Several robotic frameworks have been recently developed to assist ophthalmic surgeons in performing complex vitreoretinal procedures such as subretinal injection of advanced therapeutics. These surgical robots show promising capabilities; however, most of them have to limit their working volume to achieve maximum accuracy. Moreover, the visible area seen through the surgical microscope is limited and solely depends on the eye posture. If the eye posture, trocar position, and robot configuration are not correctly arranged, the instrument may not reach the target position, and the preparation will have to be redone. Therefore, this paper proposes the optimization framework of the eye tilting and the robot positioning to reach various target areas for different patients. Our method was validated with an adjustable phantom eye model, and the error of this workflow was 0.13 ± 1.65 deg (rotational joint around Y axis), -1.40 ± 1.13 deg (around X axis), and 1.80 ± 1.51 mm (depth, Z). The potential error sources are also analyzed in the discussion section.

Several robotic systems have emerged in the recent past to enhance the precision of micro-surgeries such as retinal procedures. Significant advancements have recently been achieved to increase the precision of such systems beyond surgeon capabilities. However, little attention has been paid to the impact of non-predicted and sudden movements of the patient and the environment. Therefore, analyzing environmental motion and vibrations is crucial to ensuring the optimal performance and reliability of medical systems that require micron-level precision, especially in real-life scenarios.To address this challenge, this paper introduces a novel environmental motion analysis system that employs a grid layout with distributed sensing nodes throughout the environment. This system effectively tracks undesired movements (motions) at designated locations and predicts upcoming motions using neural network-based approaches. The outcomes of our experiments exhibit promising prospects for real-time motion monitoring and prediction, which has the potential to form a solid basis for enhancing the automation, safety, integration, and overall efficiency of robot-assisted micro-surgeries.

Retinal surgery is a complex medical procedure that requires high precision dexterity to perform delicate instrument maneuvers with sub-millimeter accuracy. Minimizing the manual tremor and achieving precise and repeatable execution of surgical tasks has motivated the development of robotic platforms to overcome the limitations of manual surgery. However, specific tasks, such as instrument insertion through the trocar, are more challenging in robotic surgery than in conventional manual procedures since the robot control is often optimized for navigation inside the eye. This challenges the integration of robotic systems, creating a high cognitive load on the operator and prolonging the surgery time. Moreover, misalignment of the robot’s remote center of motion (RCM) and trocar position during the procedure can lead to excessive forces between the instrument and the trocar, potentially causing patient trauma. Precise and rapid localization of the trocars enables the automation of the insertion procedure and dynamic compensation of eye motion.In this work, we present a real-time marker-less method for 3D pose tracking of trocar, achieved with only a single monocular camera. Our experiments show promising results towards real-time trocar pose estimation and tracking, achieving an average error of 3◦in trocar orientation estimation, with an average processing time of 15 fps. This could serve as a foundation to improve robotic systems’ automation, integration, and efficiency of robotic systems for retinal surgery. The dataset created for this work is made publicly available.

Several robotic frameworks have been recently developed to assist ophthalmic surgeons in performing complex vitreoretinal procedures such as subretinal injection. However, in order to intuitively integrate robots into the surgical workflow, it is crucial to emphasize that an accessibility analysis framework for vitreoretinal surgery must be considered as an essential component. Such a framework, ideally, considers the comprehensive factors of the eye anatomy and its positioning, the insertion point, and the initial pose and position of the robot. By combining the mobilization of the eyeball and adjusting the pose and position of the robot, the accessibility of such systems is significantly optimized. At the same time, the accessible-visible area is better and faster matched to the working volume of the robot. This paper presents an analysis of an expansion strategy for the robot’s accessibility and visibility area. The outcomes of this method demonstrate the promising potential to enhance the robot’s accessibility, as evidenced in our analytical and experimental findings from 22.4% to 99.0% of the required working area on an adjustable phantom model.