Mental workload during endoscopic sinus surgery is associated with surgeons' skill levels.

Introduction: Surgeons' mental workload during endoscopic sinus surgery (ESS) has not been fully evaluated. The assessment was challenging due to the great diversity of each patient's anatomy and the consequence variety of surgical difficulties. In this study, we examined the mental workload of surgeons with various surgical skill levels during ESS under the standardized condition provided by novel-designed 3D sinus models.

Automatic assessment of laparoscopic surgical skill competence based on motion metrics.

The purpose of this study was to characterize the motion features of surgical devices associated with laparoscopic surgical competency and build an automatic skill-credential system in porcine cadaver organ simulation training. Participants performed tissue dissection around the aorta, dividing vascular pedicles after applying Hem-o-lok (tissue dissection task) and parenchymal closure of the kidney (suturing task). Movements of surgical devices were tracked by a motion capture (Mocap) system, and Mocap-metrics were compared according to the level of surgical experience (experts: ≥50 laparoscopic surgeries, intermediates: 10-49, novices: 0-9), using the Kruskal-Wallis test and principal component analysis (PCA).

Repetitive simulation training with novel 3D-printed sinus models for functional endoscopic sinus surgeries.

Background: The purpose of this study was to find a utility of a newly developed 3D-printed sinus model and to evaluate the educational benefit of simulation training with the models for functional endoscopic sinus surgery (FESS).

Material And Methods: Forty-seven otolaryngologists were categorized as experts (board-certified physicians with ≥200 experiences of FESS,  = 9), intermediates (board-certified physicians with <200 experiences of FESS,  = 19), and novices (registrars,  = 19). They performed FESS simulation training on 3D-printed models manufactured from DICOM images of computed tomography (CT) scan of real patients.

Objective evaluation of laparoscopic surgical skills in wet lab training based on motion analysis and machine learning.

Background: Our aim was to build a skill assessment system, providing objective feedback to trainees based on the motion metrics of laparoscopic surgical instruments.

Methods: Participants performed tissue dissection around the aorta (tissue dissection task) and renal parenchymal closure (parenchymal-suturing task), using swine organs in a box trainer under a motion capture (Mocap) system. Two experts assessed the recorded movies, according to the formula of global operative assessment of laparoscopic skills (GOALS: score range, 5-25), and the mean scores were utilized as objective variables in the regression analyses.