBD管理和研究中的MH客观评估.
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