Dry Eyes Clinical Trial
Official title:
Validating the Diagnostic Accuracy of Mediview 2.0 Software as an Image Analysis Tool of Meibomian Glands
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.
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.
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Intervention Model: Single Group Assignment, Masking: Open Label
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