Autonomous Camera Movement for Robotic-Assisted Surgery: A Survey

In the past decade, Robotic-Assisted Surgery (RAS) has become a widely accepted technique as an alternative to traditional open surgery procedures. The best robotic assistant system should combine both human and robot capabilities under the human control. As a matter of fact robot should collaborate with surgeons in a natural and autonomous way, thus requiring less of the surgeons\u27 attention. In this survey, we provide a comprehensive and structured review of the robotic-assisted surgery and autonomous camera movement for RAS operation. We also discuss several topics, including but not limited to task and gesture recognition, that are closely related to robotic-assisted surgery automation and illustrate several successful applications in various real-world application domains. We hope that this paper will provide a more thorough understanding of the recent advances in camera automation in RSA and offer some future research directions

Variations in Surgeon-Applied Loads During Passive Range of Motion Following Total Knee Replacement With Relevance To Computational Modeling

Total knee replacement (TKR) is generally considered a successful treatment for musculoskeletal disorders of the knee. However, as many as 20% of patients report some dissatisfaction in their physical function after TKR. And approximately 50% of early revisions needed to address conditions related to component alignment and soft tissue tension to stabilize the knee. During TKR, surgeons manually perform passive range of motion (ROM) assessments to gain feedback perceived as tension in ligaments and other soft tissues. Such assessments are highly subjective and rely on the surgeon\u27s perception of soft tissue tension rather than quantitative objective means. The variability in applied loads during passive ROM assessments is poorly understood. The broad objective of this thesis was to analyze variations in surgeon-applied loads during passive ROM assessments following TKR on individual cadaver knees. There were three specific aims: 1) experimentally measure surgeon-applied loads during passive ROM of cadaver limbs implanted with TKR; 2) statistically analyze intra-specimen and inter-specimen repeatability in surgeon-applied loading profiles; and 3) process surgeon-applied external loads for input into computational models used to calculate knee ligament tensions. Three cadaveric lower limbs were implanted with TKR and mounted into a custom-designed knee rig instrumented to simulate and measure applied loads and kinematics during passive ROM assessments performed by an experienced orthopaedic surgeon. It was hypothesized that intra-specimen cycles would not be a significant factor affecting the applied loading profiles. It was hypothesized that inter-specimen differences would be a significant factor affecting applied loading profiles. The 4 degrees of freedom tracked (varus-valgus, anterior-posterior, compressive load, and internal-external rotation), external loads applied by the surgeon were highly consistent within the five cycles per trial and the 95% confidence interval varied within 0.5Nm for applied moments and within 5N for applied compressive forces. It was concluded that intra-specimen cycles were not a factor affecting the load profiles and inter-specimen differences were a significant factor affecting applied loading profiles. Variations in external loads during intra-operative assessments of component alignment and soft tissue tension can impact clinical decisions and outcomes. In a biomechanical sense, new technologies and sensors meant to aid intra-operative decisions need to accommodate variability in assumed load magnitudes during passive ROM assessments

Trends in virtual reality technologies for the learning patient

NextMed convened the Medicine Meets Virtual Reality 22 (MMVR 22) conference in 2016. Since 1992, the conference has brought together a diverse group of researchers to share creative solutions for the evolving challenge of integrating virtual reality tools into medical education. Virtual reality (VR) and its enabling technologies utilize hardware and software to simulate environments and encounters where users can interact and learn. The MMVR 22 symposium proceedings contain projects that support a variety of learners: medical students, practitioners, soldiers, and patients. This report will contemplate the trends in virtual reality technologies for patients navigating their medical and healthcare learning. The learning patient seeks more than intervention; they seek prevention. From virtual humans and environments to motion sensors and haptic devices, patients are surrounded by increasingly rich and transformative data-driven tools. Applied data enables VR applications to simulate experience, predict health outcomes, and motivate new behavior. The MMVR 22 presents investigations into the usability of wearable devices, the efficacy of avatar inclusion, and the viability of multi-player gaming. With increasing need for individualized and scalable programming, only committed open source efforts will align instructional designers, technology integrators, trainers, and clinicians. Curriculum and InstructionCurriculum and Instructio

Methods and Tools for Objective Assessment of Psychomotor Skills in Laparoscopic Surgery

Training and assessment paradigms for laparoscopic surgical skills are evolving from traditional mentor–trainee tutorship towards structured, more objective and safer programs. Accreditation of surgeons requires reaching a consensus on metrics and tasks used to assess surgeons’ psychomotor skills. Ongoing development of tracking systems and software solutions has allowed for the expansion of novel training and assessment means in laparoscopy. The current challenge is to adapt and include these systems within training programs, and to exploit their possibilities for evaluation purposes. This paper describes the state of the art in research on measuring and assessing psychomotor laparoscopic skills. It gives an overview on tracking systems as well as on metrics and advanced statistical and machine learning techniques employed for evaluation purposes. The later ones have a potential to be used as an aid in deciding on the surgical competence level, which is an important aspect when accreditation of the surgeons in particular, and patient safety in general, are considered. The prospective of these methods and tools make them complementary means for surgical assessment of motor skills, especially in the early stages of training. Successful examples such as the Fundamentals of Laparoscopic Surgery should help drive a paradigm change to structured curricula based on objective parameters. These may improve the accreditation of new surgeons, as well as optimize their already overloaded training schedules

A Novel Haptic Simulator for Evaluating and Training Salient Force-Based Skills for Laparoscopic Surgery

Laparoscopic surgery has evolved from an \u27alternative\u27 surgical technique to currently being considered as a mainstream surgical technique. However, learning this complex technique holds unique challenges to novice surgeons due to their \u27distance\u27 from the surgical site. One of the main challenges in acquiring laparoscopic skills is the acquisition of force-based or haptic skills. The neglect of popular training methods (e.g., the Fundamentals of Laparoscopic Surgery, i.e. FLS, curriculum) in addressing this aspect of skills training has led many medical skills professionals to research new, efficient methods for haptic skills training. The overarching goal of this research was to demonstrate that a set of simple, simulator-based haptic exercises can be developed and used to train users for skilled application of forces with surgical tools. A set of salient or core haptic skills that underlie proficient laparoscopic surgery were identified, based on published time-motion studies. Low-cost, computer-based haptic training simulators were prototyped to simulate each of the identified salient haptic skills. All simulators were tested for construct validity by comparing surgeons\u27 performance on the simulators with the performance of novices with no previous laparoscopic experience. An integrated, \u27core haptic skills\u27 simulator capable of rendering the three validated haptic skills was built. To examine the efficacy of this novel salient haptic skills training simulator, novice participants were tested for training improvements in a detailed study. Results from the study demonstrated that simulator training enabled users to significantly improve force application for all three haptic tasks. Research outcomes from this project could greatly influence surgical skills simulator design, resulting in more efficient training

Surgical skills modeling in cardiac ablation using deep learning

Cardiovascular diseases, a leading global cause of death, can be treated using Minimally Invasive Surgery (MIS) for various heart conditions. Cardiac ablation is an example of MIS, treating heart rhythm disorders like atrial fibrillation and the operation outcomes are highly dependent on the surgeon's skills. This procedure utilizes catheters, flexible endovascular devices inserted into the patient's blood vessels through a small incision. Traditionally, novice surgeons' performance is assessed in the Operating Room (OR) through surgical tasks. Unskilled behavior can lead to longer operations and inferior surgical outcomes. However, an alternative approach can be capturing surgeons' maneuvers and using them as input for an AI model to evaluate their skills outside the OR. To this end, two experimental setups were proposed to study the skills modelling for surgical behaviours. The first setup simulates the ablation procedure using a mechanical system with a synthetic heartbeat mechanism that measures contact forces between the catheter's tip and tissue. The second one simulates the cardiac catheterization procedure for the surgeon’s practice and records the user's maneuvers at the same time. The first task involved maintaining the force within a safe range while the tip of the catheter is touching the surface. The second task was passing a catheter’s tip through curves and level-intersection on a transparent blood vessel phantom. To evaluate attendees' demonstrations, it is crucial to extract maneuver models for both expert and novice surgeons. Data from participants, including novices and experts, performing the task using the experimental setups, is compiled. Deep recurrent neural networks are employed to extract the model of skills by solving a binary classification problem, distinguishing between expert and novice maneuvers. The results demonstrate the proposed networks' ability to accurately distinguish between novice and expert surgical skills, achieving an accuracy of over 92%

Design and Experimental Evaluation of a Haptic Robot-Assisted System for Femur Fracture Surgery

In the face of challenges encountered during femur fracture surgery, such as the high rates of malalignment and X-ray exposure to operating personnel, robot-assisted surgery has emerged as an alternative to conventional state-of-the-art surgical methods. This paper introduces the development of Robossis, a haptic system for robot-assisted femur fracture surgery. Robossis comprises a 7-DOF haptic controller and a 6-DOF surgical robot. A unilateral control architecture is developed to address the kinematic mismatch and the motion transfer between the haptic controller and the Robossis surgical robot. A real-time motion control pipeline is designed to address the motion transfer and evaluated through experimental testing. The analysis illustrates that the Robossis surgical robot can adhere to the desired trajectory from the haptic controller with an average translational error of 0.32 mm and a rotational error of 0.07 deg. Additionally, a haptic rendering pipeline is developed to resolve the kinematic mismatch by constraining the haptic controller (user hand) movement within the permissible joint limits of the Robossis surgical robot. Lastly, in a cadaveric lab test, the Robossis system assisted surgeons during a mock femur fracture surgery. The result shows that Robossis can provide an intuitive solution for surgeons to perform femur fracture surgery.Comment: This paper is to be submitted to an IEEE journa