Anthony R. Lanfranco

Bio: Anthony R. Lanfranco is an academic researcher from Drexel University. The author has contributed to research in topics: Robotic surgery. The author has an hindex of 2, co-authored 2 publications receiving 961 citations.

Robotic Surgery: A Current Perspective

Abstract: Robotic surgery is a new and exciting emerging technology that is taking the surgical profession by storm. Up to this point, however, the race to acquire and incorporate this emerging technology has primarily been driven by the market. In addition, surgical robots have become the entry fee for centers wanting to be known for excellence in minimally invasive surgery despite the current lack of practical applications. Therefore, robotic devices seem to have more of a marketing role than a practical role. Whether or not robotic devices will grow into a more practical role remains to be seen. Our goal in writing this review is to provide an objective evaluation of this technology and to touch on some of the subjects that manufacturers of robots do not readily disclose. In this article we discuss the development and evolution of robotic surgery, review current robotic systems, review the current data, discuss the current role of robotics in surgery, and finally we discuss the possible roles of robotic surgery in the future. It is our hope that by the end of this article the reader will be able to make a more informed decision about robotic surgery before “chasing the market.”

Surgical Management of Chronic Lower Abdominal and Groin Pain in High-performance Athletes

Abstract: Formerly, most of the causes and treatments of chronic lower abdominal and groin pain in high-performance athletes eluded sports medicine specialists. Now we are much better at identifying and managing the different syndromes. Most of the advances are based on empiric evidence, although many pitfalls remain with respect to diagnosis and management of the various syndromes. This article reviews our current understanding of several of these clinical entities. We focus on lessons learned from a large 15-year experience.

End-to-end training of deep visuomotor policies

Abstract: Policy search methods can allow robots to learn control policies for a wide range of tasks, but practical applications of policy search often require hand-engineered components for perception, state estimation, and low-level control. In this paper, we aim to answer the following question: does training the perception and control systems jointly end-to-end provide better performance than training each component separately? To this end, we develop a method that can be used to learn policies that map raw image observations directly to torques at the robot's motors. The policies are represented by deep convolutional neural networks (CNNs) with 92,000 parameters, and are trained using a guided policy search method, which transforms policy search into supervised learning, with supervision provided by a simple trajectory-centric reinforcement learning method. We evaluate our method on a range of real-world manipulation tasks that require close coordination between vision and control, such as screwing a cap onto a bottle, and present simulated comparisons to a range of prior policy search methods.

Micro/Nanorobots for Biomedicine: Delivery, Surgery, Sensing, and Detoxification.

Abstract: Micro- and nanoscale robots that can effectively convert diverse energy sources into movement and force represent a rapidly emerging and fascinating robotics research area. Recent advances in the design, fabrication, and operation of micro/nanorobots have greatly enhanced their power, function, and versatility. The new capabilities of these tiny untethered machines indicate immense potential for a variety of biomedical applications. This article reviews recent progress and future perspectives of micro/nanorobots in biomedicine, with a special focus on their potential advantages and applications for directed drug delivery, precision surgery, medical diagnosis, and detoxification. Future success of this technology, to be realized through close collaboration between robotics, medical, and nanotechnology experts, should have a major impact on disease diagnosis, treatment, and prevention.

End-to-End Training of Deep Visuomotor Policies

Abstract: Policy search methods can allow robots to learn control policies for a wide range of tasks, but practical applications of policy search often require hand-engineered components for perception, state estimation, and low-level control. In this paper, we aim to answer the following question: does training the perception and control systems jointly end-to-end provide better performance than training each component separately? To this end, we develop a method that can be used to learn policies that map raw image observations directly to torques at the robot's motors. The policies are represented by deep convolutional neural networks (CNNs) with 92,000 parameters, and are trained using a partially observed guided policy search method, which transforms policy search into supervised learning, with supervision provided by a simple trajectory-centric reinforcement learning method. We evaluate our method on a range of real-world manipulation tasks that require close coordination between vision and control, such as screwing a cap onto a bottle, and present simulated comparisons to a range of prior policy search methods.

Robotic technology in surgery: Past, present, and future

Abstract: It has been nearly 20 years since the first appearance of robotics in the operating room. In that time, much progress has been made in integrating robotic technologies with surgical instrumentation, as evidenced by the many thousands of successful robot-assisted cases. However, to build on past success and to fully leverage the potential of surgical robotics in the future, it is essential to maximize a shared understanding and communication among surgeons, engineers, entrepreneurs, and healthcare administrators. This article provides an introduction to medical robotic technologies, develops a possible taxonomy, reviews the evolution of a surgical robot, and discusses future prospects for innovation. Robotic surgery has demonstrated some clear benefits. It remains to be seen where these benefits will outweigh the associated costs over the long term. In the future, surgical robots should be smaller, less expensive, easier to operate, and should seamlessly integrate emerging technologies from a number of different fields. Such advances will enable continued progress in surgical instrumentation and, ultimately, surgical care.

The value of haptic feedback in conventional and robot-assisted minimal invasive surgery and virtual reality training: a current review

Abstract: Background Virtual reality (VR) as surgical training tool has become a state-of-the-art technique in training and teaching skills for minimally invasive surgery (MIS). Although intuitively appealing, the true benefits of haptic (VR training) platforms are unknown. Many questions about haptic feedback in the different areas of surgical skills (training) need to be answered before adding costly haptic feedback in VR simulation for MIS training. This study was designed to review the current status and value of haptic feedback in conventional and robot-assisted MIS and training by using virtual reality simulation.