ABSTRACT Medical errors are the third leading cause of death in the US at a cost of $20 billion annually. Surgical complications account for a third of these deaths and cost. Surgical performance directly impacts patient outcomes. Prostate cancer, the most common cancer in men, is treated with surgery (robot-assisted radical prostatectomy (RARP)) that can lead to impotence, incontinence, and even death. Reliable means of objectively assessing technique are required. In this project we will focus on assessing surgeon suturing skills during RARP through virtual reality (VR) simulation. Suturing is a common skill in many types of surgeries, can be tracked with performance metrics, and has been correlated with patient outcomes after RARP. In this proposal we seek to first determine the critical sub-step maneuvers of suturing and the technical skills necessary to achieve them successfully (Aim 1a). Further, we intend to develop an automated skills assessment pipeline through the analysis of raw kinematic data (Aim 1b), video (Aim 2b), and both kinematic/video (Aim 2c), from VR simulation performance by innovative machine learning strategies and deep-learning-based computer vision. The primary differentiator of the proposed work is determining how well granular sub-step maneuvers in suturing are performed. Surgeons participating in this study will not only provide data through their VR simulation performance, but will also contribute real patient data from the RARP to establish the relationship between surgeon skill, patient factors, and surgical outcomes. Statistical modeling will measure the differential impact of surgeon skill and patient factors to patient outcomes (Aim 3). We hypothesize that innovative application of machine learning algorithms can accurately assess surgeon technical skills, and can further anticipate likelihood of relevant clinical outcomes. The proposed work will enable scalable and actionable feedback in VR, empowering surgeons with valuable knowledge to minimize surgical risk in live surgery.