Discussion
This study showed that there is no agreement in meiboscore and drop-out percentage between red filter meibography technique by the Samsung S9 and iPhone XR smartphones compared with infrared. However, there is a positive correlation between the meiboscore and the drop-out percentage using ImageJ on the red filter meibography technique by both types of smartphones, although the correlation value is weak. Furthermore, inter-rater and intrarater reliability for the meiboscore assessment of the red filter meibography technique by both types of smartphones showed no agreement to weak agreement between the two raters.
None to low agreement found in this study indicates that the red filter meibography technique is not comparable to the infrared method in displaying the Meibomian glands (figure 1). The images produced by red filter meibography using smartphones have shortcomings, including the presence of very bright light reflections that make the intact Meibomian glands look like areas with drop-outs (figure 1A1–C1). In addition, the visualisation of the meibomian glands in the upper lid is also more difficult than the lower lid. Meibomian gland contrast in the image produced by the red filter meibography also appears so low that it is indistinguishable from the surrounding area (figure 1A2–C2). Such difficulties were also found in study by Lee et al although they found moderate agreement in meiboscore between red filter and infrared meibography.7
Figure 1Results of meibography photos taken on research subjects. The top row shows the upper lid and the bottom row shows the bottom lid. (A1 and A2) Infrared meibography, (B1 and B2): Red filter meibography by smartphone 1 (Samsung S9), (C1 and C2) Red filter meibography by smartphone 2 (iPhone XR).
We encountered several limitations during implementation that may have caused suboptimal images. This includes the lack of characterisation of red light passing through the red filter due to the unavailability of spectroscopic equipment owned by the institution or nearby optical laboratories. We conclude that the red-filtered light source does not adequately reach the spectrum for visualising the meibomian glands. Research by Peral et al found that there are three optimal wavelengths for observation of Meibomian glands, namely at 600 nm (visible red light wavelength), 725 nm and 950 nm (infrared wavelength) and concluded that meibomian gland contrast decreases rapidly when the wavelength is less than 600 nm.10 The red filter used in this study may not fully absorb light other than red and infrared wavelengths, thus unabsorbed light with wavelengths lower than the red light spectrum disturbs the visualisation and contrast of meibomian glands.
Second, the smartphone camera sensor is also not optimal in capturing invisible infrared light because it is embedded with an anti-infrared optical filter. Smartphone cameras have a complementary metal-oxide-semiconductor detector that is sensitive to visible light and near-infrared (NIR) light, but the camera is equipped with a filter that blocks NIR wavelengths for ordinary photography because it will affect image quality.11
Lastly, settings contrast, brightness, colour composition on smartphone cameras are done automatically by software so that the visualisation of the meibomian glands is not optimal. Infrared meibography is also difficult to assess if the resulting image is low in contrast, the illumination is not uniform and the gland area is out of focus.12
However, there are several things that can be done to overcome the lack of quality of the photos mentioned above for further research. First, using a tool capable of performing infrared imaging or often referred to as NIR optical imaging. Several studies on smartphone-based medical imaging use NIR imaging techniques. Meibography research using alternative sources of infrared light, among others, was carried out by Osae et al with a simple infrared video camera usually used for closed circuit television and by Wang et al who also added an infrared camera module to Android-based smartphones.13 14 Research by Osae et al stated that the use of a homemade meibographer was able to depict meibomian glands and could be used in developing countries with limited access to complex and expensive imaging systems.13 Research by Wang et al uses a 2-megapixel infrared camera module which has a lamp array with a light wavelength of 850 nm and is connected to an Android-based smartphone.14 The smaller size is said to be able to visualise the meibomian glands quickly and more easily.14 However, these two studies did not compare the results of the images with standard meibography.
We found that inter-reliability scores showed low agreement in assessing images produced by infrared meibography. This may be due to the difficulty of using meiboscore in MG drop-out scoring. When compared with several meibography with different percentages of atrophy, the images near the grading transition limits (0%, 33% and 66%) are very similar and difficult to classify.15 Meiboscore method was interesting because of its simplicity but it is the biggest drawback because it fails to take into account changes in the meibomian glands before drop-out occurs.16 The use of meiboscore for subjective assessment of MG drop-out is difficult for untrained raters and discussion of raters is needed to reach a good agreement.
The new meibography method with smartphones still has hope for improvement and room for innovation. The use of the red filter in this study was able to display the meibomian glands in a relatively easy and inexpensive way, although the visualisation was not optimal for grading drop-outs both subjectively and with the help of computer applications. Future research can use more optimal methods in visualising the meibomian glands, for example, using additional cameras and infrared sensors on smartphones. The image quality is expected to improve with this method so that later the image can be segmented automatically by a computer, as in the research by Celik et al and Koh et al.17 18 In the near future, processing and analysis of meibography images can also be carried out by artificial intelligence systems which offer various advantages such as reduced time for analysis, increased diagnostic efficiency and help to overcome intrarater and inter-rater variability of subjective assessment by clinicians.12 15 19 Then, the use of smartphones as a diagnostic tool for point-of-care test is also very promising and important to be studied further because it is compact, portable and relatively inexpensive.20 Despite the many weaknesses in this study, it is hoped to provide the knowledge that there is an alternative to meibomian gland visualisation if infrared meibography is not available and forms the basis for further studies of MGD.