Objective: Robotics-assisted retinal microsurgery provides several benefits including improvement of manipulation precision. The assistance provided to the surgeons by current robotic frameworks is, however, a “passive” support, e.g., by damping hand tremor. Intelligent assistance and active guidance are, however, lacking in the existing robotic frameworks. In this paper, an active interventional control framework (AICF) has been presented to increase operation safety by actively intervening the operation to avoid exertion of excessive forces to the sclera. Methods: AICF consists of the following four components: first, the steady-hand eye robot as the robotic module; second, a sensorized tool to measure tool-to-sclera forces; third, a recurrent neural network to predict occurrence of undesired events based on a short history of time series of sensor measurements; and finally, a variable admittance controller to command the robot away from the undesired instances. Results: A set of user studies were conducted involving 14 participants (with four surgeons). The users were asked to perform a vessel-following task on an eyeball phantom with the assistance of AICF as well as other two benchmark approaches, i.e., auditory feedback (AF) and real-time force feedback (RF). Statistical analysis shows that AICF results in a significant reduction of proportion of undesired instances to about 2.5%, compared with 38.4% and 26.2% using AF and RF, respectively. Conclusion: AICF can effectively predict excessive-force instances and augment performance of the user to avoid undesired events during robot-assisted microsurgical tasks. Significance: The proposed system may be extended to other fields of microsurgery and may potentially reduce tissue injury.

中文翻译：

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迈向安全的视网膜显微手术：基于 RNN 的主动干预控制框架的开发和评估

目标：机器人辅助的视网膜显微手术提供了几个好处，包括提高操作精度。然而，当前机器人框架向外科医生提供的帮助是“被动”支持，例如通过抑制手部颤抖。然而，现有的机器人框架缺乏智能辅助和主动引导。在本文中，提出了一种主动介入控制框架（AICF），通过主动干预手术以避免对巩膜施加过大的力来提高手术安全性。方法：AICF由以下四个部分组成：第一，稳手眼机器人作为机器人模块；第二，一种用于测量工具到巩膜力的传感工具；第三，一个循环神经网络，根据传感器测量的时间序列的短历史来预测不希望事件的发生；最后，一个可变导纳控制器命令机器人远离不需要的实例。结果：进行了一组用户研究，涉及 14 名参与者（有 4 名外科医生）。要求用户在 AICF 以及其他两种基准方法（即听觉反馈 (AF) 和实时力反馈 (RF)）的帮助下在眼球模型上执行血管跟踪任务。统计分析表明，与使用 AF 和 RF 分别为 38.4% 和 26.2% 相比，AICF 导致不受欢迎的实例比例显着降低至约 2.5%。结论：AICF 可以有效地预测过度用力的情况并增强用户的性能，以避免在机器人辅助显微外科任务期间发生意外事件。意义：提议的系统可以扩展到显微外科的其他领域，并可能减少组织损伤。