Transfer Learning of a Neural Network for Robotic Surgical Assessment

In the researchers' words

The goal of this observational study is to explore how pretrained artificial intelligence (AI) models, trained on preclinical data, can improve the accuracy of action recognition and skills assessment in robot-assisted surgery (RAS) in urological patients by the use of transfer learning. The main questions it aims to answer are: * Can pretrained AI models accurately assess action recognition and skills assessment in clinical surgeries? * How do different training approaches of transfer learning affect the performance of the AI models? A baseline model developed from scratch using clinical data will be compared to pretrained models that are (1) directly applied to clinical data (2) fine-tuned by training only some layers of the AI model, and (3) fully retrained to see if these approaches improve performance. Participants who are robot surgeons will: * Undergo RAS procedures on patients, with no intervention, where video data will be collected for later action recognition and skills assessment. * Contribute to model training and evaluation through clinical dataset integration.