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BackgroundOur 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.MethodsParticipants 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).ResultsA 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.ConclusionOur 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.

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.

This study developed and validated a surgical instrument motion measurement system for skill evaluation during practical laparoscopic surgery training. Owing to the various advantages of laparoscopic surgery including minimal invasiveness, this technique has been widely used. However, expert surgeons have insufficient time for providing training to beginners due to the shortage of surgeons and limited working hours. Skill transfer efficiency has to be improved for which there is an urgent need to develop objective surgical skill evaluation methods. Therefore, a simple motion capture–based surgical instrument motion measurement system that could be easily installed in an operating room for skill assessment during practical surgical training was developed. The tip positions and orientations of the instruments were calculated based on the marker positions attached to the root of the instrument. Because the patterns of these markers are individual, this system can track multiple instruments simultaneously and detect exchanges. However due to the many obstacles in the operating room, the measurement data included noise and outliers. In this study, the effect of this decrease in measurement accuracy on feature calculation was determined. Accuracy verification experiments were conducted during wet-lab training to demonstrate the capability of this system to measure the motion of surgical instruments with practical accuracy. A surgical training experiment on a cadaver was conducted, and the motions of six surgical instruments were measured in 36 cases of laparoscopic radical nephrectomy. Outlier removal and smoothing methods were also developed and applied to remove the noise and outliers in the obtained data. The questionnaire survey conducted during the experiment confirmed that the measurement system did not interfere with the surgical operation. Thus, the proposed system was capable of making reliable measurements with minimal impact on surgery. The system will facilitate surgical education by enabling the evaluation of skill transfer of surgical skills.

This article was updated to correct the labeling of Fig. 6.

In off-pump coronary artery bypass grafting (OPCAB), the coronary arteries are joined as the heart beats. This procedure requires high skill and experience to be performed reliably and quickly. Although training kits are commonly used for technical training, the inability of trainees to always be with experienced surgeons for guidance and to receive immediate evaluation remains problematic. To address this problem, a system that allows a single trainee to observe and quantitatively evaluate the procedures performed by an experienced surgeon is being developed. In this study, to analyze the differences between the motions of experienced and novice surgeons, Leap Motion was used to measure the hand motion of the vascular anastomosis performed by both surgeons using a training kit. Using the measured data, the features of the vascular anastomosis surgical techniques performed by experienced and novices were tested using the Mann–Whitney U test. In addition, a binary classification was performed using machine learning with these features. The binary classification results show that trainees can be classified as experts or novices with high accuracy using the developed system.

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.

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.

Although various biomimetic soft materials that display structural hierarchies and stimuli responsiveness have been developed from organic materials, the creation of their counterparts consisting entirely of inorganic materials presents an attractive challenge, as the properties of such materials generally differ from those of living organisms. Here, we have developed a hydrogel consisting of inorganic nanosheets (14 wt%) and water (86 wt%) that undergoes thermally induced reversible and abrupt changes in its internal structure and mechanical elasticity (23-fold). At room temperature, the nanosheets in water electrostatically repel one another and self-assemble into a long-periodic lamellar architecture with mutually restricted mobility, forming a physical hydrogel. Upon heating above 55 °C, the electrostatic repulsion is overcome by competing van der Waals attraction, and the nanosheets rearrange into an interconnected 3D network of another hydrogel. By doping the gel with a photothermal-conversion agent, the gel-to-gel transition becomes operable spatiotemporally on photoirradiation. Inorganic soft materials are an attractive concept but challenging to make. Here the authors have developed a hydrogel consisting of inorganic nanosheets (14 wt%) and water (86 wt%) that undergoes thermally induced reversible and abrupt changes in its internal structure and mechanical elasticity (23-fold).

Just like in living organisms, if precise coherent operation of tiny movable components is possible, one may generate a macroscopic mechanical motion. Here we report that ~10 10 pieces of colloidally dispersed nanosheets in aqueous media can be made to operate coherently to generate a propagating macroscopic wave under a non-equilibrium state. The nanosheets are initially forced to adopt a monodomain cofacial geometry with a large and uniform plane-to-plane distance of ~420 nm, where they are strongly correlated by competitive electrostatic repulsion and van der Waals attraction. When the electrostatic repulsion is progressively attenuated by the addition of ionic species, the nanosheets sequentially undergo coherent motions, generating a propagating wave. This elaborate wave in time and space can transport microparticles over a long distance in uniform direction and velocity. The present discovery may provide a general principle for the design of macroscopically movable devices from huge numbers of tiny components. Tiny movable components could generate macroscopic mechanical motion if precise coherent operation can be exerted simultaneously. Here, the authors demonstrate this by using 10^10 pieces of colloidally dispersed nanosheets to generate wave under non-equilibrium state.