Using Image Recognition Technology and Smartphones to Improve Trichiasis Surgery Outcomes

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.