Gloria Pelizzo1,2, Lucilla Cardinali3, Lilla Bonanno4, Silvia Marino4, Carlo Cavaliere5, Marco Aiello5, Placido Bramanti4, Emanuela Mazzon4, Andrea Soddu6, Valeria Calcaterra7,8. 1. Department of Biomedical and Clinical Sciences "L. Sacco", University of Milan, Milan, Italy, gloriapelizzo@gmail.com. 2. Department of Pediatric Surgery, "Vittore Buzzi" Children's Hospital, University of Milano, Milan, Italy, gloriapelizzo@gmail.com. 3. CMoN, Cognition, Motion and Neuroscience Unit, Fondazione Istituto Italiano di Tecnologia, Genoa, Italy. 4. IRCCS Centro Neurolesi "Bonino-Pulejo", Messina, Italy. 5. IRCCS SDN, Istituto di Ricerca Diagnostica e Nucleare, Naples, Italy. 6. Brain and Mind Institute, Department of Physics and Astronomy, Western University, London, Ontario, Canada. 7. Pediatrics and Adolescentology Unit, Department of Internal Medicine and Therapeutics, University of Pavia, Pavia, Italy. 8. Pediatric Unit, "Vittore Buzzi" Children's Hospital, Milan, Milan, Italy.
Abstract
INTRODUCTION: The advantages of the robotic approach in surgery are undisputed. However, during surgical training, how this technique influences the learning curve has not been described. We provide a tentative model for analyzing the learning curves associated with observation and active participation in learning different surgical techniques, using functional imaging. METHODS:Forty medical students were enrolled and assigned to 4 groups who underwent training in robotic (ROB), laparoscopic (LAP), or open (OPEN) surgery, and a control group that performed motor training without surgical instruments. Surgical/motor training included six 1-h sessions completed over 6 days of the same week. All subjects underwent functional magnetic resonance imaging (fMRI) scanning sessions, before and after surgical training during. RESULTS:Twenty-three participants completed the study. The 3 surgical groups exhibited different learning curves during training. The main effects of the day of training (p < 0.01) and the group (p < 0.01) as well as a significant interaction of day of training group (p < 0.01) were observed. The performance increased in the first 4 days, reaching a peak at day 4, when all groups were considered together. The OPEN group showed the best performance compared to all other groups (p < 0.04). The OPEN group showed a rapid improvement in performance, which peaked at day 4 and decreased on the last day. Similarly, the LAP group showed a steady increase in the number of exercises they completed, which continued for the entire training period and reached a peak on the last day. However, the participants training in ROB surgery, after a performance initially indistinguishable from that of the LAP group, had a dip in their performance, quickly followed by an improvement and reaching a plateau on day 4. fMRI analysis documented the different involvement of the cortical and subcortical areas based on the type of training. Surgical training modified the activation of some brain regions during both observation and the execution of tasks. CONCLUSIONS: Differences in the learning curves of the 3 surgical groups were noted. Functional brain activity represents an interesting starting point to guide training programs.
RCT Entities:
INTRODUCTION: The advantages of the robotic approach in surgery are undisputed. However, during surgical training, how this technique influences the learning curve has not been described. We provide a tentative model for analyzing the learning curves associated with observation and active participation in learning different surgical techniques, using functional imaging. METHODS: Forty medical students were enrolled and assigned to 4 groups who underwent training in robotic (ROB), laparoscopic (LAP), or open (OPEN) surgery, and a control group that performed motor training without surgical instruments. Surgical/motor training included six 1-h sessions completed over 6 days of the same week. All subjects underwent functional magnetic resonance imaging (fMRI) scanning sessions, before and after surgical training during. RESULTS: Twenty-three participants completed the study. The 3 surgical groups exhibited different learning curves during training. The main effects of the day of training (p < 0.01) and the group (p < 0.01) as well as a significant interaction of day of training group (p < 0.01) were observed. The performance increased in the first 4 days, reaching a peak at day 4, when all groups were considered together. The OPEN group showed the best performance compared to all other groups (p < 0.04). The OPEN group showed a rapid improvement in performance, which peaked at day 4 and decreased on the last day. Similarly, the LAP group showed a steady increase in the number of exercises they completed, which continued for the entire training period and reached a peak on the last day. However, the participants training in ROB surgery, after a performance initially indistinguishable from that of the LAP group, had a dip in their performance, quickly followed by an improvement and reaching a plateau on day 4. fMRI analysis documented the different involvement of the cortical and subcortical areas based on the type of training. Surgical training modified the activation of some brain regions during both observation and the execution of tasks. CONCLUSIONS: Differences in the learning curves of the 3 surgical groups were noted. Functional brain activity represents an interesting starting point to guide training programs.