使用机器学习评估结肠镜技能来衡量质量:概念验证和初步验证
Matthew Wittbrodt1, Matthew Klug1, Mozziyar Etemadi1,2
1Information Services, Northwestern Medicine, Chicago, United States.
Endoscopy international open
|July 5, 2024
概括
开发了一个AI工具来评估结肠镜检查的质量. 这种自动化系统准确地测量关键指标,有助于识别和培训需要改进的提供者,以提高结肠镜质量.
科学领域:
- 胃肠病学 胃肠病学
- 医疗成像医学成像
- 机器学习 机器学习
背景情况:
- 低质量的结肠镜检查与癌症风险增加有关.
- 测量结肠镜检查的质量是一个重大挑战.
- 需要自动化评估工具来改善质量控制.
研究的目的:
- 开发和验证使用机器学习对结肠镜质量 (AI-CQ) 的自动互动评估.
- 评估AI-CQ在测量关键质量指标中的准确性.
- 评估AI-CQ在提供者培训和质量改善方面的潜力.
主要方法:
- 在结肠镜图像上使用机器学习开发了一个AI-CQ模型.
- 包括诸如插入时间,撤回时间和多体检测率等指标.
- 纳入的新型指标:HQ-WT和WT-PT.
- 预先训练了一个视觉变压器,并为多标签分类进行了微调.
- 在第二家医院的50次结肠镜检查中对该模型进行了外部验证.
主要成果:
- AI-CQ实现了88%的准确性,用于腔输管识别.
- 在AI-CQ和手动测量插入和提取时间之间有很高的相关性.
- AI-CQ的息肉检测率与手动评估相比 (47.6%与45.5%相比).
- 正确识别的逆向曲 (95.2%) 和右结肠评估 (100%).
结论:
- 交互式人工智能工具可以自动评估结肠镜质量.
- 这种AI-CQ系统对识别需要补救的提供商有很大的希望.
- 该工具可以促进有针对性的培训,以提高结肠镜技术和患者的治疗结果.
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