Abstract
PURPOSE: Eye rubbing is considered to play a significant role in the progression of keratoconus and of corneal ectasia following refractive surgery. To our knowledge, no tool performs an objective quantitative evaluation of eye rubbing using a device that is familiar to typical patients. We introduce here an innovative solution for objectively quantifying and preventing eye rubbing. It consists of an application that uses a deep-learning artificial intelligence (AI) algorithm deployed on a smartwatch. METHODS: A Samsung Galaxy Watch 4 smartwatch collected motion data from eye rubbing and everyday activities, including readings from the gyroscope, accelerometer, and linear acceleration sensors. The training of the model was carried out using two deep-learning algorithms, long short-term memory (LSTM) and gated recurrent unit (GRU), as well as four machine learning algorithms: random forest, K-nearest neighbors (KNN), support vector machine (SVM), and XGBoost. RESULTS: The model achieved an accuracy of 94%. The developed application could recognize, count, and display the number of eye rubbings carried out. The GRU model and XGBoost algorithm also showed promising performance. CONCLUSIONS: Automated detection of eye rubbing by deep-learning AI has been proven to be feasible. This approach could radically improve the management of patients with keratoconus and those undergoing refractive surgery. It could detect and quantify eye rubbing and help to reduce it by sending alerts directly to the patient. TRANSLATIONAL RELEVANCE: This proof of concept could confirm one of the most prominent paradigms in keratoconus management, the role of abnormal eye rubbing, while providing the means to challenge or even negate it by offering the first automated and objective tool for detecting eye rubbing.