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

通过量化机器人前列腺切除术的神经保留步骤来保留勃起功能

基本信息

批准号：
10661812
负责人：
Jim Hu
金额：
$ 1.2万
依托单位：
UNIVERSITY OF SOUTHERN CALIFORNIA
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-07-07 至 2023-07-10
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10661812
关键词：
Address Assessment tool Automobile Driving Classification Computer Vision Systems Consensus Data Development Dissection E-learning Electrocoagulation Evaluation Feedback Future Gestures Human Injury Interobserver Variability Knowledge Link Machine Learning Malignant Neoplasms Malignant neoplasm of prostate Manuals Measurable Methodology Modeling Nerve-Sparing Surgery Operative Surgical Procedures Outcome Outcome Measure Patient Outcomes Assessments Patient-Focused Outcomes Patients Pattern Performance Procedures Process Prostatectomy Questionnaires Radical Prostatectomy Recovery Recovery of Function Reproducibility Research Robotics Statistical Models Surgeon Surgical sutures System Technical Expertise Techniques Technology Testing Tissues Training Translating Treatment Factor Validation Variant Visual Pattern Recognition Work deep learning empowerment experience improved improved outcome instrument men neurovascular novel postoperative recovery preference preservation programs quality assurance robot assistance simulation skills spared nerve surgery outcome tool virtual reality virtual reality simulation

项目摘要

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.

摘要