基于人工智能的结直肠粘膜暴露区域评估:多中心前性观察性研究
Jialing Li1, Li Huang1, Chaijie Luo1
1Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.
Endoscopy
|September 3, 2025
概括
一个新的深度学习系统,即累积结直肠粘膜暴露区域 (CCMEA),提高了结直肠镜的质量. 在CCMEA合格的组中,腺瘤检测率显著提高,从而改善了患者的护理.
科学领域:
- 胃肠病学
- 医疗技术
- 医学的人工智能
背景情况:
- 结肠镜检查质量对于检测结肠直肠瘤至关重要.
- 当前的质量指标可能无法完全捕捉在手术期间的粘膜暴露.
- 使用深度学习开发了一种新型指标,即累积结直肠粘膜暴露面积 (CCMEA).
研究的目的:
- 引入和验证结肠直肠粘膜暴露区域 (CCMEA) 作为结肠镜的新型质量控制指标.
- 评估基于深度学习的CCMEA系统在临床环境中的性能.
- 根据腺瘤检测率来确定CCMEA是否可以区分结肠镜.
主要方法:
- 使用ResNet50和UNet++开发了一个深度学习系统来计算CCMEA.
- 根据25%的腺瘤检测率 (ADR) 质量基准,确定了2000年的CCMEA门.
- 一项多中心前性观察研究评估了510名患者,比较了CCMEA合格和不合格组之间的ADR和其他检测率.
主要成果:
- 在CCMEA系统中,有270名患者被归类为合格患者,240名患者被归类为不合格患者 (CCMEA门:2000).
- 合格组显示显著更高的ADR (53. 70%对比14. 17%,aOR=7. 97),特别是小腺瘤 (42. 22%对比10. 00%).
- 在合格组中观察到较高的聚检测率 (89. 63% vs 40. 00%),腺瘤数量 (1. 00 vs 0. 17) 和聚数量 (5. 79 vs 1.32).
结论:
- 累计结肠直肠粘膜暴露区域 (CCMEA) 系统,其值为2000,有效地识别了更高质量的结肠镜.
- 通过CCMEA认证的结肠镜检测可以显著提高腺瘤检测率.
- 作为结肠镜检查质量控制的有价值的客观指标,CCMEA 是有前途的.
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