Sains Malaysiana 49(5)(2020): 1015-1020

http://dx.doi.org/10.17576/jsm-2020-4905-06

Reliability of Pterygium Redness Grading Software (PRGS) in Describing Different Types of Primary Pterygia Based on Appearance

(Kebolehpercayaan Perisian Penggredan Kemerahan Pterigium (PRGS) dalam Mengelaskan Pelbagai Jenis Pterigium Berdasarkan Perawakan)

MOHD RADZI HILMI¹, MOHD ZULFAEZAL CHE AZEMIN¹, KHAIRIDZAN MOHD KAMAL²*, AZRIN ESMADY ARIFFIN³, MUHAMMAD AFZAM SHAH ABDUL RAHIM¹ & MOHD IZZUDDIN MOHD TAMRIN⁴

¹Department of Optometry and Vision Science, Kulliyyah of Allied Health Sciences, International Islamic University Malaysia (IIUM), 25200 Kuantan, Pahang Darul Makmur, Malaysia

²Department of Ophthalmology, Kulliyyah of Medicine, International Islamic University Malaysia (IIUM), 25200 Kuantan, Pahang Darul Makmur, Malaysia

³Faculty of Optometry and Vision Science, SEGi University, 47810 Petaling Jaya, Selangor Darul Ehsan, Malaysia

⁴Department of Information Systems, Kulliyyah of Information and Communication Technology, International Islamic University Malaysia (IIUM), 53100 Gombak, Selangor Darul Ehsan, Malaysia

Received: 27 March 2019/Accepted: 15 January 2020

ABSTRACT

The aim of this study was to evaluate the reliability of Pterygium Redness Grading Software (PRGS) in describing different types of primary pterygia. Ninety-three participants with primary pterygia who visited an ophthalmology clinic were recruited in this study. PRGS is a semi-automated computer program used to measure fibrovascular pterygium redness by analysing digital images of the pterygium and grading it on a continuous scale of 1 (minimum redness) to 3 (maximum redness). An ocular surface expert graded all 93 images in random order. The reliability of PRGS was determined by comparing pterygium redness measured using the software and by the expert. The mean and standard deviation of redness of the pterygium fibrovascular images measured using PRGS and by the expert were 1.81 ± 0.58 and 1.73 ± 0.61, respectively (P = 0.396). A comparative analysis based on pterygium type showed an increase in redness according to pterygium type (Type I: 1.43 ± 0.32; Type II: 1.67 ± 0.55; and Type III: 2.31 ± 0.46), without significant differences compared to redness measured by the expert (Type I: 1.38 ± 0.34; Type II: 1.78 ± 0.62; and Type III: 2.02 ± 0.66) (all P > 0.05). PRGS could describe and classify pterygia according to their redness, and PRGS-based classification was in agreement with the established classification of pterygia. Therefore, PRGS can be used in addition to the existing pterygium grading system.

Keywords: Automate; morphology; pterygium; redness; translucence

ABSTRAK

Matlamat kajian ini adalah untuk menilai kebolehpercayaan Perisian Penggredan Kemerahan Pterygium (PRGS) dalam mengelaskan jenis-jenis pterigium primer. Kajian ini berjaya merekrut 93 pesakit daripada klinik oftalmologi yang menghidap pterigium primer. PRGS merupakan program komputer semi-automatik yang berfungsi untuk mengukur darjah kemerahan pterigium fibrovaskular yang diperoleh daripada imej digital pterigium, dalam bentuk pengredan berterusan (1 untuk kemerahan minimum dan 3 untuk kemerahan maksimum). Kesemua 93 imej pterigium telah digredkan secara rambang oleh pakar permukaan okul. Kebolehpercayaan PRGS telahpun ditentukan dengan membandingkannya dengan kemerahan yang dicerap oleh pakar. Nilai min dan sisihan piawai untuk kemerahan pterigium fibrovaskular adalah 1.81 ± 0.58 (PRGS) dan 1.73 ± 0.61 (pakar), (P = 0.396). Analisis berasaskan jenis pterigium menunjukkan terdapat peningkatan kemerahan pterigium fibrovaskular apabila diukur menggunakan PRGS (Jenis I: 1.43 ± 0.32; Jenis II: 1.67 ± 0.55; Jenis III: 2.31 ± 0.46) berbanding pakar (Jenis I: 1.38 ± 0.34; Jenis II: 1.78 ± 0.62; Jenis III: 2.02 ± 0.66), tetapi perbezaan ini tidak signifikan untuk semua jenis pterigium (P > 0.05). Skala pengredan PRGS dapat mengelaskan pterigium berdasarkan kemerahan dan ia selaras dengan pengelasan pterigium sedia ada. Skala kemerahan ini boleh digunakan sebagai tambahan kepada pengredan pterigium yang sedia ada.

Kata kunci: Automatik; kemerahan; morfologi; pterigium; translusen

REFERENCES

Altan-Yaycioglu, R., Kucukerdonmez, C., Karalezli, A., Corak, F. & Akova, Y.A. 2013. Astigmatic changes following pterygium removal: Comparison of 5 different methods. Indian J. Ophthalmol. 61: 104-108.

Amparo, F., Yin, J., Di Zazzo, A., Abud, T., Jurkunas, U.V., Hamrah, P. & Dana, R. 2017. Evaluating changes in ocular redness using a novel automated method. Transl. Vis. Sci. Technol. 6(4): 13. doi: 10.1167/tvst.6.4.13.

Azemin, M.Z.C., Hilmi, M.R. & Kamal, M.K. 2014. Supervised pterygium fibrovascular redness grading using generalized regression neural network. In New Trends in Software Methodologies, Tools and Techniques, edited by Fujita, H. Amsterdam: IOS Press.

Bradley, J.C., Yang, W., Bradley, R.H., Reid, T.W. & Schwab, I.R. 2010. The science of pterygia. Br. J. Ophthalmol. 94(7): 815-820.

Che Azemin, M.Z., Mohd Tamrin, M.I., Hilmi, M.R. & Mohd Kamal, K. 2015. GLCM texture analysis on different color space for pterygium grading. ARPN J. Eng. Appl. Sci. 10: 6410-6413.

Che Azemin, M.Z., Hilmi, M.R., Mohd Tamrin, M.I. & Mohd Kamal, K. 2014. Fibrovascular redness grading using Gaussian process regression with radial basis function kernel. In 2014 IEEE Conference Biomedical Engineering and Sciences (IECBES). pp. 113-116.

Chui, J., Coroneo, M.T., Tat, L.T., Crouch, R., Wakefield, D. & DiGirolamo, N. 2011. Ophthalmic pterygium: A stem cell disorder with premalignant features. Am. J. Pathology 178(2): 817-827.

Chun, Y.S., Yoon, W.B., Kim, K.G. & Park, I.K. 2014. Objective assessment of corneal staining using digital image analysis. Invest. Ophthalmol. Vis. Sci. 55(12): 7896-7903.          

Downie, L.E., Keller, P.R. & Vingrys, A.J. 2016. Assessing ocular bulbar redness: A comparison of methods. Ophthalmic. Physiol. Opt. 36(2): 132-139.

Džunic, B., Jovanović, P., Veselinović, D., Petrović, A., Stefanović, I. & Kovaćević, I. 2010. Analysis of pathohistological characteristics of pterygium. Bosn. J. of Basic Med. Sci. 10(4): 307-313.

Efron, N. 1998. Grading scales for contact lens complications. Ophthalmic. Physiol. Opt. 18: 182-186.

Farhood, Q.K. & Kareem, A.A. 2012. Pterygium and induced astigmatism. Kufa Med. Journal 15(11): 341-345.

Fieguth, P. & Simpson, T. 2002. Automated measurement of bulbar redness. Invest. Ophthalmol. Vis. Sci. 43: 340-347.

Gumus, K., Erkilic, K., Topaktas, D. & Colin, J. 2011. Effect of pterygia on refractive indices, corneal topography, and ocular aberrations. Cornea 30(1): 24-29.

Hilmi, M.R., Khairidzan, M.K., Azemin, M.Z.C., Azami, M.H. & Ariffin, A.E. 2019. Corneopterygium total area measurements utilizing image analysis method. J. Optom. 12(4): 272-277.

Hilmi, M.R., Khairidzan, M.K., Azemin, M.Z.C., Azami, M.H. & Ariffin, A.E. 2018. Measurement of contrast sensitivity using the M&S Smart System II compared with the standard Pelli–Robson Chart in patients with primary pterygium. Makara. J. Health Res. 22(3): 167-171.

Jiang, H., Zhong, J., DeBuc, D.C., Tao, A., Xu, Z., Lam, B.L, Liua, C. & Wang, J. 2014. Functional slit lampbiomicroscopy for imaging bulbar conjunctival microvasculature in contact lens wearers. Microvasc. Res. 92: 62-71.

Kheirkhah, A., Safi, H., Nazari, R., Kaghazkanani, R., Hashemi, H. & Behrouz, M.J. 2012a. Effects of pterygium surgery on front and back corneal astigmatism. Int. Ophthalmol. 32: 251-257.

Kheirkhah, A., Safi, H., Molaei, S., Nazari, R., Behrouz, M.J. & Raju, V.K. 2012b. Effects of pterygium surgery on front and back corneal surfaces and anterior segment parameters. Can. J. Ophthalmol. 47(5): 423-428.

Manzar, N. & Mahar, P.S. 2013. Pterygium recurrence related to its size and corneal involvement. J. Coll. Physicians Surg. Pak. 23(2): 120-123.

Modenese, A. & Gobba, F. 2017. Occupational exposure to solar radiation at different latitudes and pterygium: A systematic review of the last 10 years of scientific literature. Int. J. Environ. Res. Public Health. 15(1): E37. doi: 10.3390/ijerph15010037.

Mohd Radzi, H., Khairidzan, M.K., Ariffin, A.E., Norazmar, N.A., Maruziki, N.N., Musa, N.H., Nasir, M.S., Azemin, M.Z.C., Azami, M.H. & Abdul Rahim, M.A.S. 2020. Effects of different types of primary pterygium on changes in oculovisual function. Sains Malaysiana 49(2): 383-388.

Mohd Radzi, H., Mohd Zulfaezal, C.A., Khairidzan, M.K., Mohd Izzuddin, M.T., Norfazrina, A.G. & Tengku Mohd, T.S. 2017. Prediction of changes in visual acuity and contrast sensitivity function by tissue redness after pterygium surgery. Curr. Eye Res. 42: 852-856.

Mohammad-Salih, P.A.K. & Sharif, A.F.M.D. 2008. Analysis of pterygium size and induced corneal astigmatism. Cornea 27: 434-438.

Oellers, P., Karp, C.L., Sheth, A., Kao, A.A., Abdelaziz, A., Matthews, J.L., Dubovy, S.R. & Galor, A. 2013. Prevalence, treatment, and outcomes of coexistent ocular surface squamous neoplasia and pterygium. Ophthalmology 120: 445-450.

Papas, E.B. 2000. Key factors in the subjective and objective assessment of conjunctival erythema. Invest. Ophthal. Vis. Sci. 41: 687-691.

Park, Y.M., Kim, C.D. & Lee, J.S. 2015. Effect of bevacizumab on human tenon’s fibroblasts cultured from primary and recurrent pterygium. Korean J. Physiol. Pharmacol. 19(4): 357-363.

Peterson, R.C. & Wolffsohn, J.S. 2009. Objective grading of the anterior eye. Optom. Vis. Sci. 86: 273-278.

Qin, Z., Fu, Q., Zhang, L., Yin, H., Jin, X., Tang, Q., Danni, L. & Yao, K. 2016. Proliferative effects of histamine on primary human pterygium fibroblasts. Mediators Inflamm. 2016: 9862496.

Ribatti, D., Nico, B., Perra, M.T., Maxia, C., Piras, F., Murtas, D., Crivellato, E. & Sirigu, P. 2009. Correlation between NGF/TrkA and microvascular density in human pterygium. Int. J. Exp. Pathology. 90(6): 615-620.

Ribatti, D., Nico, B., Maxia, C., Longo, V., Murtas, D., Mangieri, D., Perra, M.T., De Giorgis, M., Piras, F., Crivellato, E. & Sirigub, P. 2007. Neovascularization and mast cells with tryptase activity increase simultaneously in human pterygium. J. Cell. Molec. Med. 11(3): 585-589.

Romano, V., Steger, B., Brunner, M., Kaye, A., Zheng, Y. & Willoughby, C.E. 2017. Detecting change in conjunctival hyperemia using a pixel densitometry index. Ocul. Immunol. Inflamm. pp: 1-6.

Sánchez, B.M.L., Rodríguez, B.N., González, A.M., Evans, K. & Pena-Verdeal, H. 2016. Defining the optimal region of interest for hyperemia grading in the bulbar conjunctiva. Comput. Math. Methods Med. 2016: 3695014. doi: 10.1155/2016/3695014.

Schulze, M.M., Hutchings, N. & Simpson, T.L. 2011. Grading bulbar redness using cross calibrated clinical grading scales. Invest. Ophthalmol. Vis. Sci. 52: 5812-5817.

Schulze, M.M., Hutchings, N. & Simpson, T.L. 2009. The perceived bulbar redness of clinical grading scales. Optom. Vis. Sci. 84: 1250-1258.

Schulze, M.M., Hutchings, N. & Simpson, T.L. 2008. The use of fractal analysis and photometry to estimate the accuracy of bulbar redness grading scales. Optom. Vis. Sci. 49: 1398-1406.

Schulze, M.M., Jones, D.A. & Simpson, T.L. 2007. The development of validated bulbar redness grading scales. Optom. Vis. Sci. 84: 976-983.

Sha, X., Wen, Y., Liu, Z., Song, L., Peng, J. & Xie, L. 2014. Inhibition of α-smooth muscle actin expression and migration of pterygium fibroblasts by coculture with amniotic mesenchymal stem cells. Curr. Eye Res. 39(11): 1081-1089.

Sumi, T., Yoneda, T., Fukuda, K., Hoshikawa, Y., Kobayashi, M., Yanagi, M. Kiuchi, Y., Yasumitsu-Lovell, K. & Fukushima, A. 2013. Development of automated conjunctival hyperemia analysis software. Cornea 32: 52-59.

Tan, D.T., Chee, S.P., Dear, K.B. & Lim, A.S. 1997. Effect of pterygium morphology on pterygium recurrence in a controlled trial comparing conjunctival autografting with bare sclera excision. Arch. Ophthalmol. 115: 1235-1240.

Touhami, A., Di Pascuale, M.A., Kawatika, T., Del Valle, M., Rosa Jr., R.H., Duvony, S. & Tseng, S.C.G. 2005. Characterisation of myofibroblasts in fibrovascular tissues of primary and recurrent pterygia. Br. J. Ophthalmol. 89(3): 269-274.

Urbaniak, G.C. & Plous, S. 2013. Research Randomizer (Version 4.0)[computer software]. http://www.randomizer.org/. Accessed on June 19 2013.

Vives, P.P., Castanheira, A.M.C.M., Mora, G.J., Margarit, S.L., Encina, D.M. & Garcia, I.S. 2013. Topographic corneal changes in astigmatism due to pterygium’s limbal-conjunctival autograft surgery. J. Emmetropia. 4: 13-18.

Wolffsohn, J.S. & Purslow, C. 2003. Clinical monitoring of ocular physiology using digitalimage analysis. Cont. Lens Anterior Eye 26(1): 27-35.

Wolffsohn, J.S. 2004. Incremental nature of anterior eye grading scales determined by objective image analysis. Br. J. Ophthalmol. 88: 1434-1438.

Zhao, W.J., Duan, F., Li, Z.T., Yang, H.J., Huang, Q. & Wu, K.L. 2014. Evaluation of regional bulbar redness using an image-based objective method. Int. J. Ophthalmol. 7(1): 71-76.

*Corresponding author; email: khairidzan@gmail.com