Page 111 - Read Online

Page 167 Choksi et al. Art Int Surg. 2025;5:160-9 https://dx.doi.org/10.20517/ais.2024.84

Figure 4. Confusion matrix for surgeon proficiency prediction based on 27 participants. The X-axis shows predicted labels and the Y-
axis shows true labels.

increased sample size in the future will help us increase the accuracy of our model. However, this study is a
first step to creating an automatic assessment and training tool for surgical trainees that does not require a
large time burden for expert surgeons.

With its continued prevalence and popularity, robotic surgery needs a standardized curriculum and
assessment for training. Specifically, an objective evaluation method with a limited time burden on expert
surgeons is a crucial need in robotic training. This study provides a clinically relevant proposal to improve
robotic surgical education. This dry lab model proposes a standardized training tool for suturing tasks
utilizing CV for automatic assessment. This relieves the time burden on surgical experts of video-based
assessment and removes the subjectivity of an individual expert. It also allows for standardized feedback
based on needle movements. This model can be utilized in the future to ensure trainees are at an adequate
level before operating on patients to improve surgical safety.