PKC-RCM: Preoperative Kinematic Calibration for Enhancing RCM Accuracy in Automatic Vitreoretinal Robotic Surgery

Many robotic systems have emerged in the recent past as cutting-edge solutions to enhance the capabilities of ophthalmic surgeons in order not only to increase the quality of conventional operations but also to enable new and advanced interventions such as gene- and stem-cell-based therapies. Some of these operations require precise and stable delivery of therapeutics into the sub-retinal domain and therefore, automatic procedures with micron precision at the tooltip are essential. One of the most critical parameters to precisely maintain the tooltip in automated robotic retinal surgery is the appropriate configuration and control of the Remote Center of Motion (RCM). The RCM precision might be affected by any physical uncertainties, such as instrument assembling, or minor kinematic changes. Therefore, an accurate RCM identification requires an extensive calibration plan before each operation. This paper presents a novel preoperative evaluation-calibration method for kinematic-adjustable software-based RCM robots. Our key concept is decomposing the 3D workspace into two orthogonal working planes in order to reduce the complexity while adding robustness to RCM evaluation and software calibration. First, we propose an ablation-based RCM-related analysis method of the kinematics of the robot. Using a Convolutional Neural Network (CNN), we analyze image-based RCM along the instrument during a predefined RCM motion maneuver. Utilizing software calibration protocol by prior-analyzed RCM-related kinematic parameters, the software calibration is done automatically. The process is repeated until the RCM accuracy is set within a clinically acceptable range. Evaluation of the method on a highly accurate 5-DOF-Software-RCM robot demonstrated significant optimization in RCM error within an average of 4 minutes for each plane and 0.300 ± 0.20 mm accuracy.