一种用于精确内镜尺寸测量胃病变的新型数字方法:用视觉和forceps辅助估计进行比较研究
Soyoung Yun1, Bokyung Kim2, Gihong Park1
1Department of Internal Medicine and Liver Research Institute, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea.
Journal of gastric cancer
|July 9, 2025
概括
对内镜病变的视觉估计是不准确的. 一种新的深度估计算法 (DEA) 与视觉估计和子辅助估计相比,显著提高了病变大小测量的准确性,特别是对于初学者内镜师.
科学领域:
- 胃肠病学 胃肠病学
- 医疗成像医学成像
- 内镜技术的使用方法
背景情况:
- 准确的内镜损伤大小测量对于临床决策至关重要.
- 视觉估计是常用的,但容易出现重大错误.
- 传统的方法,如子辅助估计也表现出局限性.
研究的目的:
- 调查常规内镜损伤大小估计方法中的不准确性.
- 引入和评估用于损伤测量的新型深度估计算法 (DEA).
- 为了比较DEA与视觉估计 (VE) 和子辅助估计 (FE) 的性能.
主要方法:
- 19名内镜师 (专家和新手) 在3D打印的胃模型中测量了人造病变.
- 使用视觉估计 (VE),助估计 (FE) 和新型深度估计算法 (DEA) 进行了测量.
- 使用中位数百分比误差 (MdPE) 和四分位数间范围 (IQR) 评估准确性,并根据内镜师的经验和病变特征进行分组分析.
主要成果:
- 视觉估计 (VE) 具有最高的MdPE (33.33%),其次是子辅助估计 (FE) (30.00%).
- 新的深度估计算法 (DEA) 实现了显著更高的准确性,MdPE为7.78%.
- 在所有病变类型和内镜医生经验水平上,DEA的表现优于VE和FE,表现一致.
结论:
- 传统方法具有很高的错误率,特别是对于特定的损伤形状和位置.
- 深度估计算法 (DEA) 在损伤大小测量准确度方面提供了显著的改进.
- 将DEA整合到内镜实践中可以提高精度,改善临床决策,并优化患者的治疗结果.
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