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

State of the art of robotic surgery related to vision: Brain and eye applications of newly available devices

Raffaele Nuzzi, Luca Brusasco Department of Surgical Sciences, Eye Clinic, University of Torino, Turin, Italy Background: Robot-assisted surgery has revolutionized many surgical subspecialties, mainly where procedures have to be performed in confined, difficult to visualize spaces. Despite advances in general surgery and neurosurgery, in vivo application of robotics to ocular surgery is still in its infancy, owing to the particular complexities of microsurgery. The use of robotic assistance and feedback guidance on surgical maneuvers could improve the technical performance of expert surgeons during the initial phase of the learning curve. Evidence acquisition: We analyzed the advantages and disadvantages of surgical robots, as well as the present applications and future outlook of robotics in neurosurgery in brain areas related to vision and ophthalmology. Discussion: Limitations to robotic assistance remain, that need to be overcome before it can be more widely applied in ocular surgery. Conclusion: There is heightened interest in studies documenting computerized systems that filter out hand tremor and optimize speed of movement, control of force, and direction and range of movement. Further research is still needed to validate robot-assisted procedures. Keywords: robotic surgery related to vision, robots, ophthalmological applications of robotics, eye and brain robots, eye robot

A 5-DOFs Robot for Posterior Segment Eye Microsurgery

In retinal surgery clinicians access the internal volume of the eyeball through small scale trocar ports, typically 0.65 mm in diameter, to treat vitreoretinal disorders like idiopathic epiretinal membrane and age-related macular holes. The treatment of these conditions involves the removal of thin layers of diseased tissue, namely the epiretinal membrane and the internal limiting membrane. These membranes have an average thickness of only 60 μm and 2 μm respectively making extremely challenging even for expert clinicians to peel without damaging the surrounding tissue. In this work we present a novel Ophthalmic microsurgery Robot (OmSR) designed to operate a standard surgical forceps used in these procedures with micrometric precision, overcoming the limitations of current robotic systems associated with the offsetting of the remote centre of motion of the end effector when accessing the sclera. The design of the proposed system is presented, and its performance evaluated. The results show that the end effector can be controlled with an accuracy of less than 30 μm and the surgical forceps opening and closing positional error is less than 4.3 μm. Trajectory-following experiments and membrane peeling experiments are also presented, showing promising results in both scenarios

Force-Sensing-Based Multi-Platform Robotic Assistance for Vitreoretinal Surgery

Vitreoretinal surgery aims to treat disorders of the retina, vitreous body, and macula, such as retinal detachment, diabetic retinopathy, macular hole, epiretinal membrane and retinal vein occlusion. Challenged by several technical and human limitations, vitreoretinal practice currently ranks amongst the most demanding fields in ophthalmic surgery. Of vitreoretinal procedures, membrane peeling is the most common to be performed, over 0.5 million times annually, and among the most prone to complications. It requires an extremely delicate tissue manipulation by various micron scale maneuvers near the retina despite the physiological hand tremor of the operator. In addition, to avoid injuries, the applied forces on the retina need to be kept at a very fine level, which is often well below the tactile sensory threshold of the surgeon. Retinal vein cannulation is another demanding procedure where therapeutic agents are injected into occluded retinal veins. The feasibility of this treatment is limited due to challenges in identifying the moment of venous puncture, achieving cannulation and maintaining it throughout the drug delivery period. Recent advancements in medical robotics have significant potential to address most of the challenges in vitreoretinal practice, and therefore to prevent traumas, lessen complications, minimize intra-operative surgeon effort, maximize surgeon comfort, and promote patient safety. This dissertation presents the development of novel force-sensing tools that can easily be used on various robotic platforms, and robot control methods to produce integrated assistive surgical systems that work in partnership with surgeons against the current limitations in vitreoretinal surgery, specifically focusing on membrane peeling and vein cannulation procedures. Integrating high sensitivity force sensing into the ophthalmic instruments enables precise quantitative monitoring of applied forces. Auditory feedback based upon the measured forces can inform (and warn) the surgeon quickly during the surgery and help prevent injury due to excessive forces. Using these tools on a robotic platform can attenuate hand tremor of the surgeon, which effectively promotes tool manipulation accuracy. In addition, based upon certain force signatures, the robotic system can precisely identify critical instants, such as the venous puncture in retinal vein cannulation, and actively guide the tool towards clinical targets, compensate any involuntary motion of the surgeon, or generate additional motion that will make the surgical task easier. The experimental results using two distinct robotic platforms, the Steady-Hand Eye Robot and Micron, in combination with the force-sensing ophthalmic instruments, show significant performance improvement in artificial dry phantoms and ex vivo biological tissues

Vitreoretinal Surgical Robotic System with Autonomous Orbital Manipulation using Vector-Field Inequalities

Vitreoretinal surgery pertains to the treatment of delicate tissues on the fundus of the eye using thin instruments. Surgeons frequently rotate the eye during surgery, which is called orbital manipulation, to observe regions around the fundus without moving the patient. In this paper, we propose the autonomous orbital manipulation of the eye in robot-assisted vitreoretinal surgery with our tele-operated surgical system. In a simulation study, we preliminarily investigated the increase in the manipulability of our system using orbital manipulation. Furthermore, we demonstrated the feasibility of our method in experiments with a physical robot and a realistic eye model, showing an increase in the view-able area of the fundus when compared to a conventional technique. Source code and minimal example available at https://github.com/mmmarinho/icra2023_orbitalmanipulation.Comment: 7 pages, 7 figures, accepted on ICRA202

Augmentation Of Human Skill In Microsurgery

Surgeons performing highly skilled microsurgery tasks can benefit from information and manual assistance to overcome technological and physiological limitations to make surgery safer, efficient, and more successful. Vitreoretinal surgery is particularly difficult due to inherent micro-scale and fragility of human eye anatomy. Additionally, surgeons are challenged by physiological hand tremor, poor visualization, lack of force sensing, and significant cognitive load while executing high-risk procedures inside the eye, such as epiretinal membrane peeling. This dissertation presents the architecture and the design principles for a surgical augmentation environment which is used to develop innovative functionality to address the fundamental limitations in vitreoretinal surgery. It is an inherently information driven modular system incorporating robotics, sensors, and multimedia components. The integrated nature of the system is leveraged to create intuitive and relevant human-machine interfaces and generate a particular system behavior to provide active physical assistance and present relevant sensory information to the surgeon. These include basic manipulation assistance, audio-visual and haptic feedback, intraoperative imaging and force sensing. The resulting functionality, and the proposed architecture and design methods generalize to other microsurgical procedures. The system's performance is demonstrated and evaluated using phantoms and in vivo experiments

Doctor of Philosophy

dissertationIn this dissertation, we present methods for intuitive telemanipulation of manipulators that use piezoelectric stick-slip actuators (PSSAs). Commercial micro/nano-manipulators, which utilize PSSAs to achieve high precision over a large workspace, are typically controlled by a human operator at the joint level, leading to unintuitive and time-consuming telemanipulation. Prior work has considered the use of computer-vision-feedback to close a control loop for improved performance, but computer-vision-feedback is not a viable option for many end users. We discuss how open-loop models of the micro/nano-manipulator can be used to achieve desired end-effector movements, and we explain the process of obtaining open-loop models. We propose a rate-control telemanipulation method that utilizes the obtained model, and we experimentally quantify the effectiveness of the method using a common commercial manipulator (the Kleindiek MM3A). The utility of open-loop control methods for PSSAs with a human in the loop depends directly on the accuracy of the open-loop models of the manipulator. Prior research has shown that modeling of piezoelectric actuators is not a trivial task as they are known to suffer from nonlinearities that degrade their performance. We study the effect of static (non-inertial) loads on a prismatic and a rotary PSSA, and obtain a model relating the step size of the actuator to the load. The actuator-specific parameters of the model are calibrated by taking measurements in specific configurations of the manipulator. Results comparing the obtained model to experimental data are presented. PSSAs have properties that make them desirable over traditional DC-motor actuators for use in retinal surgery. We present a telemanipulation system for retinal surgery that uses a full range of existing disposable instruments. The system uses a PSSA-based manipulator that is compact and light enough that it could reasonably be made head-mounted to passively compensate for head movements. Two mechanisms are presented that enable the system to use existing disposable actuated instruments, and an instrument adapter enables quick-change of instruments during surgery. A custom stylus for a haptic interface enables intuitive and ergonomic telemanipulation of actuated instruments. Experimental results with a force-sensitive phantom eye show that telemanipulated surgery results in reduced forces on the retina compared to manual surgery, and training with the system results in improved performance. Finally, we evaluate operator efficiency with different haptic-interface kinematics for telemanipulated retinal surgery. Surgical procedures of the retina require precise manipulation of instruments inserted through trocars in the sclera. Telemanipulated robotic systems have been developed to improve retinal surgery, but there is not a unique mapping of the motions of the surgeon's hand to the lower-dimensional motions of the instrument through the trocar. We study operator performance during a precision positioning task on a force-sensing phantom retina, reminiscent of telemanipulated retinal surgery, with three common haptic-interface kinematics implemented in software on a PHANTOM Premium 6DOF haptic interface. Results from a study with 12 human subjects show that overall performance is best with the kinematics that represent a compact and inexpensive option, and that subjects' subjective preference agrees with the objective performance results

Context-aware learning for robot-assisted endovascular catheterization

Endovascular intervention has become a mainstream treatment of cardiovascular diseases. However, multiple challenges remain such as unwanted radiation exposures, limited two-dimensional image guidance, insufficient force perception and haptic cues. Fast evolving robot-assisted platforms improve the stability and accuracy of instrument manipulation. The master-slave system also removes radiation to the operator. However, the integration of robotic systems into the current surgical workflow is still debatable since repetitive, easy tasks have little value to be executed by the robotic teleoperation. Current systems offer very low autonomy, potential autonomous features could bring more benefits such as reduced cognitive workloads and human error, safer and more consistent instrument manipulation, ability to incorporate various medical imaging and sensing modalities. This research proposes frameworks for automated catheterisation with different machine learning-based algorithms, includes Learning-from-Demonstration, Reinforcement Learning, and Imitation Learning. Those frameworks focused on integrating context for tasks in the process of skill learning, hence achieving better adaptation to different situations and safer tool-tissue interactions. Furthermore, the autonomous feature was applied to next-generation, MR-safe robotic catheterisation platform. The results provide important insights into improving catheter navigation in the form of autonomous task planning, self-optimization with clinical relevant factors, and motivate the design of intelligent, intuitive, and collaborative robots under non-ionizing image modalities.Open Acces