3D Autonomous Surgeon's Hand Movement Assessment Using a Cascaded Fuzzy Supervisor in Multi-Thread Video Processing

Advances in gynecologic simulation: implementation, validity, and new resources

PURPOSE OF REVIEW

Simulation plays an integral role in gynecological surgical training that will continue and grow in the future. It aims to supplement surgical training as residents are experiencing decreased surgical volumes. It has also been recognized as a means for assessing a trainee's capabilities to the point of being incorporated into the board certification process. This review will highlight the recent updates in research and resources available for gynecologic surgery simulation.

RECENT FINDINGS

The literature reviewed demonstrated that, across the board, participants in simulation find it beneficial. A variety of low-fidelity and high-fidelity models, virtual reality programs, and artificial intelligence technology have been developed recently. As the field advances, researchers have started to investigate methods to increased engagement and provide feedback without the need for direct observation, the logical next step considerations to maximize the impact and availability of simulation.

SUMMARY

Gynecologic surgery training programs must stay abreast of the wide variety of simulation training available. Efforts should continue to be made to investigate the impact of simulation patient outcomes. The academic medicine community has a responsibility to ensure high-quality simulation is available to all surgical trainees to avoid disparities between low-resource and high-resource programs.

Automatic assessment of performance in the FLS trainer using computer vision

BACKGROUND

Fundamentals of Laparoscopic Surgery (FLS) box trainer is a well-accepted method for training and evaluating laparoscopic skills. It mandates an observer that will measure and evaluate the trainee's performance. Measuring performance in the Peg Transfer task includes time and penalty for dropping pegs. This study aimed to assess whether computer vision (CV) may be used to automatically measure performance in the FLS box trainer.

METHODS

Four groups of metrics were defined and measured automatically using CV. Validity was assessed by dividing participants to 3 groups of experience levels. Twenty-seven participants were recorded performing the Peg Transfer task 2-4 times, amounting to 72 videos. Frames were sampled from the videos and labeled to create an image dataset. Using these images, we trained a deep neural network (YOLOv4) to detect the different objects in the video. We developed an evaluation system that tracks the transfer of the triangles and produces a feedback report with the metrics being the main criteria. The metric groups were Time, Grasper Movement Speed, Path Efficiency, and Grasper Coordination. The performance was compared based on their last video (3 participants were excluded due to technical issues).

RESULTS

The ANOVA tests show that for all metrics except one, the variance in performance can be explained by the experience level of participants. Senior surgeons and residents significantly outperform students and interns on almost every metric. Senior surgeons usually outperform residents, but the gap is not always significant.

CONCLUSION

The statistical analysis shows that the metrics can differentiate between the experts and novices performing the task in several aspects. Thus, they may provide a more detailed performance analysis than is currently used. Moreover, these metrics calculation is automatic and relies solely on the video camera of the FLS trainer. As a result, they allow independent training and assessment.