Background Mortality rates are unnecessarily high in developing countries due to lack of medical training or available procedures. For example, maternal deaths were estimated at 287,000 in 2010 of which 99% happened in developing countries [1]. Many of these deaths could be avoided if the physicians and nurses had a way to train and practice life-saving medical procedures like Cesarean sections or tracheotomies without risking harm to patients. A way to do this would be to have surgical simulators that they could train on. While the medical profession in the developed world has shifted to simulation-based training [2], current procedural simulators range from $500 to $300,000 [3], which is not an option for most hospitals or care providers in developing countries. We propose an open-source ultralow-cost (less than $10 USD) medical procedure simulator platform that would be made of materials available in developing countries so that they could be locally made yet provide a means to accurately train and assess skill acquisition in medical procedures. Two enabling technologies may satisfy these requirements: low-cost bioplastics to simulate tissue and two-dimensional surface potentiometers to electronically track surgical tools on tissue. The goal of this research is to assess the feasibility of such simulators by evaluating the feasibility of these two technologies for low-cost medical simulators in the developing world.
Background Surgical robots are becoming more common in the operating room. Although surgeons utilize these robots for improved dexterity, scalable movements, and enhanced vision, they lose their sense of tactile and haptic feedback [1]. Okamura demonstrated a negative consequence of this by showing that forces exerted during robotic sutures significantly exceed that of hand sutures [2]. This excess in force can lead to a variety of complications including tissue crushing, which has been shown to be a clinically relevant problem [3]. Sie et al. proposed tissue-aware grasping as a solution for tissue crush injury, which may obviate the need for tactile and haptic feedback altogether [4]. By coupling online tissue identification with tissue-specific thresholds for crush injury, the surgical robot can warn of imminent tissue crushing or potentially prevent it. Sie et al. provided relevant work in this area by validating an approach for online tissue identification within the first 0.3 s of a grasp. This work was done with a modified manual laparoscopic Babcock grasper; this is a specialized instrument not commonly used in surgery. We herein aim to extend the results of Sie et al. to a much more common surgical tool: the da Vinci EndoWrist surgical instrument (Intuitive Surgical, Sunnyvale, CA). We demonstrate that tissue identification is possible using existing robotic tools without additional sensors or modifications to the tool tip, by using only motor torque and position data at the proximal end of the tool.
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