Validating the Diagnostic Accuracy of Mediview 2.0 Software as an Image Analysis Tool of Meibomian Glands

Meibomian gland dysfunction (MGD) is a chronic abnormality of the meibomian glands and commonly characterized by terminal meibomian duct obstruction and/or qualitative or quantitative changes in the glandular secretion. This may result in alteration of the composition and functioning of the tear film lipid layer, and subsequently destabilisation of the tear film. Studies have shown that MGD is one of the most common causes of evaporative dry eye.

It is recommended that the diagnosis of MGD be made by assessing ocular symptoms, lid morphology, mebomian gland mass, gland expressibility, lipid layer thickness and meibography. In meibography, the structure of the meibomian glands, including the ducts and acini can be observed. Photographic documentation of the meibomian gland is also possible under specialised illumination techniques.

There are two types of meibography currently in use- contact meibography and non-contact meibography. Non-contact meibography is advantageous over contact meibography as it is more comfortable for patients. In non-contact meibography, the slit-lamp microscope is equipped with an IR charge-coupled (CCD) device video camera and an IR transmitting filter. During the procedure, IR light is projected onto the inverted eyelid of the subject and the image of the eyelid captured by a camera.

Currently, skilled clinicians analyse images obtained via non-contact meibography manually to determine the health of the patient's eyelid. Through a collaborative project between scientists from bioinformatics institute, A*STAR, Singapore (Dr Lee Hwee Kuan) and SERI, semi-automatic algorithms for classifying meibographs into healthy and unhealthy have been previously developed. The commercial partner for this project, Mediworks, China, has incorporated these algorithms into the Mediview 2.0 software. This software consists of modules for acquiring meibography images as well as for analysing these images. Provided a user draws the outline of the tarsal plate on the captured image, the software is able to classify images into two categories- Healthy and Unhealthy without the aid of a skilled clinician. A trained technician could be taught how to capture the images and hence leaving the doctor with more time to focus on clinical assessment and treatment.

However, the accuracy and reproducibility of this software should be validated against a clinical assessor just as in the original algorithms. We propose to recruit 50 healthy volunteers and 50 patients from SNEC clinics and from public.