# Artificial Intelligence Driven Tools for Objective Surgical Performance Improvement

> **NIH NIH R01** · JOHNS HOPKINS UNIVERSITY · 2024 · $460,000

## Abstract

Abstract / Summary
Currently, supervised surgical training provides only a small fraction of surgical experience in the career of a practicing
surgeon. Surgeons’ skill develops throughout their career. Surgeons benefit from supervised feedback from experts during
training, but they lose such structured and specific feedback once they begin independent practice. Surgical skill is
associated with patient outcomes. Therefore, supporting surgeons’ continuous professional learning through automated
structured resources can improve patient care. The status quo for surgeons in practice is to measure patient outcomes or
other process of care variables as indirect measures of their skill. These measures do not inform surgeons how to improve.
The goal in this project is to develop tools to analyze videos of the surgical field to provide surgeons with unbiased skill
assessments and specific feedback on how to improve. This project includes integration of these tools into a personalized
surgical learning platform and evaluation of its effectiveness for surgeons’ skill acquisition. To achieve this goal, this project
includes a multi-disciplinary team to include expertise in ophthalmology, surgical education, surgical data science, computer
vision, machine learning and deep learning, statistics, and human-computer interaction. The video analysis tools developed
in this project will enable the following for cataract surgery, one of the most common surgical procedures in the U.S. and
across the world: 1) objective assessments of surgeons’ skill; 2) provide surgeons with specific feedback on how to improve
that is personalized given their past performance; and 3) preliminary evidence of effectiveness of a personalized learning
platform for surgeons’ skill acquisition. The anticipated impact of our work is to create a pathway in which the surgeon is
incentivized to see themselves and their performance as part of the process of improving outcomes and value in care, and
institutions have access to objective tools to create reproducible standards for surgical competency.

## Key facts

- **NIH application ID:** 10907495
- **Project number:** 5R01EY033065-04
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Shameema Sikder
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $460,000
- **Award type:** 5
- **Project period:** 2021-09-30 → 2025-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10907495

## Citation

> US National Institutes of Health, RePORTER application 10907495, Artificial Intelligence Driven Tools for Objective Surgical Performance Improvement (5R01EY033065-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10907495. Licensed CC0.

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