基于深度学习的方法的开发和验证,以预测梅奥内镜治疗性结肠炎的内评分
Jing Qi1, Guangcong Ruan2, Yi Ping2
1Department of Digital Medicine, School of Biomedical Engineering and Imaging Medicine, Army Medical University, Chongqing, China.
Therapeutic advances in gastroenterology
|May 30, 2023
概括
一个新的深度学习模型,UC-former,准确地从内镜图像中预测性结肠炎 (UC) 的严重程度. 这种人工智能工具显示出高准确度,可能有助于临床实践评估UC患者.
科学领域:
- 胃肠病学 胃肠病学
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 梅奥内镜评分对于在临床环境中评估性结肠炎 (UC) 严重程度至关重要.
- 准确的内镜评估对于有效的UC管理至关重要.
研究的目的:
- 开发和验证一个深度学习模型,用于使用UC内镜图像自动预测梅奥内镜评分.
- 评估AI模型在多中心环境中的性能和通用性.
主要方法:
- 这是一项回顾性研究,涉及来自中国两家医院的768名UC患者的15120张结肠镜图像.
- 开发基于视觉转换器的深度学习模型,命名为UC-former.
- 与六位内镜医生和三家医院的多中心外部验证进行性能比较.
主要成果:
- 前UC在内部测试集上获得了梅奥分数03的曲线下高面积 (0.9730.998).
- 该模型的准确性 (90.8%) 超过了最好的高级内镜医生.
- 在三个中心进行的外部验证结果分别为82.4%,85.0%和83.6%.
结论:
- 开发的UC-former在评估UC严重程度方面表现出高准确性,忠实性和稳定性.
- 这种人工智能驱动的方法显示了将UC评估纳入临床实践的巨大潜力.
- 自动内镜评分可以提高UC患者管理的效率和一致性.
相关概念视频
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