Assessment of joystick and wrist control in hand-held articulated laparoscopic prototypes

Various steerable instruments with flexible distal tip have been developed for laparoscopic surgery. The problem of steering such instruments, however, remains a challenge, because no study investigated which control method is the most suitable. This study was designed to examine whether thumb (joystick) or wrist control method is designated for prototypes of steerable instruments by means of motion analysis. Methods: Five experts and 12 novices participated. Each participant performed a needle-driving task in three directions with two prototypes (wrist and thumb) and a conventional instrument. Novices performed the tasks in three sessions, whereas experts performed one session only. The order of performing the tasks was determined by Latin squares design. Assessment of performance was done by means of five motion analysis parameters, a newly developed matrix for assigning penalty points, and a questionnaire. Results: The thumb-controlled prototype outperformed the wrist-controlled prototype. Comparison of the results obtained in each task showed that regarding penalty points, the up ? down task was the most difficult to perform. Conclusions: The thumb control is more suitable for steerable instruments than the wrist control. To avoid uncontrolled movements and difficulties with applying forces to the tissue while keeping the tip of the instrument at the constant angle, adding a ‘‘locking’’ feature is necessary. It is advisable not to perform the needle driving task in the up down directionBiomechanical EngineeringMechanical, Maritime and Materials Engineerin

Retracting and seeking movements during laparoscopic goal-oriented movements. Is the shortest path length optimal?

Aims- Minimally invasive surgery (MIS) requires a high degree of eye–hand coordination from the surgeon. To facilitate the learning process, objective assessment systems based on analysis of the instruments’ motion are being developed. To investigate the influence of performance on motion characteristics, we examined goaloriented movements in a box trainer. In general, goal-oriented movements consist of a retracting and a seeking phase, and are, however, not performed via the shortest path length. Therefore, we hypothesized that the shortest path is not an optimal concept in MIS. Methods-Participants were divided into three groups (experts, residents, and novices). Each participant performed a number of one-hand positioning tasks in a box trainer. Movements of the instrument were recorded with the TrEndo tracking system. The movement from point A to B was divided into two phases: A-M (retracting) and M-B (seeking). Normalized path lengths (given in %) of the two phases were compared. Results- Thirty eight participants contributed. For the retracting phase, we found no significant difference between experts [median (range) %: 152 (129–178)], residents [164 (126–250)], and novices [168 (136–268)]. In the seeking phase, we find a significant difference (<0.001) between experts [180 (172–247)], residents [201 (163–287)], and novices [290 (244–469)]. Moreover, within each group, a significant difference between retracting and seeking phases was observed. Conclusions- Goal-oriented movements in MIS can be split into two phases: retracting and seeking. Novices are less effective than experts and residents in the seeking phase. Therefore, the seeking phase is characteristic of performance differences. Furthermore, the retracting phase is essential, because it improves safety by avoiding intermediate tissue contact. Therefore, the shortest path length, as presently used during the assessment of basic MIS skills, may be not a proper concept for analyzing optimal movements and, therefore, needs to be revised.Biomechanical EngineeringMechanical, Maritime and Materials Engineerin

EVA: Laparoscopic instrument tracking based on endoscopic video analysis for psychomotor skills assessment

INTRODUCTION: The EVA (Endoscopic Video Analysis) tracking system a new tracking system for extracting motions of laparoscopic instruments based on non-obtrusive video tracking was developed. The feasibility of using EVA in laparoscopic settings has been tested in a box trainer setup. METHODS: EVA makes use of an algorithm that employs information of the laparoscopic instrument's shaft edges in the image, the instrument's insertion point, and the camera's optical centre to track the 3D position of the instrument tip. A validation study of EVA comprised a comparison of the measurements achieved with EVA and the TrEndo tracking system. To this end, 42 participants (16 novices, 22 residents, and 4 experts) were asked to perform a peg transfer task in a box trainer. Ten motion-based metrics were used to assess their performance. RESULTS: Construct validation of the EVA has been obtained for seven motion-based metrics. Concurrent validation revealed that there is a strong correlation between the results obtained by EVA and the TrEndo for metrics such as path length (p=0,97), average speed (p=0,94) or economy of volume (p=0,85), proving the viability of EVA. CONCLUSIONS: EVA has been successfully used in the training setup showing potential of endoscopic video analysis to assess laparoscopic psychomotor skills. The results encourage further implementation of video tracking in training setups and in image guided surgery

Laparoscopic Video Analysis for Training and Image Guided Surgery

Automatic analysis of Minimally Invasive Surgical video has the potential to drive new solutions for alleviating needs of safe and reproducible training programs, objective and transparent evaluation systems and navigation tools to assist surgeons and improve patient safety. Surgical video is an always available source of information, which can be used without any additional intrusive hardware in the operating room. This paper is focused on surgical video analysis methods and techniques. It describes authors' contributions in two key aspects, the 3D reconstruction of the surgical field and the segmentation and tracking of tools and organs based on laparoscopic video images. Results are given to illustrate the potential of this field of research, like the calculi of the 3D position and orientation of a tool from its 2D image, or the translation of a preoperative resection plan into a hepatectomy surgical procedure using the shading information of the image. Research efforts are required to further develop these technologies in order to harness all the valuable information available in any video-based surgery

Learning curves of basic laparoscopic psychomotor skills in SINERGIA VR simulator

Purpose: Surgical simulators are currently essential within any laparoscopic training program because they provide a low-stakes, reproducible and reliable environment to acquire basic skills. The purpose of this study is to determine the training learning curve based on different metrics corresponding to five tasks included in SINERGIA laparoscopic virtual reality simulator. Methods: Thirty medical students without surgical experience participated in the study. Five tasks of SINERGIA were included: Coordination, Navigation, Navigation and touch, Accurate grasping and Coordinated pulling. Each participant was trained in SINERGIA. This training consisted of eight sessions (R1–R8) of the five mentioned tasks and was carried out in two consecutive days with four sessions per day. A statistical analysis was made, and the results of R1, R4 and R8 were pair-wise compared with Wilcoxon signed-rank test. Significance is considered at P value <0.005. Results: In total, 84.38% of the metrics provided by SINERGIA and included in this study show significant differences when comparing R1 and R8. Metrics are mostly improved in the first session of training (75.00% when R1 and R4 are compared vs. 37.50% when R4 and R8 are compared). In tasks Coordination and Navigation and touch, all metrics are improved. On the other hand, Navigation just improves 60% of the analyzed metrics. Most learning curves show an improvement with better results in the fulfillment of the different tasks. Conclusions: Learning curves of metrics that assess the basic psychomotor laparoscopic skills acquired in SINERGIA virtual reality simulator show a faster learning rate during the first part of the training. Nevertheless, eight repetitions of the tasks are not enough to acquire all psychomotor skills that can be trained in SINERGIA. Therefore, and based on these results together with previous works, SINERGIA could be used as training tool with a properly designed training program

Complex Product Architecture Analysis using an Integrated Approach

yesProduct design decomposition and synthesis is a constant challenge with its continuously increasing complexity at each level of abstraction. Currently, design decomposition and synthesis analytical tasks are mostly accomplished via functional and structural methods. These methods are useful in different phases of design process for product definition and architecture but limited in a way that they tend to focus more on ‘what’ and less on ‘how’ and vice versa. This paper combines a functional representation tool known as System State Flow Diagram (a solution independent approach), a solution search tool referred as Morphology Table, and Design Structure Matrix (mainly a solution dependent tool). The proposed approach incorporates Multiple Domain Matrix (MDM) to integrate the knowledge of both solution independent and dependent analyses. The approach is illustrated with a case study of solar robot toy, followed by its limitations, future work and discussion

Complex Product Architecture Analysis using an Integrated Approach

yesProduct design decomposition and synthesis is a constant challenge with its continuously increasing complexity at each level of abstraction. Currently, design decomposition and synthesis analytical tasks are mostly accomplished via functional and structural methods. These methods are useful in different phases of design process for product definition and architecture but limited in a way that they tend to focus more on ‘what’ and less on ‘how’ and vice versa. This paper combines a functional representation tool known as System State Flow Diagram (a solution independent approach), a solution search tool referred as Morphology Table, and Design Structure Matrix (mainly a solution dependent tool). The proposed approach incorporates Multiple Domain Matrix (MDM) to integrate the knowledge of both solution independent and dependent analyses. The approach is illustrated with a case study of solar robot toy, followed by its limitations, future work and discussion