Development and face validation of a virtual camera navigation task trainer

Haptic based fundamentals of laparoscopic surgery simulation for training with objective assessments

Force is crucial for learning psychomotor skills in laparoscopic tissue manipulation. Fundamental laparoscopic surgery (FLS), on the other hand, only measures time and position accuracy. FLS is a commonly used training program for basic laparoscopic training through part tasks. The FLS is employed in most of the laparoscopic training systems, including box trainers and virtual reality (VR) simulators. However, many laparoscopic VR simulators lack force feedback and measure tissue damage solely through visual feedback based on virtual collisions. Few VR simulators that provide force feedback have subjective force metrics. To provide an objective force assessment for haptic skills training in the VR simulators, we extend the FLS part tasks to haptic-based FLS (HFLS), focusing on controlled force exertion. We interface the simulated HFLS part tasks with a customized bi-manual haptic simulator that offers five degrees of freedom (DOF) for force feedback. The proposed tasks are evaluated through face and content validity among laparoscopic surgeons of varying experience levels. The results show that trainees perform better in HFLS tasks. The average Likert score observed for face and content validity is greater than 4.6 ± 0.3 and 4 ± 0.5 for all the part tasks, which indicates the acceptance of the simulator among subjects for its appearance and functionality. Face and content validations show the need to improve haptic realism, which is also observed in existing simulators. To enhance the accuracy of force rendering, we incorporated a laparoscopic tool force model into the simulation. We study the effectiveness of the model through a psychophysical study that measures just noticeable difference (JND) for the laparoscopic gripping task. The study reveals an insignificant decrease in gripping-force JND. A simple linear model could be sufficient for gripper force feedback, and a non-linear LapTool force model does not affect the force perception for the force range of 0.5-2.5 N. Further study is required to understand the usability of the force model in laparoscopic training at a higher force range. Additionally, the construct validity of HFLS will confirm the applicability of the developed simulator to train surgeons with different levels of experience.

Detection of adverse events leading to inadvertent injury during laparoscopic cholecystectomy using convolutional neural networks

Uncontrolled movements of laparoscopic instruments can lead to inadvertent injury of adjacent structures. The risk becomes evident when the dissecting instrument is located outside the field of view of the laparoscopic camera. Technical solutions to ensure patient safety are appreciated. The present work evaluated the feasibility of an automated binary classification of laparoscopic image data using Convolutional Neural Networks (CNN) to determine whether the dissecting instrument is located within the laparoscopic image section. A unique record of images was generated from six laparoscopic cholecystectomies in a surgical training environment to configure and train the CNN. By using a temporary version of the neural network, the annotation of the training image files could be automated and accelerated. A combination of oversampling and selective data augmentation was used to enlarge the fully labeled image data set and prevent loss of accuracy due to imbalanced class volumes. Subsequently the same approach was applied to the comprehensive, fully annotated Cholec80 database. The described process led to the generation of extensive and balanced training image data sets. The performance of the CNN-based binary classifiers was evaluated on separate test records from both databases. On our recorded data, an accuracy of 0.88 with regard to the safety-relevant classification was achieved. The subsequent evaluation on the Cholec80 data set yielded an accuracy of 0.84. The presented results demonstrate the feasibility of a binary classification of laparoscopic image data for the detection of adverse events in a surgical training environment using a specifically configured CNN architecture.

Quality-based assessment of camera navigation skills for laparoscopic fundoplication

Laparoscopic fundoplication is considered the gold standard surgical procedure for the treatment of symptomatic hiatus hernia. Studies on surgical performance in minimally invasive hiatus hernia repair have neglected the role of the camera assistant so far. The current study was designed to assess the applicability of the structured assessment of laparoscopic assistance skills (SALAS) score to laparoscopic fundoplication as an advanced and commonly performed laparoscopic upper GI procedure. Randomly selected laparoscopic fundoplications (n = 20) at a single institute were evaluated. Four trained reviewers independently assigned SALAS scoring based on synchronized video and voice recordings. The SALAS score (5-25 points) consists of five key aspects of laparoscopic camera navigation as previously described. Experience in camera assistance was defined as at least 100 assistances in complex laparoscopic procedures. Nine different surgical teams, consisting of five surgical residents, three fellows, and two attending physicians, were included. Experienced and inexperienced camera assistants were equally distributed (10/10). Construct validity was proven with a significant discrimination between experienced and inexperienced camera assistants for all reviewers (P < 0.05). The intraclass correlation coefficient of 0.897 demonstrates the score's low interrater variability. The total operation time decreases with increasing SALAS score, not reaching statistical significance. The applied SALAS score proves effective by discriminating between experienced and inexperienced camera assistants in an upper GI surgical procedure. This study demonstrates the applicability of the SALAS score to a more advanced laparoscopic procedure such as fundoplication enabling future investigations on the influence of camera navigation on surgical performance and operative outcome.

Rating of camera navigation skills in colorectal surgery

PURPOSE

In advanced minimally invasive surgery the laparoscopic camera navigation (LCN) quality can influence the flow of the operation. This study aimed to investigate the applicability of a scoring system for LCN (SALAS score) in colorectal surgery and whether an adequate scoring can be achieved using a specified sequence of the operation.

METHODS

The score was assessed by four blinded raters using synchronized video and voice recordings of 20 randomly selected laparoscopic colorectal surgeries (group A: assessment of the entire operation; group B: assessment of the 2nd and 3rd quartile). Experience in LCN was defined as at least 100 assistances in complex laparoscopic procedures.

RESULTS

The surgical teams consisted of three residents, three fellows, and two attendings forming 15 different teams. The ratio between experienced and inexperienced camera assistants was balanced (n = 11 vs. n = 9). Regarding the total SALAS score, the four raters discriminated between experienced and inexperienced camera assistants, regardless of their group assignment (group A, p < 0.05; group B, p < 0.05). The score's interrater variability and reliability were proven with an intraclass correlation coefficient of 0.88. No statistically relevant correlation was achieved between operation time and SALAS score.

CONCLUSION

This study presents the first intraoperative, objective, and structured assessment of LCN in colorectal surgery. We could demonstrate that the SALAS score is a reliable tool for the assessment of LCN even when only the middle part (50%) of the procedure is analyzed. Construct validity was proven by discriminating between experienced and inexperienced camera assistants.

Advancing Simulation-Based Orthopaedic Surgical Skills Training: An Analysis of the Challenges to Implementation

Simulation-based surgical skills training is recognized as a valuable method to improve trainees' performance and broadly perceived as essential for the establishment of a comprehensive curriculum in surgical education. However, there needs to be improvement in several areas for meaningful integration of simulation into surgical education. The purpose of this focused review is to summarize the obstacles to a comprehensive integration of simulation-based surgical skills training into surgical education and board certification and suggest potential solutions for those obstacles. First and foremost, validated simulators need to be rigorously assessed to ensure their feasibility and cost-effectiveness. All simulation-based courses should include clear objectives and outcome measures (with metrics) for the skills to be practiced by trainees. Furthermore, these courses should address a wide range of issues, including assessment of trainees' problem-solving and decision-making abilities and remediation of poor performance. Finally, which simulation-based surgical skills courses will become a standard part of the curriculum across training programs and which will be of value in board certification should be precisely defined. Sufficient progress in these areas will prevent excessive development of training and assessment tools with duplicative effort and large variability in quality.