Validating the Diagnostic Accuracy of Mediview 2.0 Software as an Image Analysis Tool of Meibomian Glands
Condition: Dry Eyes
MeSH terms: Dry Eye Syndromes
Study type: Interventional

Summary: 
      Non-contact meibography is a useful tool in assessing the health of meibomian glands in
      patients. Instead of using normal light in contact meibography, non-contact meibography
      utilises infra-red (IR) light. IR light is shined on patients' inverted eyelids and a special
      camera then allows visualisation of the structure of the meibomian glands, including the
      ducts and acini. Currently, images taken via non-contact meibography are manually analysed by
      a skilled clinician.

      Knowledge of the health of meibomian glands is useful, especially in the diagnosis of
      Meibomian Gland Dysfunction (MGD). Studies have shown that MGD is one of the most common
      causes of evaporative dry eye.

      Mediview 2.0 is a software that has been programmed to semi-automatically classify meibograph
      images taken via non-contact meibography. This software is collaboration with Shanghai
      MediWorks Precision Instruments Co., Ltd. The algorithm used was developed by a collaboration
      between Agency for Science, Technology and Research (A*STAR) and Singapore Eye Research
      Institute (SERI).

      This current study aims to validate the diagnostic accuracy of Mediview 2.0 in assessing the
      health of meibomian glands of patients, against a trained clinician. We aim to recruit 100
      participants for this study.

      Once this software is found to be valid, a trained technician could be taught how to capture
      the images and hence leaving the doctors with more time to focus on clinical assessment and
      treatment instead. Therefore, this study has the potential to increase efficiency in the
      clinics.
    

Detailed description: 
      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.

      Clinical importance Should this modality of imaging be shown to be repeatable, it can be
      incorporated into current protocol/workflow for assessment and monitoring of Meibomian Gland
      Dysfunction and treatment progress in our centre. The images captured in this project can
      also be used for validating further updates of the Mediworks software, since the hardware
      will remain unchanged in the future.

      Study Objectives and Purpose The primary purpose is to validate Mediview 2.0 software as an
      objective tool in assessing the health of meibomian glands.

      The specific aims include assessing a., the accuracy of Mediview 2.0 in classifying
      meibograph images (into Healthy and Unhealthy categories) and b., reproducibility of the
      classifications.

      Study design:

      Prospective study

      Rationale:

      Mediview 2.0 is a newly developed software and has yet been validated as a tool for
      assessment of the health of meibomian glands.

      Methods:

      Participants and target sample size 50 healthy volunteers and 50 patients aged 21 to 99 years
      will be recruited for this study. They are outpatients and accompanying relatives from the
      Singapore National Eye Centre. Permission would be sought from the attending doctors before
      subjects are being recruited. Informed written consent will be obtained from all
      participants.

      Visit schedules One visit is required for participants. In general, they will not have to
      come for additional visits if they are recruited whilst on a scheduled clinical appointment
      for other reasons. In exceptional cases, if the meibography cannot be performed on the same
      day, participants would be given the option of coming on another suitable day for this study.

      Duration of study:

      12 months.

      Procedures:

        1. Imaging of Meibomian Gland During the imaging of each eye, participants will be required
           to place their chins on a chin rest on the slit-lamp microscope. The upper eyelids of
           the participant will be in turn everted and an image of meibomian glands present on the
           eyelid will be captured. This is also performed for the lower eyelids. This is a
           non-invasive technique and the camera and IR lamp will not touch the patient. During
           each eversion of eyelid, optometrist will acquire 3 images at one time. Thereafter,
           Mediview 2.0 software will proceed to analyse the image into healthy and unhealthy
           glands.

           Throughout all measurements, all parameters of the meibographer, (such as light
           intensity, magnification of eyelid) will be kept constant.

        2. Analysis of Image of Meibomian Gland The images taken above will be exported and
           assessed by another optometrist who is unaware of the classification of the image by
           Mediview. This assessor will classify the images into healthy and unhealthy. Results
           between the Mediview 2.0 software and the optometrist will be assessed. Should there be
           any disagreement on the results, a 3rd clinician will be involved in the validation.

      Statistical analysis:

        1. Comparison between software classification and human classification Cohen's kappa
           coefficient will be calculated to assess the agreement between these two ratings. For
           interpretation of the calculated statistic, we use recommendations from Landis and Koch,
           who characterized values < 0 as indicating no agreement and 0-0.20 as slight, 0.21-0.40
           as fair, 0.41-0.60 as moderate, 0.61-0.80 as substantial, and 0.81-1 as almost perfect
           agreement.

        2. Comparison between the first and second software classifications Similarly we will use
           the kappa coefficient to evaluate the agreements between the first and second
           classifications made by the software.

      Expected outcomes:

      The classification of meibograph images from Mediview 2.0 should show good agreement with the
      assessment by the skilled clinician. Similarly classifications of the meibographs taken on
      the same day for the same eyes should show good agreement.

      Potential problems No potential problems are expected for this study.
    

Intervention name(s): MediView 2.0 software
Intervention type(s): Other
Intervention model: Single Group Assignment
Masking: None (Open Label)
Keywords: Dry Eyes; Meibomian glands; Algorithm; MediView software