Robot-assisted ex vivo neobladder reconstruction: preliminary results of surgical skill evaluation

PURPOSE: Advanced developments in the medical field have gradually increased the public demand for surgical skill evaluation. However, this assessment always depends on the direct observation of experienced surgeons, which is time-consuming and variable. The introduction of robot-assisted surgery provides a new possibility for this evaluation paradigm. This paper aims at evaluating surgeon performance automatically with novel evaluation metrics based on different surgical data. METHODS: Urologists ([Formula: see text] ) from a hospital were requested to perform a simplified neobladder reconstruction on an ex vivo setup twice with different camera modalities ([Formula: see text] ) randomly. They were divided into novices and experts ([Formula: see text] , respectively) according to their experience in robot-assisted surgeries. Different performance metrics ([Formula: see text] ) are proposed to achieve the surgical skill evaluation, considering both instruments and endoscope. Also, nonparametric tests are adopted to check if there are significant differences when evaluating surgeons performance. RESULTS: When grouping according to four stages of neobladder reconstruction, statistically significant differences can be appreciated in phase 1 ([Formula: see text] ) and phase 2 ([Formula: see text] ) with normalized time-related metrics and camera movement-related metrics, respectively. On the other hand, considering experience grouping shows that both metrics are able to highlight statistically significant differences between novice and expert performances in the control protocol. It also shows that the camera-related performance of experts is significantly different ([Formula: see text] ) when handling the endoscope manually and when it is automatic. CONCLUSION: Surgical skill evaluation, using the approach in this paper, can effectively measure surgical procedures of surgeons with different experience. Preliminary results demonstrate that different surgical data can be fully utilized to improve the reliability of surgical evaluation. It also demonstrates its versatility and potential in the quantitative assessment of various surgical operations.