Luzio de Melo, Paulo, da Silva, Miguel Tavares, Martins, Jorge, and Newman, Dava
Artificial Organs. May 2015, Vol. 39 Issue 5, E56, 11 p.
Semiconductor device, Circuit components, and Rapid prototyping
Byline: Paulo Luzio de Melo,Miguel Tavares da Silva, Jorge Martins, Dava Newman Keywords: Functional electrical stimulation; Neuroprosthesis; Arduino microcontroller platform; Accelerometer; Inertial measurement unit; Gait; Force sensitive resistors; Closed-loop control; Drop foot; Rapid prototyping Abstract Functional electrical stimulation (FES) has been used over the last decades as a method to rehabilitate lost motor functions of individuals with spinal cord injury, multiple sclerosis, and post-stroke hemiparesis. Within this field, researchers in need of developing FES-based control solutions for specific disabilities often have to choose between either the acquisition and integration of high-performance industry-level systems, which are rather expensive and hardly portable, or develop custom-made portable solutions, which despite their lower cost, usually require expert-level electronic skills. Here, a flexible low-cost microcontroller-based platform for rapid prototyping of FES neuroprostheses is presented, designed for reduced execution complexity, development time, and production cost. For this reason, the Arduino open-source microcontroller platform was used, together with off-the-shelf components whenever possible. The developed system enables the rapid deployment of portable FES-based gait neuroprostheses, being flexible enough to allow simple open-loop strategies but also more complex closed-loop solutions. The system is based on a modular architecture that allows the development of optimized solutions depending on the desired FES applications, even though the design and testing of the platform were focused toward drop foot correction. The flexibility of the system was demonstrated using two algorithms targeting drop foot condition within different experimental setups. Successful bench testing of the device in healthy subjects demonstrated these neuroprosthesis platform capabilities to correct drop foot.
Comrie, Michaela L., Monteith, Gabrielle, Zur Linden, Alex, Oblak, Michelle, Phillips, John, and James, Fiona M. K.
PLoS ONE. March 25, 2019, Vol. 14 Issue 3, e0214123
Algorithm, Algorithms -- Analysis, Medical imaging equipment -- Usage, Diagnostic imaging -- Usage, and CT imaging -- Usage
Author(s): Michaela L. Comrie 1,2, Gabrielle Monteith 3, Alex Zur Linden 3, Michelle Oblak 3, John Phillips 4, Fiona M. K. James 3,*, on behalf of the Ontario Veterinary College [...] This study's objective was to determine the accuracy of using current computed tomography (CT) scan and software techniques for rapid prototyping by quantifying the margin of error between CT models and laser scans of canine skull specimens. Twenty canine skulls of varying morphology were selected from an anatomy collection at a veterinary school. CT scans (bone and standard algorithms) were performed for each skull, and data segmented (testing two lower threshold settings of 226HU and -650HU) into 3-D CT models. Laser scans were then performed on each skull. The CT models were compared to the corresponding laser scan to determine the error generated from the different types of CT model parameters. This error was then compared between the different types of CT models to determine the most accurate parameters. The mean errors for the 226HU CT models, both bone and standard algorithms, were not significant from zero error (p = 0.1076 and p = 0.0580, respectively). The mean errors for both -650HU CT models were significant from zero error (p 0.001). Significant differences were detected between CT models for 3 CT model comparisons: Bone (p 0.0001); Standard (p 0.0001); and -650HU (p 0.0001). For 226HU CT models, a significant difference was not detected between CT models (p = 0.2268). Independent of the parameters tested, the 3-D models derived from CT imaging accurately represent the real skull dimensions, with CT models differing less than 0.42 mm from the real skull dimensions. The 226HU threshold was more accurate than the -650HU threshold. For the 226HU CT models, accuracy was not dependent on the CT algorithm. For the -650 CT models, bone was more accurate than standard algorithms. Knowing the inherent error of this procedure is important for use in 3-D printing for surgical planning and medical education.
Quadri, Shakeba, Kapoor, Bhumika, Singh, Gaurav, and Tewari, Rajendra
Journal of Oral Research and Review. July-Dec, 2017, Vol. 9 Issue 2, 96
Dentistry -- Research and Rapid prototyping -- Methods
Emergence of advanced digital technology has opened up new perspectives for design and production in the field of dentistry. Rapid prototyping (RP) is a technique to quickly and automatically construct [...]
Computational linguistics -- Usage, Natural language interfaces -- Usage, Language processing -- Usage, and Hypothesis -- Usage
Author(s): Connor Sweetnam[sup.1], Simone Mocellin[sup.2], Michael Krauthammer[sup.3,4], Nathaniel Knopf[sup.1,5], Robert Baertsch[sup.1] and Jeff Shrager[sup.1,6] Background Shrager and Tenenbaum  described a closed loop system where case-based treatment and outcome information [...] Background We describe a prototype implementation of a platform that could underlie a Precision Oncology Rapid Learning system. Results We describe the prototype platform, and examine some important issues and details. In the Appendix we provide a complete walk-through of the prototype platform. Conclusions The design choices made in this implementation rest upon ten constitutive hypotheses, which, taken together, define a particular view of how a rapid learning medical platform might be defined, organized, and implemented. Keywords: Natural language processing, Precision oncology, Controlled natural language, Nanopublication, Treatment reasoning, Rapid learning, Tumor boards, Targeted therapies
Plaisance, Ariane, Witteman, Holly O., LeBlanc, Annie, Kryworuchko, Jennifer, Heyland, Daren Keith, Ebell, Mark H., Blair, Louisa, Tapp, Diane, Dupuis, Audrey, Lavoie-Berard, Carole-Anne, McGinn, Carrie Anna, Legare, France, and Archambault, Patrick Michel
PLoS ONE. Feb 15, 2018, Vol. 13 Issue 2, e0191844
Technology application, CPR (First aid) -- Study and teaching, Practice guidelines (Medicine) -- Evaluation, Medical personnel -- Training, and Medical personnel -- Technology application
Author(s): Ariane Plaisance 1,2,3, Holly O. Witteman 4,5,6, Annie LeBlanc 3,6, Jennifer Kryworuchko 7, Daren Keith Heyland 8,9, Mark H. Ebell 10, Louisa Blair 3, Diane Tapp 11,12, Audrey Dupuis [...] Background Upon admission to an intensive care unit (ICU), all patients should discuss their goals of care and express their wishes concerning life-sustaining interventions (e.g., cardiopulmonary resuscitation (CPR)). Without such discussions, interventions that prolong life at the cost of decreasing its quality may be used without appropriate guidance from patients. Objectives To adapt an existing decision aid about CPR to create a wiki-based decision aid individually adapted to each patient's risk factors; and to document the use of a wiki platform for this purpose. Methods We conducted three weeks of ethnographic observation in our ICU to observe intensivists and patients discussing goals of care and to identify their needs regarding decision making. We interviewed intensivists individually. Then we conducted three rounds of rapid prototyping involving 15 patients and 11 health professionals. We recorded and analyzed all discussions, interviews and comments, and collected sociodemographic data. Using a wiki, a website that allows multiple users to contribute or edit content, we adapted the decision aid accordingly and added the Good Outcome Following Attempted Resuscitation (GO-FAR) prediction rule calculator. Results We added discussion of invasive mechanical ventilation. The final decision aid comprises values clarification, risks and benefits of CPR and invasive mechanical ventilation, statistics about CPR, and a synthesis section. We added the GO-FAR prediction calculator as an online adjunct to the decision aid. Although three rounds of rapid prototyping simplified the information in the decision aid, 60% (n = 3/5) of the patients involved in the last cycle still did not understand its purpose. Conclusions Wikis and user-centered design can be used to adapt decision aids to users' needs and local contexts. Our wiki platform allows other centers to adapt our tools, reducing duplication and accelerating scale-up. Physicians need training in shared decision making skills about goals of care and in using the decision aid. A video version of the decision aid could clarify its purpose.