Surgical Gym: A high-performance GPU-based platform for reinforcement learning with surgical robots

Recent advances in robot-assisted surgery have resulted in progressively more precise, efficient, and minimally invasive procedures, sparking a new era of robotic surgical intervention. This enables doctors, in collaborative interaction with robots, to perform traditional or minimally invasive surgeries with improved outcomes through smaller incisions. Recent efforts are working toward making robotic surgery more autonomous which has the potential to reduce variability of surgical outcomes and reduce complication rates. Deep reinforcement learning methodologies offer scalable solutions for surgical automation, but their effectiveness relies on extensive data acquisition due to the absence of prior knowledge in successfully accomplishing tasks. Due to the intensive nature of simulated data collection, previous works have focused on making existing algorithms more efficient. In this work, we focus on making the simulator more efficient, making training data much more accessible than previously possible. We introduce Surgical Gym, an open-source high performance platform for surgical robot learning where both the physics simulation and reinforcement learning occur directly on the GPU. We demonstrate between 100-5000x faster training times compared with previous surgical learning platforms. The code is available at: https://github.com/SamuelSchmidgall/SurgicalGym

Healthcare Robotics

Robots have the potential to be a game changer in healthcare: improving health and well-being, filling care gaps, supporting care givers, and aiding health care workers. However, before robots are able to be widely deployed, it is crucial that both the research and industrial communities work together to establish a strong evidence-base for healthcare robotics, and surmount likely adoption barriers. This article presents a broad contextualization of robots in healthcare by identifying key stakeholders, care settings, and tasks; reviewing recent advances in healthcare robotics; and outlining major challenges and opportunities to their adoption.Comment: 8 pages, Communications of the ACM, 201

Better Medicine

https://scholarlyworks.lvhn.org/better-medicine/1007/thumbnail.jp

The Role Of Industry Structure On Customer Value In Robotic Surgery

Spending on robot surgery is expected to increase by $17 billion in the next 6 years. This new surgical treatment has challenged hospitals with higher costs and varying performance. Healthcare executives struggle balancing the adoption of medical innovations with managing healthcare costs. This dilemma can be further complicated by industry structures relative to capital-intensive medical innovations. This research explores the interaction between industry structure and customer value. Specifically, how can hospitals apply an understanding of supplier industry structure and customer value to improve the value of a robotic surgery program (RSP)? This industry study represents an exhaustive longitudinal review of over 15 years of public data relative to robotic surgery, across three distinct time periods. Within the research, industry structure is evaluated using Porter’s 5-forces model. A framework based upon contributions from Grönroos as well as Menon, Homburg, and Beutin is introduced to assess customer value based upon clinical, financial and strategic (CFS) value. The implications of periodic industry structure on customer value were examined to identify opportunities for hospital executives to increase RSP customer value. There were several empirical and theoretical findings from this research. First, in the face of increasing industry structure the identification of favorable forces may create opportunities to increase RSP value. Secondarily, exploring customer value through the lens of core, add-on, relational and transactional benefits in the sub-context of CFS value aids in the identification of market power influences on customer value. The implications of the absence of high levels of relational and transactional benefits without high levels of core and add-on benefits may influence avenues of pursuit in improving RSP value overall. The research also suggests that clinical and strategic value was present despite varying degrees of industry structure. Finally, this study represents an empirical joint analysis of industry structure and customer value in robotic surgery. Some proponents may find the introduction of an integrative model for measuring customer value in robotic surgery, applicable to other capital-intensive medical innovations or disruptive technologies at large

Deep Homography Prediction for Endoscopic Camera Motion Imitation Learning

In this work, we investigate laparoscopic camera motion automation through imitation learning from retrospective videos of laparoscopic interventions. A novel method is introduced that learns to augment a surgeon's behavior in image space through object motion invariant image registration via homographies. Contrary to existing approaches, no geometric assumptions are made and no depth information is necessary, enabling immediate translation to a robotic setup. Deviating from the dominant approach in the literature which consist of following a surgical tool, we do not handcraft the objective and no priors are imposed on the surgical scene, allowing the method to discover unbiased policies. In this new research field, significant improvements are demonstrated over two baselines on the Cholec80 and HeiChole datasets, showcasing an improvement of 47% over camera motion continuation. The method is further shown to indeed predict camera motion correctly on the public motion classification labels of the AutoLaparo dataset. All code is made accessible on GitHub.Comment: Early accepted at MICCAI 202

Medicine + Health Magazine, Fall 2018

https://hsrc.himmelfarb.gwu.edu/smhs_medhealth/1016/thumbnail.jp

Technology spreading in healthcare: a novel era in medicine and surgery?

Surgery and technological innovation have begun to move at the speed of light, with innovations and discoveries such as virtual reality, robotic systems, navigation surgery, and 5G networks radically revolutionizing the surgical world as well as the medical world in general, bringing significant benefits for healthcare professionals and patients alike. Technology will increasingly be a crucial element in surgical and medical development. This new therapeutic approach aims to enhance human–computer interaction by putting a new “patient” figure at its center. Multiple studies will be needed to demonstrate new advanced technological systems’ noninferiority to traditional patient approaches. Scientific societies, hospitals, and healthcare professionals cannot be found ill prepared for this revolution