Automated detection and assessment of post‑operative eyelid outcome in trachomatous trichiasis surgery

Trachomatous trichiasis (TT) is an ocular condition in which the eyelid turns inward causing the eyelashes to scratch the eye, leading to blindness and affecting millions of people worldwide. The standard treatment for TT is surgery, where an incision is made to rotate the eyelid margin outward, repositioning the eyelashes to their original position. However, outcomes after surgery are often suboptimal, with a high risk of post-operative trichiasis (PTT). Studies have shown that the appearance of the immediate post-operative eyelid strongly correlates with the success of the procedure after six weeks, emphasizing the importance of early identification and correction of poor surgical results. We propose a mobile application that detects post-operative eyelids at higher risk of poor outcomes, enabling surgeons in the field to have immediate feedback to perform the necessary corrections and improve patient outcomes. The algorithm is based on the well-established Faster R-CNN model, which detects and classifies parts of the eyelid into three categories: under-correction, overcorrection, or appropriate correction. The model achieved 75.7% recall for under-correction and 75.6% recall for overcorrection, demonstrating strong sensitivity in identifying potential for adverse outcomes. The UI/UX of the application was designed with an intuitive interface that allows users to take a picture of an eyelid and evaluate the surgical result using the model. The algorithm runs in under 12 seconds and has been tested by TT surgeons in the field. This work has the potential to significantly improve post-operative trichiasis outcomes, reducing PTT rates, and improving life quality in resource-limited settings.