Data-Driven Modelling and Control for Robot Needle Insertion in Deep Anterior Lamellar Keratoplasty.
Data-Driven Modelling and Control for Robot Needle Insertion in Deep Anterior Lamellar Keratoplasty.
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DOI：
10.1109/lra.2022.3140458
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发表时间：
2022-04
影响因子：
5.2
通讯作者：
Hauser, Kris
中科院分区：
文献类型：
--
作者：
Edwards, William;Tang, Gao;Tian, Yuan;Draelos, Mark;Izatt, Joseph;Kuo, Anthony;Hauser, Kris
深前板层角膜移植术（DALK）是一种与降低患者发病率相关的角膜移植技术。DALK已经被探索为机器人显微手术的潜在应用，因为小尺度、精细控制要求和可视化困难使得人类外科医生执行起来非常具有挑战性。我们解决了手术工具和角膜组织之间的小规模相互作用的建模问题，以提高针插入的准确性，因为在目标深度的5%内的准确放置与更可靠的临床结果相关。我们开发了一个数据驱动的工具-组织相互作用的自回归动态模型和一个模型预测控制器来指导机器人针插入。在体外模型中，与现有方法相比，我们的控制器显著提高了针定位的准确性超过40%。
Deep anterior lamellar keratoplasty (DALK) is a technique for cornea transplantation which is associated with reduced patient morbidity. DALK has been explored as a potential application of robot microsurgery because the small scales, fine control requirements, and difficulty of visualization make it very challenging for human surgeons to perform. We address the problem of modelling the small scale interactions between the surgical tool and the cornea tissue to improve the accuracy of needle insertion, since accurate placement within 5% of target depth has been associated with more reliable clinical outcomes. We develop a data-driven autoregressive dynamic model of the tool-tissue interaction and a model predictive controller to guide robot needle insertion. In an ex vivo model, our controller significantly improves the accuracy of needle positioning by more than 40% compared to prior methods.
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DOI：
10.1109/tbme.2013.2283501
发表时间：
2014-02
期刊：
IEEE transactions on bio-medical engineering
影响因子：
--
作者：
He X;Handa J;Gehlbach P;Taylor R;Iordachita I
通讯作者：
Iordachita I
影响因子：
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DOI：
10.1109/34.121791
发表时间：
1992-02-01
影响因子：
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作者：
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通讯作者：
MCKAY, ND
影响因子：
13.7
作者：
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影响因子：
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作者：
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