Complex Pattern Analysis in Robotic Surgery through Deep Learning

The purpose of this research is to demonstrate how deep learning techniques might contribute to enhanced abilities in robotic surgery systems to offer improved precision, autonomy, and safety for surgeries and how a deep learning model can help with the acquisition of surgical skills in the execution of motion plans to achieve a highly precise surgical action. By incorporating deep learning-based safety mechanisms, we may reduce complications and improve patient outcomes. Deep learning also helps to train surgeons through the analysis of surgical data, providing valuable feedback. Our approach integrates the power of abstraction techniques with the power of deep learning through vital steps. Large datasets are compiled on surgical procedures using video footage, sensor data, and instrument trajectory. Deep learning models is used to identify complex patterns connecting the surgical actions to anatomy and results. An abstraction layer is formed that translates the low-level commands of the robots into high-level surgical objectives. A deep learning model is integrated and abstracted into a robot system. The results showed that deep learning abstraction significantly improved robotic surgery systems, enhancing precision, autonomy, and safety and paving the way for more efficient and autonomous surgical interventions.