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一个计算机视觉模型用于自动结石细分和评估其性能与外科医生对比
Daiwei Lu1, Ekamjit S Deol2, Tatsuki Koyama3
1Department of Computer Science, Vanderbilt University, Nashville, TN, USA.
BJU international
|September 25, 2025
概括
一个新的计算机视觉模型在尿路透镜检查 (URS) 过程中准确地细分结石,改善可视化. 这种人工智能工具的性能与专家泌尿科医生相提并论,显示了实时手术协助的潜力.
科学领域:
- 医疗成像医学成像
- 在外科手术中使用人工智能
- 泌尿器科 泌尿器科 泌尿器科 泌尿器科
背景情况:
- 尿道透视 (URS) 是治疗结石的一种常见手术.
- 在URS过程中精确的石头可视化对于有效治疗和最大限度地减少残留碎片至关重要.
- 目前的可视化方法可能是有限的,需要先进的工具来改善手术结果.
研究的目的:
- 开发一种计算机视觉模型,用于在URS过程中对结石进行细分.
- 为了增强石头和治疗进展的手术内可视化.
- 为了比较模型的石块细分性能与专家泌尿科医生的性能.
主要方法:
- 收集并注释了136个尿路透镜视频用于内结石治疗.
- 以每秒3 (FPS) 的速度提取,并分为训练,验证和测试数据集.
- 使用子相似系数 (DSC),ROC曲线下的面积和二进制交叉损失评估模型性能,与专家外科医生的注释进行比较.
主要成果:
- 计算机视觉模型实现了0.97的优秀整体DSC,并且可以在30 FPS.
- 在光纤和数字尿路望远镜中,性能一致.
- 该模型在石头定位和激光切除方面的表现强,在残留碎片评估方面表现良好. 它超过了五位专家外科医生中的三位,并且与其他两位相匹配.
结论:
- 开发的计算机视觉模型在URS期间有效地分割石头,增强可视化.
- 该模型的细分性能与专家泌尿科医生的性能相当.
- 这证明了在URS程序中使用实时手术内辅助模型的可行性.
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