Preserving Erectile Function by Quantifying the Nerve-Sparing step of the Robotic Prostatectomy

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ABSTRACT Variation in performance between surgeons leads to differences in patient outcomes, but surgeons cannot improve if they are not aware of the technical considerations for a surgical procedure that will allow them to improve outcomes. As a prime example, robot-assisted radical prostatectomy (RARP) for prostate cancer can lead to highly variable rates of patient erectile function (EF) recovery (10-50%). Yet reliable means of objectively assessing surgeon performance, that strongly associate with patient outcomes, are generally lacking. In this project, as a test case for quantifying surgeon performance to improve a patient outcome, we will focus on assessing a surgeon’s nerve-sparing (NS) dissection quality during RARP through the evaluation of surgical video and patient outcomes. The nuanced NS step is a good test case because it is the primary determinant of the quantifiable EF outcome, RARP is a common procedure (~145,000 cases/year), and surgical video is readily available for analysis. We will accomplish our objective with three independent, yet complementary aims. Aim 1: We seek to determine through expert consensus the common technical considerations necessary to optimally perform the NS step for EF recovery. Aim 2: We will develop an automated performance assessment pipeline through computer vision analysis of surgical video. Aim 3: We will develop and validate a skills feedback assessment tool for a proof-of- concept NS-specific VR simulation. The primary differentiator of the proposed work is we will quantify the most relevant technical considerations for tissue dissection driving a patient reported outcome. Surgeons participating in this study will not only provide data through surgical videos of them performing the NS step, but they will also contribute real patient EF outcome data from the RARP to establish the relationship between surgeon skill, patient factors, and EF outcome. Statistical modeling will delineate the differential impact of surgeon skill and patient factors to EF outcome. Further, we will harness deep learning-based computer vision to holistically capture all the numerous facets of NS technique and skill to help determine how they contribute to the ultimate EF outcome. The proposed work will enable scalable and actionable feedback, empowering surgeons with valuable knowledge to maximize surgical outcome. The NS step and EF recovery after RARP will serve as our test case for future automated assessments to improve outcomes in any surgical procedure.