Predicting clinical outcome of sulfur mustard induced ocular injury using machine learning model

The sight-threatening sulfur mustard (SM) induced ocular injury presents specific symptoms in each clinical stage. The acute injury develops in all exposed eyes and may heal or deteriorate into chronic late pathology. Early detection of eyes at risk of developing late pathology may assist in providing unique monitoring and specific treatments only to relevant cases. In this study, we evaluated a machine-learning (ML) model for predicting the development of SM-induced late pathology based on clinical data of the acute phase in the rabbit model. Clinical data from 166 rabbit eyes exposed to SM vapor was used retrospectively. The data included a comprehensive clinical evaluation of the cornea, eyelids and conjunctiva using a semi-quantitative clinical score. A random forest classifier ML model, was trained to predict the development of corneal neovascularization four weeks post-ocular exposure to SM vapor using clinical scores recorded three weeks earlier. The overall accuracy in predicting the clinical outcome of SM-induced ocular injury was 73%. The accuracy in identifying eyes at risk of developing corneal neovascularization and future healed eyes was 75% and 59%, respectively. The most important parameters for accurate prediction were conjunctival secretion and corneal opacity at 1w and corneal erosions at 72 h post-exposure. Predicting the clinical outcome of SM-induced ocular injury based on the acute injury parameters using ML is demonstrated for the first time. Although the prediction accuracy was limited, probably due to the small dataset, it pointed out towards various parameters during the acute injury that are important for predicting SM-induced late pathology and revealing possible pathological mechanisms.