Characterization of force and torque interactions during a simulated transgastric appendectomy procedure

Using Contact Forces and Robot Arm Accelerations to Automatically Rate Surgeon Skill at Peg Transfer

OBJECTIVE

Most trainees begin learning robotic minimally invasive surgery by performing inanimate practice tasks with clinical robots such as the Intuitive Surgical da Vinci. Expert surgeons are commonly asked to evaluate these performances using standardized five-point rating scales, but doing such ratings is time consuming, tedious, and somewhat subjective. This paper presents an automatic skill evaluation system that analyzes only the contact force with the task materials, the broad-bandwidth accelerations of the robotic instruments and camera, and the task completion time.

METHODS

We recruited N = 38 participants of varying skill in robotic surgery to perform three trials of peg transfer with a da Vinci Standard robot instrumented with our Smart Task Board. After calibration, three individuals rated these trials on five domains of the Global Evaluative Assessment of Robotic Skill (GEARS) structured assessment tool, providing ground-truth labels for regression and classification machine learning algorithms that predict GEARS scores based on the recorded force, acceleration, and time signals.

RESULTS

Both machine learning approaches produced scores on the reserved testing sets that were in good to excellent agreement with the human raters, even when the force information was not considered. Furthermore, regression predicted GEARS scores more accurately and efficiently than classification.

CONCLUSION

A surgeon's skill at robotic peg transfer can be reliably rated via regression using features gathered from force, acceleration, and time sensors external to the robot.

SIGNIFICANCE

We expect improved trainee learning as a result of providing these automatic skill ratings during inanimate task practice on a surgical robot.

Development of a Haptic Interface for Natural Orifice Translumenal Endoscopic Surgery Simulation

Natural orifice translumenal endoscopic surgery (NOTES) is a minimally invasive procedure, which utilizes the body's natural orifices to gain access to the peritoneal cavity. The NOTES procedure is designed to minimize external scarring and patient trauma, however flexible endoscopy based pure NOTES procedures require critical scope handling skills. The delicate nature of the NOTES procedure requires extensive training. Thus, to improve access to training while reducing risk to patients, we have designed and developed the VTEST, a virtual reality NOTES simulator. As part of the simulator, a novel decoupled 2-DOF haptic device was developed to provide realistic force feedback to the user in training. A series of experiments were performed to determine the behavioral characteristics of the device. The device was found capable of rendering up to 5.62N and 0.190 Nm of continuous force and torque in the translational and rotational DOF, respectively. The device possesses 18.1 and 5.7 Hz of force bandwidth in the translational and rotational DOF, respectively. A feedforward friction compensator was also successfully implemented to minimize the negative impact of friction during the interaction with the device. In this work, we have presented the detailed development and evaluation of the haptic device for the VTEST.