SUPPORTING ENGINEERING DESIGN OF ADDITIVELY MANUFACTURED MEDICAL DEVICES WITH KNOWLEDGE MANAGEMENT THROUGH ONTOLOGIES

Medical environments pose a substantial challenge for engineering designers. They combine significant knowledge demands with large investment for new product development and severe consequences in the case of design failure. Engineering designers must contend with an often-chaotic environment to which they have limited access and familiarity, a user base that is difficult to engage and highly diverse in many attributes, and a market structure that often pits stakeholders against one another. As medical care in general moves towards personalized models and surgical tools towards less invasive options emerging manufacturing technologies in additive manufacturing offer significant potential for the design of highly innovative medical devices. At the same time however these same technologies also introduce yet more challenges to the design process. This dissertation presents a knowledge-based approach to addressing the existing and emerging challenges of medical device design. The approach aims to address these challenges using knowledge captured in a suite of modular ontologies modeling knowledge domains that must be considered in medical device design. These include ontologies for understanding clinical context, human factors, regulation, enterprise, and manufacturability. Together these ontologies support design ideation, knowledge capture, and design verification. These ontologies are subsequently used to formulate a comprehensive knowledge framework for medical device design, and to enable an innovative design process. Case studies analyzing the design of surgical tools in several medical specialties are used to assess the capabilities of this approach

Book of Abstracts 15th International Symposium on Computer Methods in Biomechanics and Biomedical Engineering and 3rd Conference on Imaging and Visualization

In this edition, the two events will run together as a single conference, highlighting the strong connection with the Taylor & Francis journals: Computer Methods in Biomechanics and Biomedical Engineering (John Middleton and Christopher Jacobs, Eds.) and Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization (JoãoManuel R.S. Tavares, Ed.). The conference has become a major international meeting on computational biomechanics, imaging andvisualization. In this edition, the main program includes 212 presentations. In addition, sixteen renowned researchers will give plenary keynotes, addressing current challenges in computational biomechanics and biomedical imaging. In Lisbon, for the first time, a session dedicated to award the winner of the Best Paper in CMBBE Journal will take place. We believe that CMBBE2018 will have a strong impact on the development of computational biomechanics and biomedical imaging and visualization, identifying emerging areas of research and promoting the collaboration and networking between participants. This impact is evidenced through the well-known research groups, commercial companies and scientific organizations, who continue to support and sponsor the CMBBE meeting series. In fact, the conference is enriched with five workshops on specific scientific topics and commercial software.info:eu-repo/semantics/draf

Additive Manufacturing Of Functional Constructs Under Process Uncertainty

In recent years, development of novel material-sets has enabled additive manufacturing (AM) to transform from being used purely for model-making applications, to production of functional constructs. What was once only a rapid prototyping technology is now being used to print functional components, including batteries, actuators, transistors, and for tissue engineering applications, living tissue (Chapter 1). Associated with these new printing inks, however, is a drastic increase in the complexity of AM materials, and consequently, in the process uncertainty related to deposition of these materials. New applications enabled by functional printing capabilities, in particular in situ AM, also have associated process uncertainties, including situational and environmental uncertainties. That is, uncertainty in the shape of the substrate and in environmental parameters, such as temperature and humidity. As additive manufacturing makes the transition from a prototyping technology to more of a functional-object fabrication platform, these new associated process uncertainties must be addressed to yield sufficient geometric fidelity. Existing control schemes largely relied upon open-loop control and did not handle uncertainty through control algorithms, but instead avoided them by limiting their material-sets, printing on trivially shaped substrates, and restricting environmental conditions. A few techniques used geometric feedback to handle materials uncertainty, but these techniques did so on a process-parameter-level, and did not monitor/manipulate on the whole-part level. As a result, these techniques could not detect high-level errors such as whole-part deformation. The technique proposed herein, Greedy Geometric Feedback (GGF), closes the loop on the whole-part level and therefore can detect/correct types of errors that were previously un-addressable. Simulations and physical experiments were employed to validate and study the GGF algorithm. Not only was GGF effective at handling materials uncertainties, but it also has potential for situational and environmental uncertainties. Additional work focused on situational uncertainty and alternative control schemes were developed that effectively handled this type of uncertainty with less computational and data collection overhead. A novel differencebased planning approach was employed to explore in situ AM repair of osteochondral defects, and repair/adaptation of a four-legged robot. These proof-of-concept prints are the only known examples of generalized in situ AM, to date, in which the AM system was not provided a priori hard-coded substrate geometric information. The contributions of the work presented herein fall into three categories: 1) development of functional printing materials, 2) development of novel methodologies for quantitatively optimizing the printing qualities of functional printing inks, and 3) development of novel generalized control schemes for handling AM process uncertainty

Development of a design feature database to support design for additive manufacturing (DfAM)

This research introduces a method to aid the design of products or parts to be made using Additive Manufacturing (AM), particularly the laser sintering (LS) system. The research began with a literature review that encompassed the subjects of design and AM and through this the need for an assistive design approach for AM was identified. Undertaking the literature review also confirmed that little has been done in the area of supporting the design of AM parts or products. Preliminary investigations were conducted to identify the design factors to consider for AM. Two preliminary investigations were conducted, the first investigation was conducted to identify the reasons for designing for AM, the need for a design support tool for AM and current challenges of student industrial designers designing parts or products for AM, and also to identify the type of design support they required. Further investigation were conducted to examine how AM products are developed by professional industrial designers and to understand their design processes and procedures. The study has identified specific AM enabled design features that the designers have been able to create within their case study products. Detailed observation of the case study products and parts reveals a number of features that are only economical or possible to produce with AM. A taxonomy of AM enabled design features was developed as a precursor for the development of a computer based design tool. The AM enabled design features was defined as a features that would be uneconomical or very expensive to be produced with conventional methods. The taxonomy has four top-level taxons based on four main reasons for using AM, namely user fit requirements, improved product functionality requirements, parts consolidation requirements and improvement of aesthetics or form requirements. Each of these requirements was expanded further into thirteen sub categories of applications that contained 106 examples of design features that are only possible to manufacture using AM technology. The collected and grouped design features were presented in a form of a database as a method to aid product design of parts or products for AM. A series of user trials were conducted that showed the database enabled industrial designers to visualise and gather design feature information that could be incorporated into their own design work. Finally, conclusions are drawn and suggestions for future work are listed. In summary, it can be concluded that this research project has been a success, having addressed all of the objectives that were identified at its outset. From the user trial results, it is clear to see that the proposed tool would be an effective tool to support product design for AM, particularly from an educational perspective. The tool was found to be beneficial to student designers to take advantage of the design freedom offered by AM in order to produce improved product design. As AM becomes more widely used, it is anticipated that new design features will emerge that could be included in future versions of the database so that it will remain a rich source of inspirational information for tomorrow s industrial designers

Personalized medicine in surgical treatment combining tracking systems, augmented reality and 3D printing

Mención Internacional en el título de doctorIn the last twenty years, a new way of practicing medicine has been focusing on the problems and needs of each patient as an individual thanks to the significant advances in healthcare technology, the so-called personalized medicine. In surgical treatments, personalization has been possible thanks to key technologies adapted to the specific anatomy of each patient and the needs of the physicians. Tracking systems, augmented reality (AR), three-dimensional (3D) printing and artificial intelligence (AI) have previously supported this individualized medicine in many ways. However, their independent contributions show several limitations in terms of patient-to-image registration, lack of flexibility to adapt to the requirements of each case, large preoperative planning times, and navigation complexity. The main objective of this thesis is to increase patient personalization in surgical treatments by combining these technologies to bring surgical navigation to new complex cases by developing new patient registration methods, designing patient-specific tools, facilitating access to augmented reality by the medical community, and automating surgical workflows. In the first part of this dissertation, we present a novel framework for acral tumor resection combining intraoperative open-source navigation software, based on an optical tracking system, and desktop 3D printing. We used additive manufacturing to create a patient-specific mold that maintained the same position of the distal extremity during image-guided surgery as in the preoperative images. The feasibility of the proposed workflow was evaluated in two clinical cases (soft-tissue sarcomas in hand and foot). We achieved an overall accuracy of the system of 1.88 mm evaluated on the patient-specific 3D printed phantoms. Surgical navigation was feasible during both surgeries, allowing surgeons to verify the tumor resection margin. Then, we propose and augmented reality navigation system that uses 3D printed surgical guides with a tracking pattern enabling automatic patient-to-image registration in orthopedic oncology. This specific tool fits on the patient only in a pre-designed location, in this case bone tissue. This solution has been developed as a software application running on Microsoft HoloLens. The workflow was validated on a 3D printed phantom replicating the anatomy of a patient presenting an extraosseous Ewing’s sarcoma, and then tested during the actual surgical intervention. The results showed that the surgical guide with the reference marker can be placed precisely with an accuracy of 2 mm and a visualization error lower than 3 mm. The application allowed physicians to visualize the skin, bone, tumor and medical images overlaid on the phantom and patient. To enable the use of AR and 3D printing by inexperienced users without broad technical knowledge, we designed a step-by-step methodology. The proposed protocol describes how to develop an AR smartphone application that allows superimposing any patient-based 3D model onto a real-world environment using a 3D printed marker tracked by the smartphone camera. Our solution brings AR solutions closer to the final clinical user, combining free and open-source software with an open-access protocol. The proposed guide is already helping to accelerate the adoption of these technologies by medical professionals and researchers. In the next section of the thesis, we wanted to show the benefits of combining these technologies during different stages of the surgical workflow in orthopedic oncology. We designed a novel AR-based smartphone application that can display the patient’s anatomy and the tumor’s location. A 3D printed reference marker, designed to fit in a unique position of the affected bone tissue, enables automatic registration. The system has been evaluated in terms of visualization accuracy and usability during the whole surgical workflow on six realistic phantoms achieving a visualization error below 3 mm. The AR system was tested in two clinical cases during surgical planning, patient communication, and surgical intervention. These results and the positive feedback obtained from surgeons and patients suggest that the combination of AR and 3D printing can improve efficacy, accuracy, and patients’ experience In the final section, two surgical navigation systems have been developed and evaluated to guide electrode placement in sacral neurostimulation procedures based on optical tracking and augmented reality. Our results show that both systems could minimize patient discomfort and improve surgical outcomes by reducing needle insertion time and number of punctures. Additionally, we proposed a feasible clinical workflow for guiding SNS interventions with both navigation methodologies, including automatically creating sacral virtual 3D models for trajectory definition using artificial intelligence and intraoperative patient-to-image registration. To conclude, in this thesis we have demonstrated that the combination of technologies such as tracking systems, augmented reality, 3D printing, and artificial intelligence overcomes many current limitations in surgical treatments. Our results encourage the medical community to combine these technologies to improve surgical workflows and outcomes in more clinical scenarios.Programa de Doctorado en Ciencia y Tecnología Biomédica por la Universidad Carlos III de MadridPresidenta: María Jesús Ledesma Carbayo.- Secretaria: María Arrate Muñoz Barrutia.- Vocal: Csaba Pinte