Andreas Hein

Bio: Andreas Hein is an academic researcher from University of Oldenburg. The author has contributed to research in topics: Health care & Robot. The author has an hindex of 20, co-authored 238 publications receiving 1891 citations. Previous affiliations of Andreas Hein include Humboldt University of Berlin & Humboldt State University.

Application of stereolithography for scaffold fabrication for tissue engineered heart valves.

Abstract: A crucial factor in tissue engineering of heart valves is the functional and physiologic scaffold design. In our current experiment, we describe a new fabrication technique for heart valve scaffolds, derived from x-ray computed tomography data linked to the rapid prototyping technique of stereolithography. To recreate the complex anatomic structure of a human pulmonary and aortic homograft, we have used stereolithographic models derived from x-ray computed tomography and specific software (CP, Aachen, Germany). These stereolithographic models were used to generate biocompatible and biodegradable heart valve scaffolds by a thermal processing technique. The scaffold forming polymer was a thermoplastic elastomer, a poly-4-hydroxybutyrate (P4HB) and a polyhydroxyoctanoate (PHOH) (Tepha, Inc., Cambridge, MA). We fabricated one human aortic root scaffold and one pulmonary heart valve scaffold. Analysis of the heart valve included functional testing in a pulsatile bioreactor under subphysiological and supraphysiological flow and pressure conditions. Using stereolithography, we were able to fabricate plastic models with accurate anatomy of a human valvular homograft. Moreover, we fabricated heart valve scaffolds with a physiologic valve design, which included the sinus of Valsalva, and that resembled our reconstructed aortic root and pulmonary valve. One advantage of P4HB and PHOH was the ability to mold a complete trileaflet heart valve scaffold from a stereolithographic model without the need for suturing. The heart valves were tested in a pulsatile bioreactor, and it was noted that the leaflets opened and closed synchronously under subphysiological and supraphysiological flow conditions. Our preliminary results suggest that the reproduction of complex anatomic structures by rapid prototyping techniques may be useful to fabricate custom made polymeric scaffolds for the tissue engineering of heart valves.

Method and device system for removing material or for working material

Abstract: The invention relates to a method and device system, which are provided for removing material or tissue or for working material or tissue and which can be used in the fields of medicine and dentistry as well as for the most varied types of material working in different areas of application and model working. The invention is particularly advantageous in that an exact removal of material or a highly precise, reproducible material working can be realized within the shortest amount of time by acquiring, storing and computer processing data pertaining to position and/or orientation of the effector and their changes relative to the position of at least one reference body. In addition, commands for controlling and/or regulating are initiated in such a manner that, according to a predetermined working volume and/or material removal volume and/or material remaining volume, the effector is switched into an on/off function or, in the on function, is controlled and/or regulated with regard to its power and/or parameterization. The inventive device system is characterized in that a first marking support (6) with markings (7) is arranged on a handpiece (1) with the effector (2), in that the handpiece (1) is connected to a control unit (22), and in that a second marking support (6) with markings (7) is attached to the material object or tissue object (5).

Robot-assisted insertion of craniofacial implants—clinical experience

Abstract: A medically approved robot system was clinically used to insert craniofacial implants into the skull for anchoring a silicone ear prosthesis. Additionally, a new process was developed for the preoperative manufacturing of the prosthesis using the computed tomography image data and a rapid prototyping technique. The navigated robot showed the surgeon intraoperatively the planned implant positions and guided the insertion procedure. The robot worked not automatically but interactively with the surgeon. In 13 patients, 30 implants were inserted with no intraoperative injuries. An absolute implant position accuracy of about ±1 mm and a relative accuracy between the implants of about ±0.2 mm were reached. This accuracy made it possible to apply the preoperatively manufactured ear prosthesis directly after surgery. The rehabilitation time for the patient was shortened. These clinical results were reached only by careful optimisation of each step of the intervention, the image acquisition, patient fixation and the intraoperative execution. The experience is good cause to use the robot system and the new manufacturing concept for anaplastology in other areas of the head as well.

A surgical robot system for maxillofacial surgery

Abstract: In this paper, the first active surgical robot system (OTTO) in a clinical environment for maxillofacial surgery is presented. The medical application is described from a technical point of view and the requirements for a robot in this speciality are defined. The paper describes the system architecture of the robotics environment and the need for research and development. The robot's hardware is based on a delta-kinematics robot system. At the current state of development, the robot can be used for inserting nonflexible catheters and for implanting bone fixtures in the skull.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

The knowledge-creating company : how Japanese companies create the dynamics of innovation

Abstract: How has Japan become a major economic power, a world leader in the automotive and electronics industries? What is the secret of their success? The consensus has been that, though the Japanese are not particularly innovative, they are exceptionally skilful at imitation, at improving products that already exist. But now two leading Japanese business experts, Ikujiro Nonaka and Hiro Takeuchi, turn this conventional wisdom on its head: Japanese firms are successful, they contend, precisely because they are innovative, because they create new knowledge and use it to produce successful products and technologies. Examining case studies drawn from such firms as Honda, Canon, Matsushita, NEC, 3M, GE, and the U.S. Marines, this book reveals how Japanese companies translate tacit to explicit knowledge and use it to produce new processes, products, and services.

Porous scaffold design for tissue engineering

Abstract: A paradigm shift is taking place in medicine from using synthetic implants and tissue grafts to a tissue engineering approach that uses degradable porous material scaffolds integrated with biological cells or molecules to regenerate tissues. This new paradigm requires scaffolds that balance temporary mechanical function with mass transport to aid biological delivery and tissue regeneration. Little is known quantitatively about this balance as early scaffolds were not fabricated with precise porous architecture. Recent advances in both computational topology design (CTD) and solid free-form fabrication (SFF) have made it possible to create scaffolds with controlled architecture. This paper reviews the integration of CTD with SFF to build designer tissue-engineering scaffolds. It also details the mechanical properties and tissue regeneration achieved using designer scaffolds. Finally, future directions are suggested for using designer scaffolds with in vivo experimentation to optimize tissue-engineering treatments, and coupling designer scaffolds with cell printing to create designer material/biofactor hybrids.