Smart Medical

We introduce an implantable intracardiac soft robotic right ventricular ejection device (RVED) for dynamic approximation of the right ventricular (RV) free wall and the interventricular septum (IVS) in synchrony with the cardiac cycle to augment blood ejection in right heart failure (RHF). The RVED is designed for safe and effective intracardiac operation and consists of an anchoring system deployed across the IVS, an RV free wall anchor, and a pneumatic artificial muscle linear actuator that spans the RV chamber between the two anchors. Using a ventricular simulator and a custom controller, we characterized ventricular volume ejection, linear approximation against different loads and the effect of varying device actuation periods on volume ejection. The RVED was then tested in vivo in adult pigs (n = 5). First, we successfully deployed the device into the beating heart under 3D echocardiography guidance (n = 4). Next, we performed a feasibility study to evaluate the device's ability to augment RV ejection in an experimental model of RHF (n = 1). RVED actuation augmented RV ejection during RHF; while further chronic animal studies will provide details about the efficacy of this support device. These results demonstrate successful design and implementation of the RVED and its deployment into the beating heart. This soft robotic ejection device has potential to serve as a rapidly deployable system for mechanical circulatory assistance in RHF.

Recent advances in medical robotics have initiated a transition from rigid serial manipulators to flexible or continuum robots capable of navigating to confined anatomy within the body. A desire for further procedure minimization is a key accelerator for the development of these flexible systems where the end goal is to provide access to the previously inaccessible anatomical workspaces and enable new minimally invasive surgical (MIS) procedures. While sophisticated navigation and control capabilities have been demonstrated for such systems, existing manufacturing approaches have limited the capabilities of millimeter-scale end-effectors for these flexible systems to date and, to achieve next generation highly functional end-effectors for surgical robots, advanced manufacturing approaches are required. We address this challenge by utilizing a disruptive 2D layer-by-layer precision fabrication process (inspired by printed circuit board manufacturing) that can create functional 3D mechanisms by folding 2D layers of materials which may be structural, flexible, adhesive, or conductive. Such an approach enables actuation, sensing, and circuitry to be directly integrated with the articulating features by selecting the appropriate materials during the layer-by-layer manufacturing process. To demonstrate the efficacy of this technology, we use it to fabricate three modular robotic components at the millimeter-scale: (1) sensors, (2) mechanisms, and (3) actuators. These modules could potentially be implemented into transendoscopic systems, enabling bilateral grasping, retraction and cutting, and could potentially mitigate challenging MIS interventions performed via endoscopy or flexible means. This research lays the ground work for new mechanism, sensor and actuation technologies that can be readily integrated via new millimeter-scale layer-by-layer manufacturing approaches.

Light-Intensity Modulated (LIM) force sensors are seeing increasing interest in the field of surgical robotics and flexible systems in particular. However, such sensing modalities are notoriously susceptible to ambient effects such as temperature and environmental irradiance which can register as false force readings. We explore machine learning techniques to dynamically compensate for environmental biases that plague multi-axis optoelectronic force sensors. In this work, we fabricate a multisensor: three-axis LIM force sensor with integrated temperature and ambient irradiance sensing manufactured via a monolithic, origami-inspired fabrication process called printed-circuit MEMS. We explore machine learning regression techniques to compensate for temperature and ambient light sensitivity using on-board environmental sensor data. We compare batch-based ridge regression, kernelized regression and support vector techniques to baseline ordinary least-squares estimates to show that on-board environmental monitoring can substantially improve sensor force tracking performance and output stability under variable lighting and large (>100C) thermal gradients. By augmenting the least-squares estimate with nonlinear functions describing both environmental disturbances and cross-axis coupling effects, we can reduce the error in Fx, Fy and Fz by 10%, 33%, and 73%, respectively. We assess viability of each algorithm tested in terms of both prediction accuracy and computational overhead, and analyze kernel-based regression for prediction in the context of online force feedback and haptics applications in surgical robotics. Finally, we suggest future work for fast approximation and prediction using stochastic, sparse kernel techniques.

The innovation in surgical robotics has seen a shift toward flexible systems that can access remote locations inside the body. However, a general reliance on the conventional fabrication techniques ultimately limits the complexity and the sophistication of the distal implementations of such systems, and poses a barrier to further innovation and widespread adoption. In this paper, we present a novel, self-assembling force sensor manufactured using a composite lamination fabrication process, wherein linkages pre-machined in the laminate provide the required degrees-of-freedom and fold patterns to facilitate self-assembly. Using the purely 2-D fabrication techniques, the energy contained within a planar elastic biasing element directly integrated into the laminate is released post-fabrication, allowing the sensor to self-assemble into its final 3-D shape. The sensors are batch-fabricated, further driving down the production costs. The transduction mechanism relies on the principle of light intensity modulation, which allows the sensor to detect axial forces with millinewton-level resolution. The geometry of the sensor was selected based on the size constraints inherent in minimally invasive surgery, as well as with a specific focus on optimizing the sensor's linearity. The sensor is unique from the fiber-based force sensors in that the emitter and the detector are encapsulated within the sensor itself. The bare sensor operates over a force range of 0-200 mN, with a sensitivity of 5 V/N and a resolution of 0.8 mN. The experimental results show that the sensor's stiffness can be tuned using a thicker material for the spring layer and/or encapsulation/integration with soft materials. The empirical validation shows that the sensor has the sensitivity and the resolution necessary to discern the biologically relevant forces in a simulated cannulation task.