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.

Public Health Relevance

The learning curve for surgeons performing robot assisted radical prostatectomy (RARP) for prostate cancer is steep, and current methods of evaluating surgeons require subjective and time-consuming expert review. Streamlined training and assessment utilizing objective performance metrics and machine learning algorithms can significantly curtail learning curve with patients, and decrease the overall morbidity of prostate cancer treatment.

National Institute of Health (NIH)
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Mentored Patient-Oriented Research Career Development Award (K23)
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Special Emphasis Panel (ZEB1)
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Erim, Zeynep
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University of Southern California
Schools of Medicine
Los Angeles
United States
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