Transfer of skills between laparoscopic and robot-assisted surgery: a systematic review

Skill assessment based on clutch use in cross-platform robot-assisted surgery

BACKGROUND

Many studies have investigated the transfer of skills between laparoscopic and robot-assisted surgery (RAS). These studies have considered time, error, and clinical outcomes in the assessment of skill transfer. However, little is known about the specific operations of the surgeon. Clutch control use is an important skill in RAS. Therefore, the present study aimed to propose a novel objective algorithm based on computer vision that can automatically evaluate a surgeon's clutch use. Additionally, the study aimed to evaluate the correlation between clutch metrics and surgical skill on different surgical robot platforms.

METHODS

The robotic surgery training center of Wuhan University trained 30 laparoscopic surgeons as the study group between 2023 and 2024. Laparoscopic surgeons were trained by combining robotic simulator exercises and RAS animal experiments. During the training, video and hand movement data were collected. Hand movements identified by a skin-color model were combined with labeling information to classify clutch use. The metrics were validated on different robotic platforms (dv-Trainer, EDGE MP1000, Toumai™ MT1000, and DaVinci Xi system) and among surgeons with different surgical skill levels.

RESULTS

On the robotic simulator, clutch accuracy in the expert group was significantly higher than in the study group for all tasks. No significant differences were observed in the number of clutches between the expert and study groups. In the RAS experiment, the number of clutches decreased significantly for both study and expert groups. The accuracy was maintained at a high level in the expert group but decreased rapidly in the study group.

CONCLUSIONS

We proposed a new objective assessment of surgical skills, clutch use metrics, in cross-platform RAS. Additionally, we verified that the metrics significantly correlated with the surgical skill levels of the surgeons.

Skill Transfer from Laparoscopic Partial Nephrectomy to the Hugo™ RAS System: A Novel Proficiency Score to Assess Surgical Quality during the Learning Curve

Background/Objectives: The absence of validated tools to assess the skill transfer from laparoscopy to robotic surgery remains an unsolved issue in the context of robot-assisted partial nephrectomy (RAPN). We aimed to describe and validate a novel proficiency score to critically evaluate the surgical quality of RAPN with the Hugo™ RAS System (Medtronic, Minneapolis, MN, USA). Methods: Between October 2022 and September 2023, 27 consecutive patients underwent off-clamp RAPN for localized renal tumors at our institution. To analyze the learning curve (LC), the cohort was chronologically divided into two phases of 6 months each. Proficiency was defined as the achievement of trifecta while maintaining a comparable intraoperative time in the interquartile range of laparoscopic partial nephrectomy performed by the same surgeon. A logistic binary regression model was built to identify predictors of proficiency achievement. Results: A proficiency score was achieved in 14 patients (74.1%). At univariable analysis, number of consecutive procedures > 12 (OR 13.7; 95%CI 2.05-21.1, p = 0.007), pathological tumor size (OR 0.92; 95%CI 0.89-0.99, p = 0.04) and essential blood hypertension (OR 0.16; 95%CI 0.03-0.82, p = 0.02) were found to be predictors of proficiency score. At multivariable analysis, after adjusting for potential confounding factors, number of consecutive procedures > 12 (OR 8.1; 95%CI 1.44-14.6, p = 0.03) was the only independent predictor of proficiency score achievement. Conclusions: Our results showed that the skills of an experienced laparoscopic surgeon are transferrable to the novel Hugo™ RAS System in the context of nephron-sparing surgery. Improved surgical quality may be expected after completing the first 12 consecutive procedures.

Early introduction of simulation in the medical curriculum: the MedInTo perspective

Despite the increasing body of evidence supporting the use of simulation in medicine, a question remains: when should we introduce it into the medical school's curriculum? We present the experience and future perspectives of the MD program in Medicine and Surgery of University of Turin-MedInTo. Since its launch, MedInTo has been dedicated to integrating innovative teaching approaches at the early stages into the medical curriculum. Herewith, we describe a case-based approach for our activities, which includes the utilization of simulation for emergency medical care training for students and the integration of virtual and augmented reality technology. Dedicated surgical training activities using virtual-augmented reality and life-like simulator for students are also described.