Smart Medical Devices

End-effectors at mm scale

The small scale of minimally-invasive surgical procedures presents significant challenges to developing robust, smart, and dexterous tools and end-effectors for manipulating millimeter and sub-millimeter anatomical structures (e.g. vessels or nerves) and surgical equipment (e.g. sutures or staples). To meet the demand, we are developing a versatile fabrication process, based on printed circuit board manufacturing techniques, to create monolithic, kinematically complex, three-dimensional machines in parallel at the millimeter to centimeter scales. During lamination, precisely aligned material layers are combined in different ways to create functional layers that serve a specific purpose, including structural layers, flexure layers that enable rotary joints and articulated structures, printed circuit board (PCB) layers, metal spring layers, or low-friction sliding bearing layers. Finally, various functional layers combine to create multi-structure, multi-material, quasi-2D laminates capable of folding into complex 3D structures.

Assured safety drill

We have developed an assured safety drilling mechanism that is compatible with a large range of bit diameters and provides safe, reliable access to the inside of the skull. This is accomplished through a dynamic bi-stable linkage that supports drilling when force is applied against the skull but retracts upon penetration when the reaction force is diminished. In the initial design retraction was achieved when centrifugal forces from rotating masses overpower the axial forces, thus changing the state of the bi-stable mechanism. The current design iteration features a torsional spring loaded mechanism that overpowers axial forces upon penetration, thus triggering the change in the bi-stable mechanism. Testing on ex-vivo animal structures has demonstrated that the device can withdraw the drill bit in sufficient time to eliminate the risk of soft tissue damage. Ease of use and portability of the device will enable its use in unregulated environments such as hospital emergency rooms and emergency disaster relief areas.

Associated Papers

A Light-Reflecting Balloon Catheter for Atraumatic Tissue Defect Repair
E. T. Roche, et al., “A Light-Reflecting Balloon Catheter for Atraumatic Tissue Defect Repair,” Science Translational Medicine, vol. 7, no. 306, pp. 306ra149, 2015. Publisher's VersionAbstract

Closing small defects in the body typically requires stitching of tissues during surgery. Toward a minimally invasive approach, Roche et al. engineered a balloon catheter with a reflective surface coating that could be used to adhere biodegradable patches to tissues. The device unfolds the patch and its adhesive, delivers ultraviolet (UV) light, and then applies pressure to stabilize the adhesive as the light cures the polymer. The authors demonstrated catheter-mediated application of the photocurable polymer patch in vivo in rat tissue, with minimal inflammation and complete animal survival, as well as in a challenging septal defect in the beating hearts of pigs. The device was also used to seal porcine stomach ulcers and abdominal hernias ex vivo, suggesting versatility of this approach in repairing defects more easily and atraumatically than sutures.A congenital or iatrogenic tissue defect often requires closure by open surgery or metallic components that can erode tissue. Biodegradable, hydrophobic light-activated adhesives represent an attractive alternative to sutures, but lack a specifically designed minimally invasive delivery tool, which limits their clinical translation. We developed a multifunctional, catheter-based technology with no implantable rigid components that functions by unfolding an adhesive-loaded elastic patch and deploying a double-balloon design to stabilize and apply pressure to the patch against the tissue defect site. The device uses a fiber-optic system and reflective metallic coating to uniformly disperse ultraviolet light for adhesive activation. Using this device, we demonstrate closure on the distal side of a defect in porcine abdominal wall, stomach, and heart tissue ex vivo. The catheter was further evaluated as a potential tool for tissue closure in vivo in rat heart and abdomen and as a perventricular tool for closure of a challenging cardiac septal defect in a large animal (porcine) model. Patches attached to the heart and abdominal wall with the device showed similar inflammatory response as sutures, with 100% small animal survival, indicating safety. In the large animal model, a ventricular septal defect in a beating heart was reduced to <1.6 mm. This new therapeutic platform has utility in a range of clinical scenarios that warrant minimally invasive and atraumatic repair of hard-to-reach defects.

M. A. Horvath, et al., “An Intracardiac Soft Robotic Device for Augmentation of Blood Ejection from the Failing Right Ventricle,” Annals of Biomedical Engineering, pp. 1-12, 2017. Publisher's VersionAbstract

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.

J. B. Gafford, F. Doshi-Velez, R. J. Wood, and C. J. Walsh, “Machine learning approaches to environmental disturbance rejection in multi-axis optoelectronic force sensors,” Sensors and Actuators A: Physical, vol. 248, pp. 78 - 87, 2016. Publisher's VersionAbstract

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.

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