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

> **NIH NIH K23** · UNIVERSITY OF SOUTHERN CALIFORNIA · 2020 · $192,304

## Abstract

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 learn...

## Key facts

- **NIH application ID:** 9982955
- **Project number:** 5K23EB026493-03
- **Recipient organization:** UNIVERSITY OF SOUTHERN CALIFORNIA
- **Principal Investigator:** Andrew Hung
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $192,304
- **Award type:** 5
- **Project period:** 2018-08-21 → 2021-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9982955

## Citation

> US National Institutes of Health, RePORTER application 9982955, Development of Machine Learning Algorithms to Assess and Train Vesico-Urethral Anastomosis during Robot Assisted Radical Prostatectomy (5K23EB026493-03). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/9982955. Licensed CC0.

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