Autonomously Navigating a Surgical Tool Inside the Eye by Learning from Demonstration

A fundamental challenge in retinal surgery is safely navigating a surgical tool to a desired goal position on the retinal surface while avoiding damage to surrounding tissues, a procedure that typically requires tens-of-microns accuracy. In practice, the surgeon relies on depth-estimation skills to localize the tool-tip with respect to the retina in order to perform the tool-navigation task, which can be prone to human error. To alleviate such uncertainty, prior work has introduced ways to assist the surgeon by estimating the tool-tip distance to the retina and providing haptic or auditory feedback. However, automating the tool-navigation task itself remains unsolved and largely unexplored. Such a capability, if reliably automated, could serve as a building block to streamline complex procedures and reduce the chance for tissue damage. Towards this end, we propose to automate the tool-navigation task by learning to mimic expert demonstrations of the task. Specifically, a deep network is trained to imitate expert trajectories toward various locations on the retina based on recorded visual servoing to a given goal specified by the user. The proposed autonomous navigation system is evaluated in simulation and in physical experiments using a silicone eye phantom. We show that the network can reliably navigate a needle surgical tool to various desired locations within 137 micrometer accuracy in physical experiments and 94 micrometer in simulation on average, and generalizes well to unseen situations such as in the presence of auxiliary surgical tools, variable eye backgrounds, and brightness conditions

Loophole-free Bell test for continuous variables via wave and particle correlations

We derive two classes of multi-mode Bell inequalities under local realistic assumptions, which are violated only by the entangled states negative under partial transposition in accordance with the Peres conjecture. Remarkably, the failure of local realism can be manifested by exploiting wave and particle correlations of readily accessible continuous-variable states, with very large violation of inequalities insensitive to detector-efficiency, which makes a strong case for a loophole-free test.Comment: 4 pages, published versio

Reply to the comment on "Loophole-free Bell test for continuous variables via wave and particle correlations"

In a recent note, Cavalcanti and Scarani (CS) constructed a counter local-hidden-variable model to explain the violation of our inequalities in Phys. Rev. Lett. 105, 170404 (2010). Here, we briefly discuss some issues in response to the comments raised by CS.Comment: published versio

Application of Copula-Based Markov Model to Generate Monthly Precipitation

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Comparison of 4-French versus 5-French sheaths for diagnostic coronary angiography via the snuffbox approach

Background: Although a shorter hemostasis duration would be expected when compared with the conventional radial approach as the diameter of the distal radial artery is smaller than that of the conventional radial artery, the optimal duration of hemostasis in diagnostic coronary angiography (CAG) via the distal radial approach, termed the snuffbox approach, has not been well investigated.Methods: Data from 171 patients were retrospectively collected (55 and 116 patients in the 4-French [Fr] and 5-Fr sheath groups, respectively). The patients had suspected myocardial ischemia and were undergoing diagnostic CAG via the snuffbox approach at a single center between January 2019 and August 2019.Results: The mean age of the study population was 67.6 ± 11.0 years, and 69% were male. The left snuffbox approach was performed in 146 (85.4%) patients. The mean snuffbox puncture time, defined as the time interval between local anesthesia and sheath cannulation, was 145.1 ± 120.8 s. The hemostasis duration was significantly shorter in the 4-Fr sheath group than in the 5-Fr sheath group (70 [62–90] vs. 120 [120–130] min; p < 0.001). There were local hematomas, defined as ≤ 5 cm in diameter, at the puncture site in 8 (4.7%) patients. Moreover, there were no conventional and distal radial artery occlusions, assessed by manual pulse, after hemostasis in the study population during hospitalization.Conclusions: Successful hemostasis was obtained within 2 h for diagnostic CAG via the snuffbox approach using the 4-Fr or 5-Fr sheaths

Autonomous Needle Navigation in Retinal Microsurgery: Evaluation in ex vivo Porcine Eyes

Important challenges in retinal microsurgery include prolonged operating time, inadequate force feedback, and poor depth perception due to a constrained top-down view of the surgery. The introduction of robot-assisted technology could potentially deal with such challenges and improve the surgeon's performance. Motivated by such challenges, this work develops a strategy for autonomous needle navigation in retinal microsurgery aiming to achieve precise manipulation, reduced end-to-end surgery time, and enhanced safety. This is accomplished through real-time geometry estimation and chance-constrained Model Predictive Control (MPC) resulting in high positional accuracy while keeping scleral forces within a safe level. The robotic system is validated using both open-sky and intact (with lens and partial vitreous removal) ex vivo porcine eyes. The experimental results demonstrate that the generation of safe control trajectories is robust to small motions associated with head drift. The mean navigation time and scleral force for MPC navigation experiments are 7.208 s and 11.97 mN, which can be considered efficient and well within acceptable safe limits. The resulting mean errors along lateral directions of the retina are below 0.06 mm, which is below the typical hand tremor amplitude in retinal microsurgery

Deep Learning Guided Autonomous Surgery: Guiding Small Needles into Sub-Millimeter Scale Blood Vessels

We propose a general strategy for autonomous guidance and insertion of a needle into a retinal blood vessel. The main challenges underpinning this task are the accurate placement of the needle-tip on the target vein and a careful needle insertion maneuver to avoid double-puncturing the vein, while dealing with challenging kinematic constraints and depth-estimation uncertainty. Following how surgeons perform this task purely based on visual feedback, we develop a system which relies solely on \emph{monocular} visual cues by combining data-driven kinematic and contact estimation, visual-servoing, and model-based optimal control. By relying on both known kinematic models, as well as deep-learning based perception modules, the system can localize the surgical needle tip and detect needle-tissue interactions and venipuncture events. The outputs from these perception modules are then combined with a motion planning framework that uses visual-servoing and optimal control to cannulate the target vein, while respecting kinematic constraints that consider the safety of the procedure. We demonstrate that we can reliably and consistently perform needle insertion in the domain of retinal surgery, specifically in performing retinal vein cannulation. Using cadaveric pig eyes, we demonstrate that our system can navigate to target veins within 22$\mu m$ XY accuracy and perform the entire procedure in less than 35 seconds on average, and all 24 trials performed on 4 pig eyes were successful. Preliminary comparison study against a human operator show that our system is consistently more accurate and safer, especially during safety-critical needle-tissue interactions. To the best of the authors' knowledge, this work accomplishes a first demonstration of autonomous retinal vein cannulation at a clinically-relevant setting using animal tissues