人工智能量化了性结肠炎的内学严重程度在分级尺度上.
Kaoru Takabayashi1, Taku Kobayashi2, Katsuyoshi Matsuoka3
1Center for Diagnostic and Therapeutic Endoscopy, Keio University School of Medicine, Tokyo, Japan.
一个人工智能 (AI) 系统被开发来评估性结肠炎 (UC) 的内学严重程度. 人工智能证明了高准确度,与专家评估相比,用于诊断UC炎症.
科学领域:
- 胃肠病学 胃肠病学
- 医疗人工智能 医疗人工智能
- 内镜成像分析分析
背景情况:
- 目前对于性结肠炎 (UC) 的内镜评分可能无法完全捕捉临床严重程度的变化.
- 炎症性肠病 (IBD) 专家根据内镜检查结果提供细微的严重性评估.
- 需要客观和可重复的方法来评估UC内镜严重程度.
研究的目的:
- 开发一个人工智能 (AI) 系统来评估UC内镜严重程度.
- 创建一个AI,反映IBD专家内镜医生的诊断能力.
- 建立一个新的AI驱动的评分尺度,用于UC内镜评估.
主要方法:
- 一个排名-卷积神经网络 (排名-CNN) 被训练在13826对联内镜图像的UC.
- 人工智能系统使用UC内镜度量表 (UCEGS) 进行严重性评估.
- 在AI诊断的UCEGS,梅奥内镜分值和专家内镜师评估之间计算了相关性.
主要成果:
- 人工智能诊断的UCEGS显示了与梅奥内镜分数的强烈相关性 (斯皮尔曼的rho ≈ 0.89).
- 人工智能和四名IBD专家内镜师之间的相关系数超过0.95 (P < 0.01).
- 人工智能系统与专家评估有很高的一致性.
结论:
- 开发的AI系统准确地诊断出性结肠炎的内镜严重程度.
- 人工智能的表现与经验丰富的IBD专家内镜医生的表现相当.
- 这种人工智能工具为客观的UC内镜评估提供了一个有前途的方法.
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