Development of Machine Learning Algorithms to Assess and Train Vesico-Urethral Anastomosis during Robot Assisted Radical Prostatectomy

开发机器学习算法来评估和训练机器人辅助根治性前列腺切除术期间的膀胱尿道吻合术

基本信息

批准号：
9982955
负责人：
Andrew Hung
金额：
$ 19.23万
依托单位：
UNIVERSITY OF SOUTHERN CALIFORNIA
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-08-21 至 2021-07-31
项目状态：
已结题

项目摘要

PROJECT SUMMARY/ABSTRACT CANDIDATE (Andrew J. Hung, MD): My long-term goal is to establish a career in innovating training methods for robotic surgery which will lead to curtailing surgeon learning curve, and maximize patient safety. My first step towards that goal focuses on understanding objective metrics that measure surgeon performance, and how machine learning algorithms can process that data to guide training. I have developed a career development program that builds on my clinical training in robotic urologic surgery and prior research in surgical training. Through mentorship, a fellowship, and formal coursework, this K23 award will provide me the necessary support to develop expertise in 3 areas where I do not have formal training, yet are critical to my success: (1) Machine learning; (2) Surgical education; (3) Advanced statistical skills and study design. MENTORING TEAM: My career development and research plans leverage existing institutional resources, including the USC Machine Learning Center, led by co-primary mentor Dr. Yan Liu; and Keck Hospital of USC, the second busiest robotic center by volume in the United States and the USC Institute of Urology (led by co- primary mentor and chairman Dr. Inderbir Gill), home to pioneers of several urologic surgical techniques with a robust research apparatus supporting several NIH-funded clinical scientists. My mentoring team is complemented by co-mentor Dr. Robert Sweet, a DOD-funded expert on surgical education; career mentor Dr. Larissa Rodriguez, a federally funded clinician/scientist experienced in mentoring K awardees; educational psychology collaborator Dr. Kenneth Yates, an authority on cognitive task analysis; and consultant Dr. Anthony Jarc, at Intuitive Surgical who has supported much of the pilot data on objective performance metrics. The proposed K23 work truly requires the robust collaboration of experts in robotic surgery, education, and machine learning. RESEARCH: The learning curve for surgeons performing robot assisted radical prostatectomy (RARP) is steep: over 100 cases. Current ‘gold standard’ methods of surgical assessment rely on subjective expert review, but such evaluations are time consuming and inconsistent. Nonetheless, credentialing a surgeon to perform robotic surgery has enormous implications - patient outcomes are at risk, and a surgeon’s career is on the line. Informed by my clinical expertise in robotic urological surgery and preliminary data, I will develop a novel method of utilizing machine learning (ML) algorithms to objectively assess robotic surgeon performance and to guide training for the vesico-urethral anastomosis (VUA), the most critical reconstructive part of the robot-assisted radical prostatectomy (RARP). I will develop and validate objective metrics directly captured from the da Vinci robot during the VUA (Aim 1), train machine learning algorithms to assess a surgeon’s performance of VUA (Aim 2), and utilize ML algorithms to guide surgeons learning the VUA (Aim 3). Armed with these data and skills from this award, I will be uniquely suited to utilize machine learning to generalize objective surgeon assessment for robot-assisted surgical procedures within and beyond urology. Finally, the results from this study will provide preliminary data for independent funding through mechanisms such as an NIH R01 grant.

项目摘要/摘要候选人（医学博士Andrew J. Hung）：我的长期目标是建立创新培训方法的职业用于机器人手术，这将导致减少外科医生的学习曲线，并最大程度地提高患者的安全性。我的第一个朝向该目标的一步侧重于理解衡量外科医生绩效的客观指标，并且机器学习算法如何处理该数据以指导培训。我发展了职业开发计划是基于我在机器人泌尿外科手术和先前研究的临床培训的基础上手术训练。通过心态，奖学金和正式课程，该K23奖将为我提供在我没有正规培训的3个领域中发展专业知识的必要支持，但对我来说至关重要成功：（1）机器学习；（2）手术教育；（3）高级统计技能和研究设计。指导团队：我的职业发展和研究计划利用现有的机构资源，包括由共同培训者Yan Liu博士领导的USC机器学习中心；和加州大学的凯克医院美国第二次最繁忙的机器人中心在美国和USC泌尿外科研究所（由共同领导主要导师兼董事长Inderbir Gill博士），是几种泌尿外科手术技术的先驱者的所在地支持数位NIH资助的临床科学家的强大研究机构。我的心理团队是由DOD资助的外科教育专家Robert Sweet博士完成；职业导师 Larissa Rodriguez博士是一位经验丰富的心理K获奖者的临床/科学家；教育心理学合作者肯尼斯·耶茨（Kenneth Yates）博士，认知任务分析的权威；和 Intuitive Surgical的Anthony Jarc顾问Anthony Jarc博士支持了有关目标的许多试点数据性能指标。拟议的K23工作确实需要机器人专家的强大合作手术，教育和机器学习。研究：表演机器人的外科医生的学习曲线辅助根治性前列腺切除术（RARP）是钢：超过100例。当前的“金标准”手术方法评估依赖于主观专家审查，但是这种评估既耗时又不一致。尽管如此，对外科医生进行机器人手术的认证具有巨大的影响 - 患者的结果处于危险之中，外科医生的职业生涯也在。由我的机器人泌尿科手术专业知识得知和初步数据，我将开发一种使用机器学习（ML）算法的新方法客观地评估机器人外科医生的表现，并指导Vesico-prethral的培训吻合术（VUA），这是机器人辅助的根治性前列腺切除术（RARP）中最关键的重建部分。将在VUA期间直接从DA Vinci机器人捕获的目标指标并验证目标指标（AIM 1），训练机器学习算法评估外科医生的VUA表现（AIM 2），并使用ML 指导外科医生学习VUA的算法（AIM 3）。凭借该奖项中的这些数据和技能，我将非常适合利用机器学习来概括客观的外科医生评估机器人辅助泌尿外科内外的手术程序。最后，这项研究的结果将提供初步数据通过NIH R01赠款等机制进行独立资金。