Development of a Novel Tool for Assessing Deformation and Hardness of Real Organs: Pressure Measuring Grasper (PMEG)

Abstract

In recent years, surgeries requiring high surgical skills-including laparoscopic surgery and function-preserving surgery-are being more commonly conducted, which has led to the growing importance of surgeons training and preoperative simulations. Although various surgical simulators and 3D models of organs are now available, many surgeons still regard them as ineffective because they do not give a realistic sense of touch. In order to improve the quality of these simulations, it is necessary to collect data on how the shape of an organ is changed when pressed by laparoscopic forceps with various levels of force in actual operations. However, we have neither such data nor any equipment that can help us collect the data under operative environment. The main focus of this paper is to report on our development of the Pressure Measuring Grasper (hereinafter, PMEG) that can accurately measure the sizes of organs or tissues when they are grasped or pressed. PMEG is a modi cation of the digital vernier calipers, with the jaws modi ed to include our original parts (small load cells), making it possible to measure grasping pressure. The cross-sectional con guration of the PMEG jaws has the same structure as the tip of laparoscopic forceps, which allows the PMEG to simulate a situation in which tissue is grasped by laparoscopic forceps. We conducted two validation experiments to evaluate the measuring function of PMEG. One is veri cation of measuring pressure using weights, and the other is veri cation of measuring stiffness using a coil spring with known stiffness. These experiments showed that PMEG was able to measure the pressure and stiffness precisely. We also successfully used PMEG in a living pig s body, and expressed in numerical data the relationship between the surgeon s pressing force and organ deformation. The PMEG will contribute to the improvement of the surgical training system.

Vision-based estimation of manipulation forces by deep learning of laparoscopic surgical images obtained in a porcine excised kidney experiment

“…CA set the threshold of safety manipulation for the force, and the precision of the estimations was validated using the ratio of the time frames during which forces exceeding and falling below 0.5 N were detected, as a metric. Here, N (Newton) is the force unit, and the threshold, 0.5N, was tentatively set on the basis of our research group’s previous research 14 . This research evaluated the degree of deformation when force is applied to organs and the magnitude of force generated during such deformation.…”