Automatic identification of meibomian gland dysfunction with meibography images using deep learning

Abstract

\n Background\n\nArtificial intelligence is developing rapidly, bringing increasing numbers of intelligent products into daily life. However, it has little progress in dry eye, which is a common disease and associated with meibomian gland dysfunction(MGD). Non-invasive infrared meibography, known as an effective diagnostic tool of MGD, allows for objective observation of meibomian glands. Thus, we discuss a deep learning method to measure and assess meibomian glands of meibography.\nMethods\n\nWe used Mask R-CNN deep learning(DL) framework. A total of 1878 meibography images were collected and manually annotated by two licensed eyelid specialists with two classes: conjunctiva and meibomian glands. The annotated pictures were used to establish a DL model. An independent test dataset contained 58 images was used to compare the accuracy and efficiency of the deep learning model with specialists.\nResults\n\nThe DL model calculated the ratio of meibomian gland loss with precise values by achieving high accuracy in the identification of conjunctiva (validation loss\u2009<\u20090.35, mAP\u2009>\u20090.976) and meibomian glands (validation loss\u2009<\u20091.0, mAP\u2009>\u20090.92). The comparison between specialists’ annotation and the DL model evaluation showed that there is little difference between the gold standard and the model. Each image takes 480ms for the model to evaluate, almost 21 times faster than specialists.\nConclusions\n\nThe DL model can improve the accuracy of meibography image evaluation, help specialists to grade the meibomian glands and save their time to some extent.