Over 1,400 da Vinci systems are in use worldwide and the install base grows by more than 350 robots each year. While adoption has been rapid, there exists a wide variance in performance of surgical procedures, which has had a negative impact on care quality, cost and patient safety. This is due in part to inefficient training practices and limited mechanisms for objectively assessing surgical performance. To address the demand for improved training, Mimic Technologies has developed a da Vinci simulator in collaboration with Intuitive Surgical. This simulator collects and analyzes diverse performance data in order to highlight deficiencies in surgical skill. The simulator then recommends steps for addressing identified weaknesses. Conversely, Johns Hopkins University has created a Surgical Assistant Workstation (SAW) that collects data from the Surgeon's Console during dry lab training and surgery. After analyzing this data with advanced statistical analysis and machine learning techniques, improper surgical practices can be identified. Mimic Technologies and John Hopkins University propose to apply respective technologies and expertise to the develop of an automated support system for the da Vinci surgical robot. The proposed system will provide continuous surgical skills assessment and decision support throughout initial training and thereafter during surgery.
Keywords: Da Vinci, Intuitive Surgical, Automated Assessment, Dv-Trainer, Saw, Surgery Simulation
