Editorial: Translational research in medical robotics—challenges and opportunities

In the last few decades, emerging medical technologies and the growing number of commercial robotic platforms have supported diagnosis and treatment of both acute and chronic diseases of the human body, improving the clinical outcome, reducing trauma, shortening the patient recovery time, and increasing postoperative survival rates (Troccaz et al., 2019). Medical robots–including surgical robots, rehabilitation and assistive robots, and hospital automation robots–with improved safety, efficacy and reduced costs, robotic platforms will soon approach a tipping point, moving beyond early adopters to become part of the mainstream clinical practice, defining the future of smart hospitals and home-based patient care. Surgical robots promise to enhance minimally invasive surgery with precise instrument control, intuitive hand-eye coordination, and superior dexterity within tight spaces (Dupont et al., 2021). Rehabilitation robotics facilitates robot-assisted therapy and automated recovery training (Xue et al., 2021). Assistive robots aid individuals with physical limitations, either enhancing or compensating for functions, promoting independence, and lessening the burden on caregivers (Trainum et al., 2023). Additionally, robotic systems can automate hospital operations, spanning service robots aiding clinicians to robots in labs for high-throughput testing (Kwon et al., 2022). These technologies aim to revolutionize healthcare, offering improved patient care and operational efficiency

Using needle orientation sensing as surrogate signal for respiratory motion estimation in percutaneous interventions

Purpose To develop and evaluate an approach to estimate the respiratory-induced motion of lesions in the chest and abdomen. Materials and methods The proposed approach uses the motion of an initial reference needle inserted into a moving organ to estimate the lesion (target) displacement that is caused by respiration. The needles position is measured using an inertial measurement unit (IMU) sensor externally attached to the hub of an initially placed reference needle. Data obtained from the IMU sensor and the target motion are used to train a learning-based approach to estimate the position of the moving target. An experimental platform was designed to mimic respiratory motion of the liver. Liver motion profiles of human subjects provided inputs to the experimental platform. Variables including the insertion angle, target depth, target motion velocity and target proximity to the reference needle were evaluated by measuring the error of the estimated target position and processing time. Results: The mean error of estimation of the target position ranged between 0.86 and 1.29 mm. The processing maximum training and testing time was 5 ms which is suitable for real-time target motion estimation using the needle position sensor. Conclusion: The external motion of an initially placed reference needle inserted into a moving organ can be used as a surrogate, measurable and accessible signal to estimate in real-time the position of a moving target caused by respiration; this technique could then be used to guide the placement of subsequently inserted needles directly into the target

Control of untethered soft grippers for pick-and-place tasks

In order to handle complex tasks in hard-toreach environments, small-scale robots have to possess suitable dexterous and untethered control capabilities. The fabrication and manipulation of soft small- scale grippers complying to these requirements is now made possible by advances in material science and robotics. In this paper, we use soft small-scale grippers to demonstrate pick-and-place tasks. The precise remote control is obtained by altering both the magnetic field gradient and the temperature in the workspace. This allows us to regulate the position and grasping configuration of the soft thermally-responsive hydrogel-nanoparticle composite magnetic grippers. The magnetic closed-loop control achieves precise localization with an average region-of-convergence of the gripper of 0.12±0.05 mm. The micro-sized payload can be placed with a positioning error of 0.57±0.33 mm. The soft grippers move with an average velocity of 0.72±0.13 mm/s without a micro-sized payload, and at 1.09±0.07 mm/s with a micro-sized payloa

Robotically steering flexible needles

Needle insertion into soft tissue is one of the common minimally invasive surgical procedures. Many diagnostic and therapeutic clinical procedures require insertion of a needle to a specific location in soft-tissue, including biopsy or radioactive seed implantation for cancer treatment (brachytherapy). \ud In this thesis, we start with modeling the effect of skin thickness on target motion during insertion. A closed-loop control algorithm is then developed for flexible needle steering using camera and ultrasound images for feedback. An ultrasound-based 3D needle tracking algorithm is then combined with real-time path planning for needle steering. The needle is steered during insertion in gelatin-based and biological soft-tissue phantoms. A non-imaging approach (fiber Bragg grating (FBG) sensors) is also used for real-time needle shape reconstruction and tip tracking. FBG sensors are used as feedback to the control algorithm to steer the needle towards a target in 3D space. We then focus on physical target localization and 3D shape reconstruction for needle steering in phantoms with curved surfaces. A clinical application (needle insertion in the prostate) is also investigated where the needle is steered in a multi-layer phantom with different tissue elasticities. \ud In order to bring the proposed algorithms to clinical environments, we consider practical issues such as including the clinician in the control loop to merge robot accuracy with clinical expertise. The proposed system is adapted to enable clinicians to directly control the insertion procedure while receiving navigation cues from the control algorithm. Navigation cues are provided through a combination of haptic (vibratory) and visual feedback to the operator who controls the needle for steering. The proposed system is further adapted by using a clinically-approved Automated Breast Volume Scanner (ABVS) which is experimentally evaluated to be used for needle insertion procedures. The ultrasound-based ABVS system is used for pre-operative scanning of soft-tissue for target localization, shape reconstruction, and also intra-operatively for needle tip tracking during the steering process. The achieved targeting errors suggest that our approach is convenient for targeting lesions that can be detected using clinical ultrasound imaging systems. These promising results allow us to proceed further in bringing our system towards clinical practice