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.

In ophthalmic surgery, surgeons or robots manipulate a light probe and an instrument around two separated trocars following sclerotomy to achieve orbital control for eyeball pose adjustment and subsequent surgical tasks referring to microscope frames. However, current methods face significant challenges in directly extracting the eyeball pose from real-time microscope frames due to the limited microscope perspective and the darkened operating room (OR). This paper decomposes eyeball rotations only along the x and y axes. Then, a method of calculating eyeball poses using eyeball geometry and microscopic trocar positions is presented. This method is tested by simulation and a phantom system with current [2.0, 2.8] degree error, providing assistant intraoperative eyeball status in the dark OR with extended method discussions.

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.

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.

In ophthalmic surgeries, the light probe is responsible for providing safe intraocular illumination and ensuring the visibility of the instrument and its shadow as the only available reference for qualitative depth estimation and landing point prediction in fundus microscopic images. To achieve sustainable shadow-based estimation during surgeries, we propose controlling the light probe automatically to limit the shadow position around the instrument tip using only 2D information from the microscope. We also integrate an intensity balancing sub-module to guarantee the normal intensity distribution and the safe depth of light-tip placement. Without motor-based pose coordination between the light probe and the instrument, experiments analyze the performance of our image-based shadow maintenance with only image information under the constraints of RCM and discuss the working volume and segmentation limitations during simulation and real-robot tests.