Description
Purpose Virtual arthroscopic training has become increasingly popular. However, there is a lack of efficiency-based tracking
of the trainee, which may be critical for determining the specifics of training programs and adapting them for the needs of
each trainee. This study aims to evaluate and compare the measures obtained with a non-invasive neurophysiological method
with The Diagnostic Arthroscopy Skill Score (DASS), a commonly used assessment tool for evaluating arthroscopic skills.
Methods The study collected simulator performance scores, consisting of “Triangulation Right Hand”, “Triangulation Left
Hand”, “Catch the Stars” and “Three Rings” and DASS scores from 22 participants (11 novices, 11 experts). These scores
were obtained while participants underwent a structured program of exercises for the fundamentals of arthroscopic surgery
training (FAST) and knee module using a simulator-based arthroscopy device. During the evaluation, data on oxy-hemoglobin
and deoxy-hemoglobin levels in the prefrontal cortex were collected using the Functional Near-Infrared Spectroscopy (fNIRS)
imaging system. Performance scores, DASS scores, and fNIRS data were subsequently analyzed to determine any correlation
between performance and cortex activity.
Results The simulator performance scores and the DASSPart2
scores were significantly higher in the expert group compared
to the novice group (200.1 ± 28.5 vs 172.5 ± 48.9, p = 0.04 and 9.4 ± 5.6 vs. 5.4 ± 5.6 p = 0.02). In the expert group, fNIRS
data showed a significantly lower prefrontal cortex activation during fundamental tasks in the FAST module, indicating
significantly more efficient mental resource use.
Conclusion The analysis of cognitive workload changes during simulation-based arthroscopy training revealed a significant
correlation between the trainees’ DASS scores and fNIRS data. This correlation suggests the potential use of fNIRS data
and DASS scores as additional metrics to create adaptive training protocols for each participant. By incorporating these
metrics, the training process can be optimized, leading to more efficient arthroscopic training and better preparedness for
clinical operations.
