# Using Image Recognition Technology and Smartphones to Improve Trichiasis Surgery Outcomes

> **NIH NIH R21** · UNIV OF NORTH CAROLINA CHAPEL HILL · 2022 · $194,240

## Abstract

ABSTRACT
Outcomes following surgery for trachomatous trichiasis (TT) are often suboptimal, with rates of post-operative
trichiasis (PTT) ranging from 10% - >50% within one year following surgery and moderate to severe eyelid
contour abnormalities (ECA) occurring >10% of the time. Nigeria is home to the second largest number of
trichiasis cases globally, with >100,000 individuals currently needing surgical correction. These individuals live
in rural communities, with limited access to health care. Thus, surgery is provided by eye nurses (“trichiasis
surgeons”) who receive a brief course on how to perform TT surgery and then are asked to conduct surgical
camps in villages across their region. Supportive supervision is often lacking. In this project, we plan to
develop an mHealth tool to provide health workers with immediate feedback on their surgery, and to
provide guidance on whether additional surgical adjustment is warranted.
Our prior work has demonstrated that the immediate post-operative eyelid appearance is a strong predictor of
later post-operative success; a surgeon on our team was able to predict 70% of PTT cases and 85% of eyelid
contour abnormality cases at six weeks post-op based on a single photograph of each eyelid at the close of
surgery. Further, our team has shown that we can develop image recognition software to detect eyelids with
TT with >90% accuracy (sensitivity 92%) using photographs from our ongoing NEI-funded clinical trial. In the
proposed project, during the R21 phase, we plan to use images from the same trial to develop a machine
learning algorithm that can predict whether an eyelid will develop either an eyelid contour abnormality or PTT
(AIM 1). This algorithm will be incorporated into a smartphone app that will allow surgeons to take a picture at
the end of surgery and receive immediate feedback on whether the eyelid could benefit from an adjustment
such as tightening or loosening the sutures (AIM 2). In the R33 phase, we will test the functionality of the
algorithm and app using an iterative, user-focused approach (AIM 3). Then we will work with the Nigerian
Ministry of Health and partners to develop and test a protocol for app deployment and surgical monitoring (AIM
4). Finally, using data from Aim 3, we will assess the impact of the app on long-term service delivery by using
economic models to estimate the number of poor outcomes that could be averted by using the app globally
(AIM 5). This project has the potential to dramatically improve trichiasis surgery outcomes worldwide. Further,
this project can be used as a proof-of principle that remote technology can be used to aid surgeons and other
health workers operating in resource-poor settings.

## Key facts

- **NIH application ID:** 10531980
- **Project number:** 1R21EY034351-01
- **Recipient organization:** UNIV OF NORTH CAROLINA CHAPEL HILL
- **Principal Investigator:** EMILY W GOWER
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $194,240
- **Award type:** 1
- **Project period:** 2022-09-30 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10531980

## Citation

> US National Institutes of Health, RePORTER application 10531980, Using Image Recognition Technology and Smartphones to Improve Trichiasis Surgery Outcomes (1R21EY034351-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10531980. Licensed CC0.

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