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This study proposes simulation-based surgical navigation concept and describes the development of a laparoscopic surgical simulator that reflects the motion of surgical instruments in the real world. In the proposed simulation-based surgical navigation, movements of the surgical instruments are captured by a motion capture system, and the movements of the real surgical instruments are reflected in the movements of the virtual instruments in the simulation in real time. Contact of the virtual surgical instruments with organ model is detected based on the signed distance field (SDF) made around the organ model. The deformations of organs caused by contacts are calculated using dynamic finite element method (FEM). Using a cubic elastic object made of urethane resin, the accuracy of the calculation of the deformation was verified. The average error in the deformation verification experiments was within 1 mm. Simulations using hepato-biliary-pancreatic finite element (FE) models were performed, and computational costs of the simulation were validated. The time for one loop simulation with a hepato-biliary-pancreatic FE model of 3,225 elements and 1,663 nodes was 50 ms. The developed simulator can be applied to a simulation-based navigation system to update the states of organs in real time. [ABSTRACT FROM AUTHOR]

Laparoscopic surgery is a minimally invasive procedure that is performed by viewing endoscopic camera images. However, the limited field of view of endoscopic cameras makes laparoscopic surgery difficult. To provide more visual information during laparoscopic surgeries, augmented reality (AR) surgical navigation systems have been developed to visualize the positional relationship between the surgical field and organs based on preoperative medical images of a patient. However, since earlier studies used preoperative medical images, the navigation became inaccurate as the surgery progressed because the organs were displaced and deformed during surgery. To solve this problem, we propose a mixed reality (MR) surgery navigation system in which surgical instruments are tracked by a motion capture (Mocap) system; we also evaluated the contact between the instruments and organs and simulated and visualized the deformation of the organ caused by the contact. This paper describes a method for the numerical calculation of the deformation of a soft body. Then, the basic technology of MR and projection mapping is presented for MR surgical navigation. The accuracy of the simulated and visualized deformations is evaluated through basic experiments using a soft rectangular cuboid object. [ABSTRACT FROM AUTHOR]

The purpose of this study was to characterize the motion features of surgical devices associated with laparoscopic surgical competency and build an automatic skill-credential system in porcine cadaver organ simulation training. Participants performed tissue dissection around the aorta, dividing vascular pedicles after applying Hem-o-lok (tissue dissection task) and parenchymal closure of the kidney (suturing task). Movements of surgical devices were tracked by a motion capture (Mocap) system, and Mocap-metrics were compared according to the level of surgical experience (experts: ≥50 laparoscopic surgeries, intermediates: 10–49, novices: 0–9), using the Kruskal-Wallis test and principal component analysis (PCA). Three machine-learning algorithms: support vector machine (SVM), PCA-SVM, and gradient boosting decision tree (GBDT), were utilized for discrimination of the surgical experience level. The accuracy of each model was evaluated by nested and repeated k-fold cross-validation. A total of 32 experts, 18 intermediates, and 20 novices participated in the present study. PCA revealed that efficiency-related metrics (e.g., path length) significantly contributed to PC 1 in both tasks. Regarding PC 2, speed-related metrics (e.g., velocity, acceleration, jerk) of right-hand devices largely contributed to the tissue dissection task, while those of left-hand devices did in the suturing task. Regarding the three-group discrimination, in the tissue dissection task, the GBDT method was superior to the other methods (median accuracy: 68.6%). In the suturing task, SVM and PCA-SVM methods were superior to the GBDT method (57.4 and 58.4%, respectively). Regarding the two-group discrimination (experts vs. intermediates/novices), the GBDT method resulted in a median accuracy of 72.9% in the tissue dissection task, and, in the suturing task, the PCA-SVM method resulted in a median accuracy of 69.2%. Overall, the mocap-based credential system using machine-learning classifiers provides a correct judgment rate of around 70% (two-group discrimination). Together with motion analysis and wet-lab training, simulation training could be a practical method for objectively assessing the surgical competence of trainees. [ABSTRACT FROM AUTHOR]

Kinect has been utilized as a cost-effective, easy-to-use motion capture sensor using the Kinect skeleton algorithm. However, a limited number of landmarks and inaccuracies in tracking the landmarks' positions restrict Kinect's capability. In order to increase the accuracy of motion capturing using Kinect, joint use of the Kinect skeleton algorithm and Kinect-based marker tracking was applied to track the 3D coordinates of multiple landmarks on human. The motion's kinematic parameters were calculated using the landmarks' positions by applying the joint constraints and inverse kinematics techniques. The accuracy of the proposed method and OptiTrack (NaturalPoint, Inc., USA) was evaluated in capturing the joint angles of a humanoid (as ground truth) in a walking test. In order to evaluate the accuracy of the proposed method in capturing the kinematic parameters of a human, lower body joint angles of five healthy subjects were extracted using a Kinect, and the results were compared to Perception Neuron (Noitom Ltd., China) and OptiTrack data during ten gait trials. The absolute agreement and consistency between each optical system and the robot data in the robot test and between each motion capture system and OptiTrack data in the human gait test were determined using intraclass correlations coefficients (ICC3). The reproducibility between systems was evaluated using Lin's concordance correlation coefficient (CCC). The correlation coefficients with 95% confidence intervals (95%CI) were interpreted substantial for both OptiTrack and proposed method (ICC > 0.75 and CCC > 0.95) in humanoid test. The results of the human gait experiments demonstrated the advantage of the proposed method (ICC > 0.75 and RMSE = 1.1460°) over the Kinect skeleton model (ICC < 0.4 and RMSE = 6.5843°). [ABSTRACT FROM AUTHOR]

Background: Our aim was to characterize the motions of multiple laparoscopic surgical instruments among participants with different levels of surgical experience in a series of wet-lab training drills, in which participants need to perform a range of surgical procedures including grasping tissue, tissue traction and dissection, applying a Hem-o-lok clip, and suturing/knotting, and digitize the level of surgical competency. Methods: Participants performed tissue dissection around the aorta, dividing encountered vessels after applying a Hem-o-lok (Task 1), and renal parenchymal closure (Task 2: suturing, Task 3: suturing and knot-tying), using swine cadaveric organs placed in a box trainer under a motion capture (Mocap) system. Motion-related metrics were compared according to participants' level of surgical experience (experts: 50 ≤ laparoscopic surgeries, intermediates: 10–49, novices: 0–9), using the Kruskal–Wallis test, and significant metrics were subjected to principal component analysis (PCA). Results: A total of 15 experts, 12 intermediates, and 18 novices participated in the training. In Task 1, a shorter path length and faster velocity/acceleration/jerk were observed using both scissors and a Hem-o-lok applier in the experts, and Hem-o-lok-related metrics markedly contributed to the 1st principal component on PCA analysis, followed by scissors-related metrics. Higher-level skills including a shorter path length and faster velocity were observed in both hands of the experts also in tasks 2 and 3. Sub-analysis showed that, in experts with 100 ≤ cases, scissors moved more frequently in the "close zone (0 ≤ to < 2.0 cm from aorta)" than those with 50–99 cases. Conclusion: Our novel Mocap system recognized significant differences in several metrics in multiple instruments according to the level of surgical experience. "Applying a Hem-o-lok clip on a pedicle" strongly reflected the level of surgical experience, and zone-metrics may be a promising tool to assess surgical expertise. Our next challenge is to give completely objective feedback to trainees on-site in the wet-lab. [ABSTRACT FROM AUTHOR]