Download Surgeon Simulator

The game's protagonist is a surgeon named Nigel Burke, who has a placement at a fictional hospital somewhere in the United Kingdom in 1987. He carries out various operations, at first on a patient affectionately named 'Bob' by the game developers, and later operates on Bob inside a space station orbiting Earth. Afterwards, he is contacted by an alien race by VHS tape, and operates on one of the aliens, gaining the title of 'Best Surgeon in the Universe'.

The original indie surgery sim, Surgeon Simulator puts your inner surgeon to the test. Have your shaky hands got what it takes to perform life-saving operations, or will you cause ultimate medical malpractice? Now owned and operated by tinyBuild Inc.

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Inspired by the popular board game Operation, Surgeon Operator converts you in an amateur surgeon that has to do open-heart surgery, for which you will have all the tools of a real surgeon at your disposal.

Surgeon Simulator 2013 is actually Surgeon Unsimulator 2013. Instead of having a surgeon's fluidly dextrous hands and vast anatomical knowledge, you have someone else's dead hands on sticks, and you know nothing other than what you saw in that half-glimpsed episode of Grey's Anatomy where Dr Beautiful was using a drill to stop someone's head exploding while Dr Sexy talked her through it over the phone.

A new technique of small group surgical teaching has been developed wherein the surgeon takes on the role of the patient. This technique, which incorporates extensive and immediate formative evaluation, has all the advantages of simulation techniques while avoiding the major problems of training, scheduling and cost. This method has been used to teach a wide variety of surgical disease processes with the major emphasis being teaching patient management strategies. Sixty-one medical students have been taught using this method and they have found it superior to conventional seminar teaching, particularly in the domains of problem solving, patient management strategies and thought provocation.

Whether you consider Surgeon Simulator any good really depends on how you approach this app. If you come at it hoping to be a pretend surgeon, you're likely to hate it. It's clumsy, has downright terrible controls, and revels in the death of the patient.

The game puts the player in the role of a surgeon operating on Bob, who has fallen off his toilet and now requires a heart transplant. The player is positioned above the hapless, draped form of Bob and must manipulate surgical tools and instruments to perform the operation.

Our previous study of adverse events reported to the U.S. Food and Drug Administration (FDA) Manufacturer and User Facility Device Experience (MAUDE) database showed that despite significant improvements in robotic surgery technology through the years and broader adoption of the robotic approach, there are ongoing occurrences of safety incidents that negatively impact patients. The number of injury and death events per procedure has stayed relatively constant since 2007, with an average of 83.4 events per 100,000 procedures [7]. Although, these incidents are often caused by accidental malfunctions or technical problems with the robot and steep learning curves, it has also been shown that surgical robots can be subject to malicious cyber-attacks that impact patient safety and progress of surgery [8, 9]. The ability of current robotic surgery technology to automatically mitigate the impact of safety incidents still lags other safety-critical industries, such as commercial aviation. In such industries, great effort has been spent over the years on improving safety practices by providing comprehensive simulation-based training that includes operation in the presence of safety-critical failures [10]. In current robotic surgeon training, the emphasis is on improving surgical skills and not on handling safety-critical events and responding to technical problems. Adverse events or accidental machine failures are rarely used as potential scenarios for safety training of surgical teams.

In this work, we are motivated by the idea of simulating safety hazards [11, 12] during robotic surgery training in order to prepare surgeons for handling safety-critical events. The objective is to develop a hardware-in-the-loop simulator platform that emulates realistic safety hazard scenarios in a virtual environment and provides awareness of the impeding hazards to the operator through haptic force feedback. In this work, we use Raven-II [13] surgical robot as the hardware that the operator will be trained with. Previous studies have shown that users trained on the Raven platform can transfer their skills to da Vinci system [14]. We developed a robot-environment interaction model using a physics engine as the robot's nominal state estimator (fault-free run), which runs simultaneously with the Raven-II robot hardware. We also developed a safety hazard injection engine that intentionally and artificially creates adverse events by inserting faults into the robot control system using Software-implemented Fault Injection (SWIFI) [15]. The faults are injected to the control software after the system's automatic safety checks are performed to increase the chance that they cause safety hazards.

When training a novice surgeon, he/she can acquire some sense of optimality by observing or sensing (through haptics) the robot's execution of an automated task. The motion can be planned optimally by minimizing certain cost functions. Related work in this area focuses on automating some of the real surgery scenarios in different robot-assisted surgery types. Weede et al. in [25] introduced an autonomous camera system including a prediction of interventions, to provide a long-term prediction of the steps a surgeon will perform in the next few minutes and move the endoscope to an optimal position. Combined with vision techniques, automatic positioning and autonomous retrieval of surgical instruments have been achieved in [26, 27]. Chow et al. in [28] showed that vision-guided autonomous knot-tying in robotic-assisted surgery has the potential to be faster than human performance. Kehoe et al. in [29] demonstrated the first reliable autonomous robot performance of surgical subtask, that is, removing tissue fragments using Raven, by generating centralized motion plans through 3D sensing and trajopt, a low-level motion planning algorithm based on sequential convex optimization to plan locally optimal, collision-free trajectories simultaneously for both arms. Hu et al. in [30] investigated path planning and semiautomated motion for the scenario of robotic ablation of tumor residues in various shapes using Raven robot, and different metrics were delivered to the surgeon to select candidate path plan. In nonlaparoscopic type of robotic surgery, for example, in needle steering community, efforts have been made in surgical preplanning for the needle type surgical robot [31, 32] and demonstrated in simulation environment [33].

The connection between the Surgeon Console and the Raven-II system is bilateral network communication in our hardware-in-the-loop simulator shown in Figure 2. One direction is for transmitting Omni command data from the local machine to the remote Raven system, while the other direction is for sending the robot state data (joint positions and velocities) back to the local machine using TCP/IP socket connection for reliability and to make comparison with the dynamics calculation results in physics engine thread. The haptic force feedback is provided to the operator if the virtual and real Raven's end-effector trajectories do not match (above a predefined threshold). Since perfect transparency (master device force/torque matching the slave's end-effector force/torque) is not possible and is especially challenging for teleoperators with significant nonlinear dynamics and no force sensors mounted at the robot end-effector, we utilize haptics feature for safety propose, rather than for surgical palpation. Because the haptic device sensor/actuator asymmetries can cause instability and robustness issues, we apply a spring-damper model with appropriate gains and saturations for feedback force calculation.

We send the ROS published joint states in the Raven computer to the simulator through the network. From the physics engine (ODE) thread, we extract the joint velocities. We compute the end-effector velocities by using spatial manipulator Jacobian transformation:

Based on our preliminary review of almost 1500 accident reports on the da Vinci surgical system from the FDA MAUDE database, we identified three categories of common safety hazard scenarios as shown in Table 1. We simulate these scenarios by injecting faults into the Raven control software during the training scenarios. The possible causes of hazards may include accidental faults in robotic hardware or software, unintentional human operator errors, or intentional malicious attacks to the control system of the robot. For each safety hazard, Table 1 shows the potential accidental causes (column 3) and impact on patients and surgical team (column 5) based on representative examples from the real incidents reported to the FDA MAUDE database. The patient impacts represent clinical scenarios and response actions on which the robotic surgeons should be trained on. ff782bc1db