计算机辅助分类的方形柱状连接点.
Hannah R Phillips1, Jeffrey R Fetzer2, Sanket Bhattarai2
1Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA.
Gastrointestinal endoscopy
|January 20, 2025
概括
这项研究开发了一种自动化算法来分类Z线形状,改进了不规则的状柱状结 (SCJ) 和潜在的巴雷特食道的诊断. 该模型在识别不规则的SCJ时达到78%的准确性.
科学领域:
- 胃肠病学和内镜检查
- 医学成像分析 医学成像分析
- 计算病理学计算病理学
背景情况:
- 这条Z线,或状柱状结 (SCJ),对于诊断诸如巴雷特食道等疾病至关重要.
- 目前对Z线不规则的视觉评估不准确,导致不一致的表征.
- 指南建议不要为不规则的Z线做活检,因为测量不准确.
研究的目的:
- 开发和验证一种标准化的方法来表征Z线形状.
- 假设Z线形状复杂性可以作为不规则性的替代分类器.
- 介绍一个计算机生成的算法,用于自动Z线细分和形状量化.
主要方法:
- 849张Z线图像的数据集使用nnUNet框架手动进行了细分.
- 用波形分解模型来确定一个不规则值.
- 胃肠道学家对Z线图像的规律性进行了评分,使用Fleiss κ统计数据评估了观察者间的变异性.
主要成果:
- 10名内镜师对Z线规律的观察者间一致是公平的 (Fleiss κ = .39).
- 食道专家之间的一致性是中等的 (Fleiss κ = .42),而非专家的一致性是公平的 (Fleiss κ = .31).
- 为了分类不规则的SCJ,最优的波形能量系数值是1.53 × 107,达到78%的准确性.
结论:
- 一个计算机生成的模型成功地自动化了Z线细分和分类.
- 为了SCJ分类,使用波束能量系数建立了一个标准化值.
- 这种方法提供了一个更客观的方法来表征Z线形态.
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