Advanced Intelligent Systems for Surgical Robotics
Graduate School of Biomedical Engineering, Faculty of Engineering, UNSW（新南威尔士大学工程学院生物医学工程研究生院）；Prince of Wales Hospital（威尔士亲王医院）；Faculty of Medicine, UNSW（新南威尔士大学医学院）
Mai Thanh Thai, Phuoc Thien Phan, Shing Wong, Nigel H. Lovell, Thanh Nho Do
Surgical robots have had clinical use since the mid 1990s. Robot-assisted surgeries offer many benefits over the conventional approach including lower risk of infection and blood loss, shorter recovery, and an overall safer procedure for patients. The past few decades have shown many emerging surgical robotic platforms that can work in complex and confined channels of the internal human organs and improve the cognitive and physical skills of the surgeons during the operation. Advanced technologies for sensing, actuation, and intelligent control have enabled multiple surgical devices to simultaneously operate within the human body at low cost and with more efficiency. Despite advances, current surgical intervention systems are not able to execute autonomous tasks and make cognitive decisions that are analogous to that of humans. This paper will overview a historical development of surgery from conventional open to robotic-assisted approaches with discussion on the capabilities of advanced intelligent systems and devices that are currently implemented in existing surgical robotic systems. It will also revisit available autonomous surgical platforms with comments on the essential technologies, existing challenges, and suggestions for the future development of intelligent robotic-assisted surgical systems towards the achievement of fully autonomous operation.
Imperial College London（伦敦帝国理工学院）；University of Leeds（利兹大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；Worcester Polytechnic Institute（伍斯特理工学院）；Intuitive Surgical Inc（直觉外科公司）
Robotic-assisted surgery is now well-established in clinical practice and has become the gold standard clinical treatment option for several clinical indications. The field of robotic-assisted surgery is expected to grow substantially in the next decade with a range of new robotic devices emerging to address unmet clinical needs across different specialities. A vibrant surgical robotics research community is pivotal for conceptualizing such new systems as well as for developing and training the engineers and scientists to translate them into practice. The da Vinci Research Kit (dVRK), an acade…
Reinforcement Learning (RL) is a machine learning framework for artificially intelligent systems to solve a variety of complex problems. Recent years has seen a surge of successes solving challenging games and smaller domain problems, including simple though non-specific robotic manipulation and grasping tasks. Rapid successes in RL have come in part due to the strong collaborative effort by the RL community to work on common, open-sourced environment simulators such as OpenAI's Gym that allow for expedited development and valid comparisons between different, state-of-art strategies. In this p…
SuFIA: Language-Guided Augmented Dexterity for Robotic Surgical Assistants
University of Toronto（多伦多大学）；University of Bern（伯尔尼大学）；University of California, Berkeley（加利福尼亚大学伯克利分校）；NVIDIA（英伟达）；Georgia Institute of Technology（佐治亚理工学院）
The University of Tokyo（东京大学）；Kitasato University（北里大学）；Saitama Children’s Medical Center（埼玉儿童医疗中心）
Commercially available surgical-robot technology currently addresses many surgical scenarios for adult patients. This same technology cannot be used to the benefit of neonate patients given the considerably smaller workspace. Medically relevant procedures regarding neonate patients include minimally invasive surgery to repair congenital esophagus disorders, which entail the suturing of the fragile esophagus within the narrow neonate cavity. In this work, we explore the use of the SmartArm robotic system in a feasibility study using a neonate chest and esophagus model. We show that a medically …
National University of Singapore（新加坡国立大学）；NUSRI Suzhou China（新加坡国立大学苏州研究院）；The Chinese University of Hong Kong（香港中文大学）；Imperial College London（伦敦帝国理工学院）；Duke-NUS Medical School（杜克-新加坡国立大学医学院）
Generating a surgical report in robot-assisted surgery, in the form of natural language expression of surgical scene understanding, can play a significant role in document entry tasks, surgical training, and post-operative analysis. Despite the state-of-the-art accuracy of the deep learning algorithm, the deployment performance often drops when applied to the Target Domain (TD) data. For this purpose, we develop a multi-layer transformer-based model with the gradient reversal adversarial learning to generate a caption for the multi-domain surgical images that can describe the semantic relation…
Automatic recognition and classification of tasks in robotic surgery is an important stepping stone toward automated surgery and surgical training. Recently, technical breakthroughs in gathering data make data-driven model development possible. In this paper, we propose a framework for high-level robotic surgery task recognition using motion data. We present a novel classification technique that is used to classify three important surgical tasks through quantitative analyses of motion: knot tying, needle passing and suturing. The proposed technique integrates state-of-the-art data mining and t…
In recent years, surgical robots have become instrumental tools for assisting surgeons in performing complex surgical procedures in hospitals. Unlike conventional surgical methods, robotic systems help surgeons, for example, to perform minimally invasive surgical procedures while enhancing the precision and control of operations (e.g. tiny incisions, wound sutures, endoscopic suturing, among others). To this extent, it is essential to consider several factors that may influence the feasibility and decision making of employing robotic systems in surgical procedures. In this paper, we propose an…
Rochester Institute of Technology（罗切斯特理工学院）；University of Washington（华盛顿大学）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）
The Raven I and the Raven II surgical robots, as open research platforms, have been serving the robotic surgery research community for ten years. The paper 1) briefly presents the Raven I and the Raven II robots, 2) reviews the recent publications that are built upon the Raven robots, aim to be applied to the Raven robots, or are directly compared with the Raven robots, and 3) uses the Raven robots as a case study to discuss the popular research problems in the research community and the trend of robotic surgery study. Instead of being a thorough literature review, this work only reviews the w…
Datasets are an essential component for training effective machine learning models. In particular, surgical robotic datasets have been key to many advances in semi-autonomous surgeries, skill assessment, and training. Simulated surgical environments can enhance the data collection process by making it faster, simpler and cheaper than real systems. In addition, combining data from multiple robotic domains can provide rich and diverse training data for transfer learning algorithms. In this paper, we present the DESK (Dexterous Surgical Skill) dataset. It comprises a set of surgical robotic skill…
Design of an innovative robotic surgical instrument for circular stapling
University of Medicine and Pharmacy “Iuliu Hațieganu”（“尤利乌·哈蒂埃加努”医药大学）；École Centrale Nantes（南特中央理工学校）；Nantes Université（南特大学）；Technical Sciences Academy of Romania（罗马尼亚技术科学院）
University of California San Diego（加利福尼亚大学圣地亚哥分校）；Kyung Hee University（庆熙大学）
Innovations from surgical robotic research rarely translates to live surgery due to the significant difference between the lab and a live environment. Live environments require considerations that are often overlooked during early stages of research such as surgical staff, surgical procedure, and the challenges of working with live tissue. One such example is the da Vinci Research Kit (dVRK) which is used by over 40 robotics research groups and represents an open-sourced version of the da Vinci Surgical System. Despite dVRK being available for nearly a decade and the ideal candidate for transl…
STRAS (Single access Transluminal Robotic Assistant for Surgeons) is a new robotic system for application to intraluminal surgical procedures. Preclinical testing of STRAS has recently permitted to demonstrate major advantages of the system in comparison with classic procedures. Benchmark methods permitting to establish objective criteria for expertise need to be worked out now to effectively train surgeons on this new system in the near future. STRAS consists of three cable driven subsystems, one endoscope serving as guide, and two flexible instruments. The flexible instruments have three deg…
Gastrointestinal (GI) cancers account for 1.5 million deaths worldwide. Endoscopic Submucosal Dissection (ESD) is an advanced therapeutic endoscopy technique with superior clinical outcome due to the minimally invasive and en bloc removal of tumours. In the western world, ESD is seldom carried out, due to its complex and challenging nature. Various surgical systems are being developed to make this therapy accessible, however, these solutions have shown limited operational workspace, dexterity, or low force exertion capabilities. The current paper shows the ESD CYCLOPS system, a bimanual surgic…
Autonomous robotic surgery has seen significant progression over the last decade with the aims of reducing surgeon fatigue, improving procedural consistency, and perhaps one day take over surgery itself. However, automation has not been applied to the critical surgical task of controlling tissue and blood vessel bleeding--known as hemostasis. The task of hemostasis covers a spectrum of bleeding sources and a range of blood velocity, trajectory, and volume. In an extreme case, an un-controlled blood vessel fills the surgical field with flowing blood. In this work, we present the first, automate…
University of Texas at Dallas（德克萨斯大学达拉斯分校）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；UT Southwestern Medical Center（德州大学西南医学中心）
Surgical skill directly affects surgical procedure outcomes; thus, effective training is needed to ensure satisfactory results. Many objective assessment metrics have been developed and some are widely used in surgical training simulators. These objective metrics provide the trainee with descriptive feedback about their performance however, often lack feedback on how to proceed to improve performance. The most effective training method is one that is intuitive, easy to understand, personalized to the user and provided in a timely manner. We propose a framework to enable user-adaptive training …
Imperial College London（帝国理工学院）；Shanghai Jiao Tong University（上海交通大学）
Accurate kinematic models are essential for effective control of surgical robots. For tendon driven robots, which is common for minimally invasive surgery, intrinsic nonlinearities are important to consider. Traditional analytical methods allow to build the kinematic model of the system by making certain assumptions and simplifications on the nonlinearities. Machine learning techniques, instead, allow to recover a more complex model based on the acquired data. However, analytical models are more generalisable, but can be over-simplified; data-driven models, on the other hand, can cater for mor…
Control Modes of Teleoperated Surgical Robotic System's Tools in Ophthalmic Surgery
Department of Mechanical and Aerospace Engineering at the University of California, Los Angeles (UCLA)（加州大学洛杉矶分校机械与航空航天工程系）；Jules Stein Eye Institute at the University of California, Los Angeles (UCLA)（加州大学洛杉矶分校Jules Stein眼科研究所）
Imperial College London（帝国理工学院）；German Aerospace Center（德国航空航天中心）；Technical University of Munich（慕尼黑工业大学）；Charing Cross Hospital（查令十字医院）
The aim of this paper is to introduce a robotic platform for autonomous iUS tissue scanning to optimise intraoperative diagnosis and improve surgical resection during robot-assisted operations. To guide anatomy specific robotic scanning and generate a representation of the robot task space, fast and accurate techniques for the recovery of 3D morphological structures of the surgical cavity are developed. The prototypic DLR MIRO surgical robotic arm is used to control the applied force and the in-plane motion of the US transducer. A key application of the proposed platform is the scanning of bra…
S3D: A Spatial Steerable Surgical Drilling Framework for Robotic Spinal Fixation Procedures
Walker Department of Mechanical Engineering and the Texas Robotics at the University of Texas at Austin（德克萨斯大学奥斯汀分校机械工程系和德克萨斯机器人实验室）；Department of Neurosurgery, The University of Texas Dell Medical School（德克萨斯大学德尔医学中心神经外科系）；School of Informatics, University of Edinburgh（爱丁堡大学信息学院）
Purdue University（普渡大学）；Indiana University School of Medicine（印第安纳大学医学院）；Johns Hopkins University（约翰斯·霍普金斯大学）
Short response time is critical for future military medical operations in austere settings or remote areas. Such effective patient care at the point of injury can greatly benefit from the integration of semi-autonomous robotic systems. To achieve autonomy, robots would require massive libraries of maneuvers. While this is possible in controlled settings, obtaining surgical data in austere settings can be difficult. Hence, in this paper, we present the Dexterous Surgical Skill (DESK) database for knowledge transfer between robots. The peg transfer task was selected as it is one of 6 main tasks …
High-Precision Surgical Robotic System for Intraocular Procedures
Department of Mechanical and Aerospace Engineering, University of California, Los Angeles（机械与航空航天工程系，加州大学洛杉矶分校）；Stein Eye Institute, University of California, Los Angeles（斯坦眼科研究所，加州大学洛杉矶分校）
University of California San Diego（加州大学圣迭戈分校）；East China Normal University（华东师范大学）
Robotic automation in surgery requires precise tracking of surgical tools and mapping of deformable tissue. Previous works on surgical perception frameworks require significant effort in developing features for surgical tool and tissue tracking. In this work, we overcome the challenge by exploiting deep learning methods for surgical perception. We integrated deep neural networks, capable of efficient feature extraction, into the tissue reconstruction and instrument pose estimation processes. By leveraging transfer learning, the deep learning based approach requires minimal training data and re…
Digital Surgery Limited（数字手术有限公司）；University College London（伦敦大学学院）；Kasetsart University（泰国农业大学）
Robotic surgery and novel surgical instrumentation present great potentials towards safer, more accurate and consistent minimally invasive surgery. However, their adoption is dependent to the access to training facilities and extensive surgical training. Robotic instruments require different dexterity skills compared to open or laparoscopic. Surgeons, therefore, are required to invest significant time by attending extensive training programs. Contrary, hands on experiences represent an additional operational cost for hospitals as the availability of robotic systems for training purposes is lim…
Robotic assistance allows surgeons to perform dexterous and tremor-free procedures, but robotic aid is still underrepresented in procedures with constrained workspaces, such as deep brain neurosurgery and endonasal surgery. In these procedures, surgeons have restricted vision to areas near the surgical tooltips, which increases the risk of unexpected collisions between the shafts of the instruments and their surroundings. In this work, our vector-field-inequalities method is extended to provide dynamic active-constraints to any number of robots and moving objects sharing the same workspace. Th…
Politecnico di Milano（米兰理工大学）；University College London（伦敦大学学院）
Robot-assisted laparoscopic prostatectomy (RALP) is a treatment for prostate cancer that involves complete or nerve sparing removal prostate tissue that contains cancer. After removal the bladder neck is successively sutured directly with the urethra. The procedure is called urethrovesical anastomosis and is one of the most dexterity demanding tasks during RALP. Two suturing instruments and a pair of needles are used in combination to perform a running stitch during urethrovesical anastomosis. While robotic instruments provide enhanced dexterity to perform the anastomosis, it is still highly c…
Master control console is a place where robots collaborate with humans in a shared environment. To this end, ergonomics is an important aspect to be considered. With ergonomic design, the surgeons can feel more comfortable to conduct the surgical tasks with higher efficiency, and the quality of the teleoperated robotic surgery can be improved. In this paper, an Ergonomic Interaction Workspace Analysis method is proposed to optimize master manipulators and fulfil ergonomics consideration for designing a master manipulator for teleoperated robotic surgery.
LIRMM, University of Montpellier, CNRS（蒙彼利埃大学法国国家科学研究中心LIRMM实验室）；Stanford University（斯坦福大学）
The current generation of surgeons requires extensive training in teleoperation to develop specific dexterous skills, which are independent of medical knowledge. Training curricula progress from manipulation tasks to simulated surgical tasks but are limited in time. To tackle this, we propose to integrate surgical robotic training together with Haptic Feedback (HF) to improve skill acquisition. This paper present the initial but promising results of our haptic device designed to support in the training of surgical gestures. Our ongoing work is related to integrate the HF in the RAVEN II platfo…
SonoGym: High Performance Simulation for Challenging Surgical Tasks with Robotic Ultrasound
Balgrist University Hospital（巴尔格里斯大学医院）；ETH Zurich（苏黎世联邦理工学院）；University of Toronto（多伦多大学）；NVIDIA（NVIDIA公司）；ETH AI Center（苏黎世联邦理工学院人工智能中心）；University of Zurich（苏黎世大学）
Tissue manipulation is a frequently used fundamental subtask of any surgical procedures, and in some cases it may require the involvement of a surgeon's assistant. The complex dynamics of soft tissue as an unstructured environment is one of the main challenges in any attempt to automate the manipulation of it via a surgical robotic system. Two AI learning based model predictive control algorithms using vision strategies are proposed and studied: (1) reinforcement learning and (2) learning from demonstration. Comparison of the performance of these AI algorithms in a simulation setting indicated…
The CYCLOPS is a cable-driven parallel mechanism used for minimally invasive applications, with the ability to be customised to different surgical needs; allowing it to be made procedure- and patient-specific. For adequate optimisation, however, appropriate data on clinical constraints and task-space is required. Whereas the former can be provided through preoperative planning and imaging, the latter remains a problem, primarily for highly dexterous MIS systems. The current work focuses on the development of a task-space optimisation method for the CYCLOPS system and the development of a data …
Robotic-assisted minimally invasive surgery (MIS) has enabled procedures with increased precision and dexterity, but surgical robots are still open loop and require surgeons to work with a tele-operation console providing only limited visual feedback. In this setting, mechanical failures, software faults, or human errors might lead to adverse events resulting in patient complications or fatalities. We argue that impending adverse events could be detected and mitigated by applying context-specific safety constraints on the motions of the robot. We present a context-aware safety monitoring syste…
The Chinese University of Hong Kong（香港中文大学）；The Johns Hopkins University（约翰斯·霍普金斯大学）
Robot-assisted surgery has become progressively more and more popular due to its clinical advantages. In the meanwhile, the artificial intelligence and augmented reality in robotic surgery are developing rapidly and receive lots of attention. However, current methods have not discussed the coherent integration of AI and AR in robotic surgery. In this paper, we develop a novel system by seamlessly merging artificial intelligence module and augmented reality visualization to automatically generate the surgical guidance for robotic surgery education. Specifically, we first leverage reinforcement …
Many have explored the application of continuum robot manipulators for minimally invasive surgery, and have successfully demonstrated the advantages their flexible design provides -- with some solutions having reached commercialisation and clinical practice. However, the usual high complexity and closed-nature of such designs has traditionally restricted the shared development of continuum robots across the research area, thus impacting further progress and the solution of open challenges. In order to close this gap, this paper introduces ENDO, an open-source 3-segment continuum robot manipula…
City University of Hong Kong（香港城市大学）；The Chinese University of Hong Kong（香港中文大学）
Automatic laparoscope motion control is fundamentally important for surgeons to efficiently perform operations. However, its traditional control methods based on tool tracking without considering information hidden in surgical scenes are not intelligent enough, while the latest supervised imitation learning (IL)-based methods require expensive sensor data and suffer from distribution mismatch issues caused by limited demonstrations. In this paper, we propose a novel Imitation Learning framework for Laparoscope Control (ILLC) with reinforcement learning (RL), which can efficiently learn the con…
Overcoming Imperfect Kinematics in Surgical Robotics Through Sim-to-Real Visuomotor Learning
Hamlyn Centre for Robotic Surgery, Institute of Global Health Innovation, Imperial College London（伦敦帝国理工学院全球健康创新研究所哈姆林机器人手术中心）；Department of Surgery and Cancer, Imperial College London（伦敦帝国理工学院外科与癌症系）；Department of Mechanical Engineering, Imperial College London（伦敦帝国理工学院机械工程系）
University of California San Diego（加利福尼亚大学圣地亚哥分校）；Zhejiang University（浙江大学）；Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies（阿里巴巴-浙江大学前沿技术联合研究院）
Traditional control and task automation have been successfully demonstrated in a variety of structured, controlled environments through the use of highly specialized modeled robotic systems in conjunction with multiple sensors. However, the application of autonomy in endoscopic surgery is very challenging, particularly in soft tissue work, due to the lack of high-quality images and the unpredictable, constantly deforming environment. In this work, we propose a novel surgical perception framework, SuPer, for surgical robotic control. This framework continuously collects 3D geometric information…
Robotic teleoperation brings great potential for advances within the field of surgery. The ability of a surgeon to reach patient remotely opens exciting opportunities. Early experience with telerobotic surgery has been interesting, but the clinical feasibility remains out of reach, largely due to the deleterious effects of communication delays. Teleoperation tasks are significantly impacted by unavoidable signal latency, which directly results in slower operations, less precision in movements, and increased human errors. Introducing significant changes to the surgical workflow, for example by …
