An organosynthetic soft robotic respiratory simulator

Abstract

In this work, we describe a benchtop model that recreates the motion and function of the diaphragm using a combination of advanced robotic and organic tissue. First, we build a high-fidelity anthropomorphic model of the diaphragm using thermoplastic and elastomeric material based on clinical imaging data. We then attach pneumatic artificial muscles to this elastomeric diaphragm, pre-programmed to move in a clinically relevant manner when pressurized. By inserting this diaphragm as the divider between two chambers in a benchtop model—one representing the thorax and the other the abdomen—and subsequently activating the diaphragm, we can recreate the pressure changes that cause lungs to inflate and deflate during regular breathing. Insertion of organic lungs in the thoracic cavity demonstrates this inflation and deflation in response to the pressures generated by our robotic diaphragm. By tailoring the input pressures and timing, we can represent different breathing motions and disease states. We instrument the model with multiple sensors to measure pressures, volumes, and flows and display these data in real-time, allowing the user to vary inputs such as the breathing rate and compliance of various components, and so they can observe and measure the downstream effect of changing these parameters. In this way, the model elucidates fundamental physiological concepts and can demonstrate pathology and the interplay of components of the respiratory system. This model will serve as an innovative and effective pedagogical tool for educating students on respiratory physiology and pathology in a user-controlled, interactive manner. It will also serve as an anatomically and physiologically accurate testbed for devices or pleural sealants that reside in the thoracic cavity, representing a vast improvement over existing models and ultimately reducing the requirement for testing these technologies in animal models. Finally, it will act as an impactful visualization tool for educating and engaging the broader community.

Object‐Oriented Lumped‐Parameter Modeling of the Cardiovascular System for Physiological and Pathophysiological Conditions

“…Diaphragmatic motion generates physiological pressures in both the thoracic and abdominal cavities as demonstrated in our previous work. [ 89 ] For this study, we employed our benchtop simulator to investigate IVC hemodynamics at a respiratory rate and cardiac output that are similar to those of the in vivo study as well as of the proposed simulation. A constant pressure head reservoir was used to provide a constant input to the system, while respiratory pressures and IVC hemodynamics were recorded throughout each breathing cycle using pressure sensors (ArgoTrans model2, Argon Medical Devices) and a flowmeter (Transonics Inc. 28PAU).…”

An implantable ventilator augments inspiration in an in vivo porcine model

“…Our group has built a custom electropneumatic control system utilizing electropneumatic pressure regulators and valves (SMC Pneumatics, SMC Corp, Tokyo, Japan) controlled by a custom software described in Horvath, Hu et al 29 . The software is designed to allow custom pressure waveforms to be input.…”

More than surgical tools: a systematic review of robots as didactic tools for the education of professionals in health sciences

“…Apart from surgical robotics, many other classifications have been proposed in the literature for health related robotics (Boubaker, 2020 ). One of the most widespread of such classifications is the one found in Cianchetti et al, ( 2018 ) who categorized health sciences related robotics as: medical robotics (Mapara & Patravale, 2017 ) including surgery (Collins & Wisz, 2020 ; Kadakia et al, 2020 ), diagnosis (Kaan & Ho, 2020 ; Tavakoli et al, 2020 ) and drug delivery devices (Mapara & Patravale, 2017 ; Nguyen et al, 2020 ); assistive robotics (Giansanti, 2021 ), such as wearable robots (Bai et al, 2018 ) and rehabilitation devices (Alias et al, 2017 ; Mohebbi, 2020 ), and human body mimicking robots including phantom devices (Hughes et al, 2020 ; Takeoka et al, 2017 ) and body-part simulators (Cz et al, 2012 ; Horvath et al, 2020 ).…”