# Automated Assessment for Robotic Suturing Utilizing Deep Learning Algorithms

> **NIH NIH R01** · UNIVERSITY OF SOUTHERN CALIFORNIA · 2021 · $693,355

## Abstract

ABSTRACT
Medical errors are the third leading cause of death in the US at a cost of $20 billion annually.
Surgical complications account for a third of these deaths and cost. Surgical performance
directly impacts patient outcomes. Prostate cancer, the most common cancer in men, is treated
with surgery (robot-assisted radical prostatectomy (RARP)) that can lead to impotence,
incontinence, and even death. Reliable means of objectively assessing technique are required.
In this project we will focus on assessing surgeon suturing skills during RARP through virtual
reality (VR) simulation. Suturing is a common skill in many types of surgeries, can be tracked
with performance metrics, and has been correlated with patient outcomes after RARP.
In this proposal we seek to first determine the critical sub-step maneuvers of suturing and the
technical skills necessary to achieve them successfully (Aim 1a). Further, we intend to develop
an automated skills assessment pipeline through the analysis of raw kinematic data (Aim 1b),
video (Aim 2b), and both kinematic/video (Aim 2c), from VR simulation performance by
innovative machine learning strategies and deep-learning-based computer vision.
The primary differentiator of the proposed work is determining how well granular sub-step
maneuvers in suturing are performed. Surgeons participating in this study will not only provide
data through their VR simulation performance, but will also contribute real patient data from the
RARP to establish the relationship between surgeon skill, patient factors, and surgical
outcomes. Statistical modeling will measure the differential impact of surgeon skill and patient
factors to patient outcomes (Aim 3). We hypothesize that innovative application of machine
learning algorithms can accurately assess surgeon technical skills, and can further anticipate
likelihood of relevant clinical outcomes.
The proposed work will enable scalable and actionable feedback in VR, empowering surgeons
with valuable knowledge to minimize surgical risk in live surgery.

## Key facts

- **NIH application ID:** 10208178
- **Project number:** 1R01CA251579-01A1
- **Recipient organization:** UNIVERSITY OF SOUTHERN CALIFORNIA
- **Principal Investigator:** Andrew Hung
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $693,355
- **Award type:** 1
- **Project period:** 2021-04-01 → 2026-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10208178

## Citation

> US National Institutes of Health, RePORTER application 10208178, Automated Assessment for Robotic Suturing Utilizing Deep Learning Algorithms (1R01CA251579-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10208178. Licensed CC0.

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