机器人输食管回声心力学:系统设计和基于深度学习的动力学建模
Seyed MohammadReza Sajadi1, Abbas Tariverdi2, Henrik Brun3,4
1Department of Informatics, University of Oslo, Oslo, Norway.
Frontiers in robotics and AI
|February 12, 2026
概括
一个新的跨食道心声学 (TEE) 机器人系统使用深度学习进行精确的控制,从而实现半自主程序. 这种先进的机器人TEE系统提高了诊断准确度,并支持结构性心脏干预.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 医疗成像医学成像
- 机器学习 机器学习
背景情况:
- 手动输食管回声心电图 (TEE) 需要专业技能,并且在各种患者位置上可能具有挑战性.
- 现有的机器人TEE系统在机械设计和动力模型准确性方面存在局限性.
- 在TEE探头中的电缆驱动机制往往遭受非线性,如摩擦和张力不对称.
研究的目的:
- 开发一个机器人转食管回声心电图 (TEE) 系统,具有增强的机械设计和基于深度学习的动态建模.
- 复制手动TEE程序的基本自由度 (DOF),以提高控制和多功能性.
- 为半自主 TEE 系统奠定基础,能够支持诊断和干预程序.
主要方法:
- 远程操作的UR5操纵器与TEE探头系统的集成,在手柄上有6个DOF,在胃镜管上有2个DOF.
- 基于数据驱动的动力学模型的开发,使用与LSTM单元的循环神经网络,在三种曲配置 (0°,45°,90°) 的42,000个姿势-命令对上进行训练.
- 通过实验追踪探头位置和方向,评估准确性和实时推断能力来验证系统的性能.
主要成果:
- 深度学习动力模型在所有配置中实现了高位置跟踪精度,根平均平方误差 (RMSE) 在1.267mm以下.
- 平均定向误差低于8.503°,在关键的90°曲配置有4.947°的误差.
- 该系统展示了实时性能,推断时间为1.8ms和坐标独立性,证实了真正的动态学习.
结论:
- 开发的机器人TEE系统,通过深度学习动态建模来增强,成功地以高精度复制了手动TEE DOF.
- 该系统能够处理各种患者位置及其实时性能对于临床部署至关重要.
- 这种整合是朝着半自主TEE系统的重要一步,用于诊断检查和图像引导的结构性心脏干预.
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