Strabismus and Artificial Intelligence App: Optimizing Diagnostic and Accuracy

Purpose: Clinical evaluation of eye versions plays an important role in the diagnosis of special strabismus. Despite the importance of versions, they are not standardized in clinical practice because they are subjective. Assuming that objectivity confers accuracy, this research aims to create an artificial intelligence app that can classify the eye versions into nine positions of gaze. Methods: We analyzed photos of 110 strabismus patients from an outpatient clinic of a tertiary hospital at nine gazes. For each photo, the gaze was identified, and the corresponding version was rated by the same examiner during patient evaluation. Results: The images were standardized by using the OpenCV library in Python language, so that the patient's eyes were located and sent to a multilabel model through the Keras framework regardless of the photo orientation. Then, the model was trained for each combination of the following groupings: eyes (left, right), gaze (1 to 9), and version (−4 to 4). Resnet50 was used as the neural network architecture, and the Data Augmentation technique was applied. For quick inference via web browser, the SteamLit app framework was employed. For use in Mobiles, the finished model was exported for use in through the Tensorflow Lite converter. Conclusions: The results showed that the mobile app might be applied to complement evaluation of ocular motility based on objective classification of ocular versions. However, further exploratory research and validations are required. Translational Relevance: Apart from the traditional clinical practice method, professionals will be able to envisage an easy-to-apply support app, to increase diagnostic accuracy.