A clinical utility assessment of the automatic measurement method of the quality of Meibomian glands

No outside funding was received for this study., This research was supported by grants from the Medical University of Silesia in Katowice, Grant No. KNW-1-023/N/6/0; the National Natural Science Foundation of China (31600758); Beijing Natural Science Foundation (7174287); the priming scientific research foundation for the junior researcher in Beijing Tongren Hospital, Capital Medical University (2015-YJJZZL- 008) and Beijing Key Laboratory of Ophthalmology and Visual Science (2016YKSJ02).Background: Meibomian gland dysfunction (MGD) is one of the most common diseases observed in clinics and is the leading cause of evaporative dry eye. Today, diagnostics of MGD is not fully automatic yet and is based on a qualitative assessment made by an ophthalmologist. Therefore, an automatic analysis method was developed to assess MGD quantiatively. Materials: The analysis made use of 228 images of 57 patients recorded by OCULUS Keratograph® 5 M with a resolution of 1024 × 1360 pixels concern 30 eyes of healthy individuals (14 women and 16 men) and 27 eyes of sick patients (10 women and 17 men). The diagnosis of dry eye was made according to the consensus of DED in China (2013). Methods: The presented method of analysis is a new, developed method enabling an automatic, reproducible and quantitative assessment of Meibomian glands. The analysis relates to employing the methods of analysis and image processing. The analysis was conducted in the Matlab environment Version 7.11.0.584, R2010b, Java VM Version: Java 1.6.0_17-b04 with Sun Microsystems Inc. with toolboxes: Statistical, Signal Processing and Image Processing. Results: The presented, new method of analysis of Meibomian glands is fully automatic, does not require operator's intervention, allows obtaining reproducible results and enables a quantitative assessment of Meibomian glands. Compared to the other known methods, particularly with the method described in literature it allows obtaining better sensitivity (98%) and specificity (100%) results by 2%.Medical University of Silesia in Katowice; National Natural Science Foundation of China; Natural Science Foundation of Beijing Municipalit