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

• 批准号: 10940356
• 负责人: Jim Hu
• 金额: $ 64.68万
• 依托单位: CEDARS-SINAI MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-07 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10940356
• 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

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.

抽象的 外科医生之间的表现差异会导致患者治疗结果的差异，但是 如果外科医生不了解手术的技术考虑因素，他们就无法进步 程序将使他们能够改善结果。作为一个典型的例子，机器人辅助 前列腺癌根治性切除术 (RARP) 可能会导致患者的死亡率存在很大差异 勃起功能 (EF) 恢复 (10-50%)。客观评估外科医生的可靠方法 通常缺乏与患者治疗结果密切相关的表现。 在此项目中，作为量化外科医生表现以改善患者水平的测试用例 结果，我们将重点评估外科医生在手术期间的神经保留（NS）解剖质量 RARP 通过评估手术视频和患者结果。微妙的 NS 步骤是 良好的测试用例，因为它是可量化 EF 结果的主要决定因素，RARP 是 常见手术（约 145,000 例/年），并且手术视频易于分析。 我们将通过三个独立但互补的目标来实现我们的目标。目标一：我们 寻求通过专家共识确定共同的技术考虑因素 优化执行 EF 恢复的 NS 步骤所必需的。目标2：我们将开发一个 通过计算机视觉分析手术的自动化绩效评估流程 视频。目标 3：我们将开发并验证技能反馈评估工具，以证明- 概念 NS 特定的 VR 模拟。 拟议工作的主要区别在于我们将量化最相关的技术 组织解剖的考虑因素驱动患者报告结果。外科医生 参与这项研究不仅会通过他们执行的手术视频提供数据 NS 步骤，但他们也会将 RARP 中的真实患者 EF 结果数据提供给 建立外科医生技能、患者因素和 EF 结果之间的关系。 统计模型将描述外科医生技能和患者因素对 EF 的不同影响 结果。此外，我们将利用基于深度学习的计算机视觉来全面捕捉所有 NS 技术和技能的众多方面，以帮助确定它们如何为 最终的EF结果。 拟议的工作将实现可扩展且可操作的反馈，使外科医生能够 最大限度地提高手术效果的宝贵知识。 RARP后的NS步骤和EF恢复 将作为我们未来自动化评估的测试用例，以改善任何方面的结果 手术过程。

项目成果

Surgical gestures as a method to quantify surgical performance and predict patient outcomes.

• DOI: 10.1038/s41746-022-00738-y
• 发表时间: 2022-12-22
• 期刊: NPJ digital medicine
• 影响因子: 15.2

Technical surgical skill assessment of neurovascular bundle dissection and urinary continence recovery after robotic-assisted radical prostatectomy.

机器人辅助根治性前列腺切除术后神经血管束解剖和尿失禁恢复的技术手术技能评估。

• DOI: 10.1097/ju9.0000000000000035
• 发表时间: 2023
• 期刊: JU open plus
• 作者: Ma,Runzhuo;Cen,Steven;Forsyth,Edward;Probst,Patrick;Asghar,Aeen;Townsend,William;Hui,Alvin;Desai,Aditya;Tzeng,Michael;Cheng,Emily;Ramaswamy,Ashwin;Wagner,Christian;Hu,JimC;Hung,AndrewJ
• 通讯作者: Hung,AndrewJ