LAPKaans: Tool-Motion Tracking and Gripping Force-Sensing Modular Smart Laparoscopic Training System

Surgical skill analysis focused on tissue traction in laparoscopic wet lab training

Background

Tissue handling is one of the pivotal parts of surgical procedures. We aimed to elucidate the characteristics of experts' left-hand during laparoscopic tissue dissection.

Methods

Participants performed tissue dissection around the porcine aorta. The grasping force/point of the grasping forceps were measured using custom-made sensor forceps, and the forceps location was also recorded by motion capture system (Mocap). According to the global operative assessment of laparoscopic skills (GOALS), two experts scored the recorded movies, and based on the mean scores, participants were divided into three groups: novice (<10), intermediate (10≤ to <20), and expert (≤20). Force-based metrics were compared among the three groups using the Kruskal-Wallis test. Principal component analysis (PCA) using significant metrics was also performed.

Results

A total of 42 trainings were successfully recorded. The statistical test revealed that novices frequently regrasped a tissue (median total number of grasps, novices: 268.0 times, intermediates: 89.5, experts: 52.0, p < 0.0001), the traction angle became stable against the aorta (median weighted standard deviation of traction angle, novices: 30.74°, intermediates: 26.80, experts: 23.75, p = 0.0285), and the grasping point moved away from the aorta according to skill competency [median percentage of grasping force applied in close zone (0 to 2.0 cm from aorta), novices: 34.96 %, intermediates: 21.61 %, experts: 10.91 %, p = 0.0032]. PCA showed that the efficiency-related (total number of grasps) and effective tissue traction-related (weighted average grasping position in Y-axis and distribution of grasping area) metrics mainly contributed to the skill difference (proportion of variance of first principal component: 60.83 %).

Conclusion

The present results revealed experts' left-hand characteristics, including correct tissue grasping, sufficient tissue traction from the aorta, and stable traction angle. Our next challenge is the provision of immediate and visual feedback onsite after the present wet-lab training, and shortening the learning curve of trainees.

An intelligent grasper to provide real-time force feedback to shorten the learning curve in laparoscopic training

BACKGROUND

A lack of force feedback in laparoscopic surgery often leads to a steep learning curve to the novices and traditional training system equipped with force feedback need a high educational cost. This study aimed to use a laparoscopic grasper providing force feedback in laparoscopic training which can assist in controlling of gripping forces and improve the learning processing of the novices.

METHODS

Firstly, we conducted a pre-experiment to verify the role of force feedback in gripping operations and establish the safe gripping force threshold for the tasks. Following this, we proceeded with a four-week training program. Unlike the novices without feedback (Group A2), the novices receiving feedback (Group B2) underwent training that included force feedback. Finally, we completed a follow-up period without providing force feedback to assess the training effect under different conditions. Real-time force parameters were recorded and compared.

RESULTS

In the pre-experiment, we set the gripping force threshold for the tasks based on the experienced surgeons' performance. This is reasonable as the experienced surgeons have obtained adequate skill of handling grasper. The thresholds for task 1, 2, and 3 were set as 0.731 N, 1.203 N and 0.938 N, respectively. With force feedback, the gripping force applied by the novices with feedback (Group B1) was lower than that of the novices without feedback (Group A1) (p < 0.005). During the training period, the Group B2 takes 6 trails to achieve gripping force of 0.635 N, which is lower than the threshold line, whereas the Group A2 needs 11 trails, meaning that the learning curve of Group B2 was significantly shorter than that of Group A2. Additionally, during the follow-up period, there was no significant decline in force learning, and Group B2 demonstrated better control of gripping operations. The training with force feedback received positive evaluations.

CONCLUSION

Our study shows that using a grasper providing force feedback in laparoscopic training can help to control the gripping force and shorten the learning curve. It is anticipated that the laparoscopic grasper equipped with FBG sensor is promising to provide force feedback during laparoscopic training, which ultimately shows great potential in laparoscopic surgery.

Clinical applications of robotic surgery platforms: a comprehensive review

Robotic surgery has expanded globally across various medical specialties since its inception more than 20 years ago. Accompanying this expansion were significant technological improvements, providing tremendous benefits to patients and allowing the surgeon to perform with more precision and accuracy. This review lists some of the different types of platforms available for use in various clinical applications. We performed a literature review of PubMed and Web of Science databases in May 2023, searching for all available articles describing surgical robotic platforms from January 2000 (the year of the first approved surgical robot, da Vinci® System, by Intuitive Surgical) until May 1st, 2023. All retrieved robotic platforms were then divided according to their clinical application into four distinct groups: soft tissue robotic platforms, orthopedic robotic platforms, neurosurgery and spine platforms, and endoluminal robotic platforms. Robotic surgical technology has undergone a rapid expansion over the last few years. Currently, multiple robotic platforms with specialty-specific applications are entering the market. Many of the fields of surgery are now embracing robotic surgical technology. We review some of the most important systems in clinical practice at this time.

Comparative evaluation of three commercially available markerless depth sensors for close-range use in surgical simulation

PURPOSE

Minimally invasive surgeries have restricted surgical ports, demanding a high skill level from the surgeon. Surgical simulation potentially reduces this steep learning curve and additionally provides quantitative feedback. Markerless depth sensors show great promise for quantification, but most such sensors are not designed for accurate reconstruction of complex anatomical forms in close-range.

METHODS

This work compares three commercially available depth sensors, namely the Intel D405, D415, and the Stereolabs Zed-Mini in the range of 12-20 cm, for use in surgical simulation. Three environments are designed that closely mimic surgical simulation, comprising planar surfaces, rigid objects, and mitral valve models of silicone and realistic porcine tissue. The cameras are evaluated on Z-accuracy, temporal noise, fill rate, checker distance, point cloud comparisons, and visual inspection of surgical scenes, across several camera settings.

RESULTS

The Intel cameras show sub-mm accuracy in most static environments. The D415 fails in reconstructing valve models, while the Zed-Mini provides lesser temporal noise and higher fill rate. The D405 could reconstruct anatomical structures like the mitral valve leaflet and a ring prosthesis, but performs poorly for reflective surfaces like surgical tools and thin structures like sutures.

CONCLUSION

If a high temporal resolution is needed and lower spatial resolution is acceptable, the Zed-Mini is the best choice, whereas the Intel D405 is the most suited for close-range applications. The D405 shows potential for applications like deformable registration of surfaces, but is not yet suitable for applications like real-time tool tracking or surgical skill assessment.

Towards next generation digital twin in robotics: Trends, scopes, challenges, and future

With the advent of Industry 4.0, several cutting-edge technologies such as cyber-physical systems, digital twins, IoT, robots, big data, cloud computation have emerged. However, how these technologies are interconnected or fused for collaborative and increased functionality is what elevates 4.0 to a grand scale. Among these fusions, the digital twin (DT) in robotics is relatively new but has unrivaled possibilities. In order to move forward with DT-integrated robotics research, a complete evaluation of the literature and the creation of a framework are now required. Given the importance of this research, the paper seeks to explore the trends of DT incorporated robotics in both high and low research saturated robotic domains in order to discover the gap, rising and dying trends, potential scopes, challenges, and viable solutions. Finally, considering the findings, the study proposes a framework based on a hypothesis for the future paradigm of DT incorporated robotics.

Fuzzy logic supervisor –A surgical skills assessment system using multi-class detection of laparoscopic box-trainer instruments

Recent developments in deep learning can be used in skill assessments for laparoscopic surgeons. In Minimally Invasive Surgery (MIS), surgeons should acquire many skills before carrying out a real operation. The Laparoscopic Surgical Box-Trainer allows surgery residents to train on specific skills that are not traditionally taught to them. This study aims to automatically detect the tips of laparoscopic instruments, localize a point, evaluate the detection accuracy to provide valuable assessment and expedite the development of surgery skills and assess the trainees’ performance using a Multi-Input-Single-Output Fuzzy Logic Supervisor system. The output of the fuzzy logic assessment is the performance evaluation for the surgeon, and it is quantified in percentages. Based on the experimental results, the trained SSD Mobilenet V2 FPN can identify each instrument at a score of 70% fidelity. On the other hand, the trained SSD ResNet50 V1 FPN can detect each instrument at the score of 90% fidelity, in each location within a region of interest, and determine their relative distance with over 65% and 80% reliability, respectively. This method can be applied in different types of laparoscopic tooltip detection. Because there were a few instances when the detection failed, and the system was designed to generate pass-fail assessment, we recommend improving the measurement algorithm and the performance assessment by adding a camera to the system and measuring the distance from multiple perspectives.