机器人系统在非马科夫高难度手术中的因果动态决策
Guo Na1, Tan Minghui1, Li Tiantian2
1School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China.
Frontiers in neurology
|March 9, 2026
概括
这项研究引入了一种使用矢量自回归 (VAR) 的新型因果建模框架,以改善手术决策. 该模型有效地处理复杂的手术内异常,提高机器人手术的安全性和适应性.
科学领域:
- 机器人和人工智能 机器人和人工智能
- 医疗信息学 医疗信息学
- 手术过程建模手术过程建模
背景情况:
- 传统的基于马尔科夫假设的模型无法捕捉手术内异常的时间变化的影响.
- 这种局限性阻碍了它们在神经外科和脊柱干预等复杂程序中的应用.
研究的目的:
- 开发一种因果建模框架,克服外科手术过程建模中的非马科夫限制.
- 在手术机器人中实现智能响应和自适应性决策.
主要方法:
- 开发了一个因果建模框架,利用矢量自回归 (VAR) 和格兰杰因果关系分析.
- 构建了一个因果链:原始手势 → 异常事件 → 恢复行动.
- 在一万个样本的合成数据集上验证了框架.
主要成果:
- 在因果推断中获得了95.60%的准确性.
- 在1万个样本中,F1得分为95.77%的稳定性被证明.
- 回忆 (95.88%) 超过了精度 (95.34%),优先考虑安全.
结论:
- 该框架有效地捕捉了异常事件的非马科夫时间相关性.
- 它克服了传统手术决策模型的局限性.
- 非程序特定的设计为各种手术机器人应用中自主决策提供了多功能途径.
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