[An advanced imaging method for measuring and assessing meibomian glands based on deep learning]

Abstract

Objective: To evaluate the application value of a deep-learning-based imaging method for rapid measurement and evaluation of meibomian glands. Methods: Diagnostic evaluation study. From January 2017 to December 2018, 2 304 meibomian gland images of 576 dry eye patients who were treated at the Eye Center of Wuhan University People's Hospital with an average age of (40.03±11.46) years were collected to build a meibomian gland image database. These images were labeled by 2 clinicians, and a deep learning algorithm was used to build a model and detect the accuracy of the model in identifying and labeling the meibomian glands and calculating the rate of meibomian gland loss. Mean average precision (mAP) and validation loss were used to assess the accuracy of the model in identifying feature areas. Sixty-four meibomian gland images apart from the database were randomly selected and evaluated by 7 clinicians independently. The results were analyzed with paired t-test. Results: This model marked the meibomian conjunctiva (mAP>0.976, validation loss<0.35) and the meibomian gland (mAP>0.922, validation loss<1.0), respectively, thereby achieving high accuracy to calculate the area and ratio of meibomian gland loss. The proportion of meibomian glands marked by the model was 53.24%±11.09%, and the artificial marking was 52.13%±13.38%. There was no statistically significant difference (t=1.935, P>0.05). In addition, the model took only 0.499 second to evaluate each image, while the average time for clinicians was more than 10 seconds. Conclusion: The deep-learning-based imaging model can improve the accuracy of the examination and save time and be used for clinical auxiliary diagnosis and screening of diseases related to meibomian gland dysfunction.(Chin J Ophthalmol, 2020, 56: 774-779).

Keywords: Artificial intelligence; Deep learning; Dry eye; Meibography; Meibomian gland dysfunction.