Correlating Personal Resourcefulness and Psychomotor Skills: An Analysis of Stress, Visual Attention and Technical Metrics

Correlation Study and Predictive Modelling of Ergonomic Parameters in Robotic-Assisted Laparoscopic Surgery

BACKGROUND

This study aims to continue research on the objective analysis of ergonomic conditions in robotic-assisted surgery (RAS), seeking innovative solutions for the analysis and prevention of ergonomic problems in surgical practice.

METHODS

Four different robotic-assisted tasks were performed by groups of surgeons with different surgical experiences. Different wearable technologies were used to record surgeons' posture and muscle activity during surgical practice, for which the correlation between them was analyzed. A predictive model was generated for each task based on the surgeons' level of experience and type of surgery. Two preprocessing techniques (scaling and normalization) and two artificial intelligence techniques were tested.

RESULTS

Overall, a positive correlation between prolonged maintenance of an ergonomically inadequate posture during RAS and increased accumulated muscle activation was found. Novice surgeons showed improved posture when performing RAS compared to expert surgeons. The predictive model obtained high accuracy for cutting, peg transfer, and labyrinth tasks.

CONCLUSIONS

This study expands on the existing ergonomic analysis of the lead surgeon during RAS and develops predictive models for future prevention of ergonomic risk situations. Both posture and muscle loading are highly related to the surgeon's previous experience.

Applying artificial intelligence on EDA sensor data to predict stress on minimally invasive robotic-assisted surgery

PURPOSE

This study aims predicting the stress level based on the ergonomic (kinematic) and physiological (electrodermal activity-EDA, blood pressure and body temperature) parameters of the surgeon from their records collected in the previously immediate situation of a minimally invasive robotic surgery activity.

METHODS

For this purpose, data related to the surgeon's ergonomic and physiological parameters were collected during twenty-six robotic-assisted surgical sessions completed by eleven surgeons with different experience levels. Once the dataset was generated, two preprocessing techniques were applied (scaled and normalized), these two datasets were divided into two subsets: with 80% of data for training and cross-validation, and 20% of data for test. Three predictive techniques (multiple linear regression-MLR, support vector machine-SVM and multilayer perceptron-MLP) were applied on training dataset to generate predictive models. Finally, these models were validated on cross-validation and test datasets. After each session, surgeons were asked to complete a survey of their feeling of stress. These data were compared with those obtained using predictive models.

RESULTS

The results showed that MLR combined with the scaled preprocessing achieved the highest R2 coefficient and the lowest error for each parameter analyzed. Additionally, the results for the surgeons' surveys were highly correlated to the results obtained by the predictive models (R2 = 0.8253).

CONCLUSIONS

The linear models proposed in this study were successfully validated on cross-validation and test datasets. This fact demonstrates the possibility of predicting factors that help us to improve the surgeon's health during robotic surgery.

Comparative Study of Ergonomics in Conventional and Robotic-Assisted Laparoscopic Surgery

BACKGROUND

This study aims to implement a set of wearable technologies to record and analyze the surgeon's physiological and ergonomic parameters during the performance of conventional and robotic-assisted laparoscopic surgery, comparing the ergonomics and stress levels of surgeons during surgical procedures.

METHODS

This study was organized in two different settings: simulator tasks and experimental model surgical procedures. The participating surgeons performed the tasks and surgical procedures in both laparoscopic and robotic-assisted surgery in a randomized fashion. Different wearable technologies were used to record the surgeons' posture, muscle activity, electrodermal activity and electrocardiography signal during the surgical practice.

RESULTS

The simulator study involved six surgeons: three experienced (>100 laparoscopic procedures performed; 36.33 ± 13.65 years old) and three novices (<100 laparoscopic procedures; 29.33 ± 8.39 years old). Three surgeons of different surgical specialties with experience in laparoscopic surgery (>100 laparoscopic procedures performed; 37.00 ± 5.29 years old), but without experience in surgical robotics, participated in the experimental model study. The participating surgeons showed an increased level of stress during the robotic-assisted surgical procedures. Overall, improved surgeon posture was obtained during robotic-assisted surgery, with a reduction in localized muscle fatigue.

CONCLUSIONS

A set of wearable technologies was implemented to measure and analyze surgeon physiological and ergonomic parameters. Robotic-assisted procedures showed better ergonomic outcomes for the surgeon compared to conventional laparoscopic surgery. Ergonomic analysis allows us to optimize surgeon performance and improve surgical training.

An objective skill assessment framework for microsurgical anastomosis based on ALI scores

INTRODUCTION

The current assessment and standardization of microsurgical skills are subjective, posing challenges in reliable skill evaluation. We aim to address these limitations by developing a quantitative and objective framework for accurately assessing and enhancing microsurgical anastomosis skills among surgical trainees. We hypothesize that this framework can differentiate the proficiency levels of microsurgeons, aligning with subjective assessments based on the ALI score.

METHODS

We select relevant performance metrics from the literature on laparoscopic skill assessment and human motor control studies, focusing on time, instrument kinematics, and tactile information. This information is measured and estimated by a set of sensors, including cameras, a motion capture system, and tactile sensors. The recorded data is analyzed offline using our proposed evaluation framework. Our study involves 12 participants of different ages ([Formula: see text] years) and genders (nine males and three females), including six novice and six intermediate subjects, who perform surgical anastomosis procedures on a chicken leg model.

RESULTS

We show that the proposed set of objective and quantitative metrics to assess skill proficiency aligns with subjective evaluations, particularly the ALI score method, and can effectively differentiate novices from more proficient microsurgeons. Furthermore, we find statistically significant disparities, where microsurgeons with intermediate level of skill proficiency surpassed novices in both task speed, reduced idle time, and smoother, briefer hand displacements.

CONCLUSION

The framework enables accurate skill assessment and provides objective feedback for improving microsurgical anastomosis skills among surgical trainees. By overcoming the subjectivity and limitations of current assessment methods, our approach contributes to the advancement of surgical education and the development of aspiring microsurgeons. Furthermore, our framework emerges to precisely distinguish and classify proficiency levels (novice and intermediate) exhibited by microsurgeons.