{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T04:47:30Z","timestamp":1787978850177,"version":"build-2784847793"},"reference-count":53,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,3,5]],"date-time":"2022-03-05T00:00:00Z","timestamp":1646438400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004515","name":"Universiti Kebangsaan Malaysia","doi-asserted-by":"publisher","award":["GUP-2019-008"],"award-info":[{"award-number":["GUP-2019-008"]}],"id":[{"id":"10.13039\/501100004515","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Ministry of Higher Education Malaysia","award":["FRGS\/1\/2019\/ICT02\/UKM\/02\/1"],"award-info":[{"award-number":["FRGS\/1\/2019\/ICT02\/UKM\/02\/1"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Pterygium is an eye condition that causes the fibrovascular tissues to grow towards the corneal region. At the early stage, it is not a harmful condition, except for slight discomfort for the patients. However, it will start to affect the eyesight of the patient once the tissues encroach towards the corneal region, with a more serious impact if it has grown into the pupil region. Therefore, this condition needs to be identified as early as possible to halt its growth, with the use of simple eye drops and sunglasses. One of the associated risk factors for this condition is a low educational level, which explains the reason that the majority of the patients are not aware of this condition. Hence, it is important to develop an automated pterygium screening system based on simple imaging modalities such as a mobile phone camera so that it can be assessed by many people. During the early stage of automated pterygium screening system development, conventional machine learning techniques such as support vector machines and artificial neural networks are the de facto algorithms to detect the presence of pterygium tissues. However, with the arrival of the deep learning era, coupled with the availability of large training data, deep learning networks have replaced the conventional networks in screening for the pterygium condition. The deep learning networks have been successfully implemented for three major purposes, which are to classify an image regarding whether there is the presence of pterygium tissues or not, to localize the lesion tissues through object detection methodology, and to semantically segment the lesion tissues at the pixel level. This review paper summarizes the type, severity, risk factors, and existing state-of-the-art technology in automated pterygium screening systems. A few available datasets are also discussed in this paper for both classification and segmentation tasks. In conclusion, a computer-assisted pterygium screening system will benefit many people all over the world, especially in alerting them to the possibility of having this condition so that preventive actions can be advised at an early stage.<\/jats:p>","DOI":"10.3390\/diagnostics12030639","type":"journal-article","created":{"date-parts":[[2022,3,6]],"date-time":"2022-03-06T20:35:50Z","timestamp":1646598950000},"page":"639","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Computer-Assisted Pterygium Screening System: A Review"],"prefix":"10.3390","volume":"12","author":[{"given":"Siti Raihanah","family":"Abdani","sequence":"first","affiliation":[{"name":"Faculty of Humanities, Management and Science, Universiti Putra Malaysia (Bintulu Campus), Bintulu 97008, Sarawak, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4010-3990","authenticated-orcid":false,"given":"Mohd Asyraf","family":"Zulkifley","sequence":"additional","affiliation":[{"name":"Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8836-9042","authenticated-orcid":false,"given":"Mohamad Ibrani","family":"Shahrimin","sequence":"additional","affiliation":[{"name":"Faculty of Humanities, Management and Science, Universiti Putra Malaysia (Bintulu Campus), Bintulu 97008, Sarawak, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nuraisyah Hani","family":"Zulkifley","sequence":"additional","affiliation":[{"name":"Community Health Department, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"769","DOI":"10.1097\/ICO.0b013e31806457b8","article-title":"Plica semilunaris temporal ectopia: Evidence of primary nasal pterygia traction","volume":"26","author":"Denion","year":"2007","journal-title":"Cornea"},{"key":"ref_2","first-page":"54","article-title":"Understanding and managing pterygium","volume":"29","author":"Hall","year":"2016","journal-title":"Community Eye Health"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e003787","DOI":"10.1136\/bmjopen-2013-003787","article-title":"Geographical prevalence and risk factors for pterygium: A systematic review and meta-analysis","volume":"3","author":"Liu","year":"2013","journal-title":"BMJ Open"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"34563","DOI":"10.1007\/s11042-019-08130-x","article-title":"Pterygium-Net: A deep learning approach to pterygium detection and localization","volume":"78","author":"Zulkifley","year":"2019","journal-title":"Multimed. Tools Appl."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"153","DOI":"10.4103\/2008-322X.183917","article-title":"Correlations between histopathologic changes and clinical features in pterygia","volume":"11","author":"Safi","year":"2016","journal-title":"J. Ophthalmic Vis. Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2303","DOI":"10.1007\/s10792-017-0722-0","article-title":"Validity of a new comprehensive pterygia grading scale for use in clinical research and clinical trial","volume":"38","author":"Huang","year":"2018","journal-title":"Int. Ophthalmol."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Abdani, S.R., Zulkifley, M.A., and Zulkifley, N.H. (2021). Group and Shuffle Convolutional Neural Networks with Pyramid Pooling Module for Automated Pterygium Segmentation. Diagnostics, 11.","DOI":"10.3390\/diagnostics11061104"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"6617","DOI":"10.1167\/iovs.11-8947","article-title":"Prevalence of and risk factors for pterygium in rural adult chinese populations of the Bai nationality in Dali: The Yunnan Minority Eye Study","volume":"53","author":"Zhong","year":"2012","journal-title":"Investig. Ophthalmol. Vis. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"658648","DOI":"10.1155\/2014\/658648","article-title":"Prevalence and risk factors for pterygium in rural older adults in Shandong Province of China: A cross-sectional study","volume":"2014","author":"Jiao","year":"2014","journal-title":"Biomed Res. Int."},{"key":"ref_10","first-page":"1","article-title":"Prevalence and associated factors for pterygium in Han and Mongolian adults: A cross-sectional study in inner Mongolian, China","volume":"20","author":"Wang","year":"2020","journal-title":"BMC Ophthalmol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"270","DOI":"10.4103\/jovr.jovr_85_16","article-title":"Risk factors for pterygium in Ilam Province, Iran","volume":"12","author":"Malekifar","year":"2017","journal-title":"J. Ophthalmic Vis. Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"977","DOI":"10.1136\/bjo.2008.150847","article-title":"The prevalence of and risk factors for pterygium in an urban Malay population: The Singapore Malay Eye Study (SiMES)","volume":"94","author":"Tong","year":"2010","journal-title":"Br. J. Ophthalmol."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Fekadu, S.A., Assem, A.S., and Adimassu, N.F. (2020). Prevalence of pterygium and its associated factors among adults aged 18 years and above in Gambella town, Southwest Ethiopia, May 2019. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0237891"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1287","DOI":"10.1136\/bjo.2008.152694","article-title":"Prevalence of pterygium in Latinos: Proyecto VER","volume":"93","author":"West","year":"2009","journal-title":"Br. J. Ophthalmol."},{"key":"ref_15","first-page":"78","article-title":"Current concepts in management of pterygium","volume":"25","author":"Bahuva","year":"2015","journal-title":"Off. Sci. J. Delhi Ophthalmol. Soc."},{"key":"ref_16","first-page":"111","article-title":"Management of double-head pterygium with conjunctival autograft and primary closure","volume":"13","author":"Rock","year":"2020","journal-title":"Int. Med Case Rep. J."},{"key":"ref_17","unstructured":"Aminlari, A., Singh, R., and Liang, D. (2021). Management of Pterygium, EyeNet Magazine."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.jtos.2014.01.001","article-title":"Surgical management of pterygium","volume":"12","author":"Janson","year":"2014","journal-title":"Ocul. Surf."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.cmpb.2017.10.026","article-title":"Automated pterygium detection method of anterior segment photographed images","volume":"154","author":"Zaki","year":"2018","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zulkifley, M.A., Mustafa, M.M., Hussain, A., Mustapha, A., and Ramli, S. (2014). Robust identification of polyethylene terephthalate (PET) plastics through bayesian decision. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0114518"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"101552","DOI":"10.1016\/j.media.2019.101552","article-title":"Generative adversarial network in medical imaging: A review","volume":"58","author":"Yi","year":"2019","journal-title":"Med Image Anal."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Fang, X., Deshmukh, M., Chee, M.L., Soh, Z.D., Teo, Z.L., Thakur, S., Goh, J.H.L., Liu, Y.C., Husain, R., and Mehta, J. (2021). Deep learning algorithms for automatic detection of pterygium using anterior segment photographs from slit-lamp and hand-held cameras. Br. J. Ophthalmol., 1136.","DOI":"10.1136\/bjophthalmol-2021-318866"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1093\/pcmedi\/pbab009","article-title":"EyeHealer: A large-scale anterior eye segment dataset with eye structure and lesion annotations","volume":"4","author":"Cai","year":"2021","journal-title":"Precis. Clin. Med."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2514","DOI":"10.1109\/TMI.2018.2837502","article-title":"Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: Is the problem solved?","volume":"37","author":"Bernard","year":"2018","journal-title":"IEEE Trans. Med Imaging"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1015","DOI":"10.17576\/jsm-2020-4905-06","article-title":"Reliability of Pterygium Redness Grading Software (PRGS) Types of Primary Pterygia Based on Appearance","volume":"49","author":"Hilmi","year":"2020","journal-title":"Sains Malays."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1235","DOI":"10.1001\/archopht.1997.01100160405001","article-title":"Effect of pterygium morphology on pterygium recurrence in a controlled trial comparing conjunctival autografting with bare sclera excision","volume":"115","author":"Tan","year":"1997","journal-title":"Arch. Ophthalmol."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Mesquita, R.G., and Figueiredo, E.M. (2012, January 25\u201330). An algorithm for measuring pterygium\u2019s progress in already diagnosed eyes. Proceedings of the 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Kyoto, Japan.","DOI":"10.1109\/ICASSP.2012.6287988"},{"key":"ref_28","unstructured":"Gao, X., Wong, D.W.K., Aryaputera, A.W., Sun, Y., Cheng, C.Y., Cheung, C., and Wong, T.Y. (September, January 28). Automatic pterygium detection on cornea images to enhance computer-aided cortical cataract grading system. Proceedings of the 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, San Diego, CA, USA."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1007\/s10384-018-0583-8","article-title":"Detection of increase in corneal irregularity due to pterygium using Fourier series harmonic analyses with multiple diameters","volume":"62","author":"Minami","year":"2018","journal-title":"Jpn. J. Ophthalmol."},{"key":"ref_30","first-page":"2378","article-title":"Benchmarked pterygium images for human and machine graders","volume":"11","author":"Azemin","year":"2016","journal-title":"J. Eng. Appl. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"6211006","DOI":"10.1155\/2021\/6211006","article-title":"Postsurgery Classification of Best-Corrected Visual Acuity Changes Based on Pterygium Characteristics Using the Machine Learning Technique","volume":"2021","author":"Jais","year":"2021","journal-title":"Sci. World J."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1016\/j.optom.2019.04.001","article-title":"Corneo-pterygium total area measurements utilising image analysis method","volume":"12","author":"Radzi","year":"2019","journal-title":"J. Optom."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Abdani, S.R., Zulkifley, M.A., and Moubark, A.M. (2020, January 18\u201319). Pterygium Tissues Segmentation using Densely Connected DeepLab. Proceedings of the 2020 IEEE 10th Symposium on Computer Applications Industrial Electronics (ISCAIE), Malaysia.","DOI":"10.1109\/ISCAIE47305.2020.9108822"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Lopez, Y.P., and Aguilera, L.R. (2019). Automatic classification of pterygium-non pterygium images using deep learning. ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing, Springer.","DOI":"10.1007\/978-3-030-32040-9_40"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Abdani, S.R., Zulkifley, M.A., and Hussain, A. (2019, January 17\u201319). Compact convolutional neural networks for pterygium classification using transfer learning. Proceedings of the 2019 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), Kuala Lumpur, Malaysia.","DOI":"10.1109\/ICSIPA45851.2019.8977757"},{"key":"ref_36","first-page":"1929","article-title":"Dropout: A Simple Way to Prevent Neural Networks from Overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Chatfield, K., Simonyan, K., Vedaldi, A., and Zisserman, A. (2014). Return of the Devil in the Details: Delving Deep into Convolutional Networks. arXiv.","DOI":"10.5244\/C.28.6"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"7651462","DOI":"10.1155\/2021\/7651462","article-title":"Research on an Intelligent Lightweight-Assisted Pterygium Diagnosis Model Based on Anterior Segment Images","volume":"2021","author":"Zheng","year":"2021","journal-title":"Dis. Markers"},{"key":"ref_39","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv."},{"key":"ref_40","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). ImageNet Classification with Deep Convolutional Neural Networks. Proceedings of the 25th International Conference on Neural Information Processing Systems\u2014Volume 1, Lake Tahoe, Nevada."},{"key":"ref_41","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition, University of Oxford. Technical Report."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 7\u201312). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"759229","DOI":"10.3389\/fpsyg.2021.759229","article-title":"Implementation and Application of an Intelligent Pterygium Diagnosis System Based on Deep Learning","volume":"12","author":"Xu","year":"2021","journal-title":"Front. Psychol."},{"key":"ref_44","unstructured":"Tan, M., and Le, Q. (2019, January 9\u201315). Efficientnet: Rethinking model scaling for convolutional neural networks. Proceedings of the 36th International Conference on Machine Learning, Long Beach, CA, USA."},{"key":"ref_45","unstructured":"Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A.L. (2014). Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs. arXiv."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs","volume":"40","author":"Chen","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Jegou, S., Drozdzal, M., Vazquez, D., Romero, A., and Bengio, Y. (2017, January 21\u201326). The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.156"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21\u201326). Pyramid Scene Parsing Network. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_49","unstructured":"Sun, K., Zhao, Y., Jiang, B., Cheng, T., Xiao, B., Liu, D., Mu, Y., Wang, X., Liu, W., and Wang, J. (2019). High-resolution representations for labeling pixels and regions. arXiv."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"3244","DOI":"10.1364\/BOE.9.003244","article-title":"DRUNET: A dilated-residual U-Net deep learning network to segment optic nerve head tissues in optical coherence tomography images","volume":"9","author":"Devalla","year":"2018","journal-title":"Biomed. Opt. Express"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_52","unstructured":"Ferrari, V., Hebert, M., Sminchisescu, C., and Weiss, Y. (2018). Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. Computer Vision\u2013ECCV 2018, Springer International Publishing."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Zulkifley, M.A., Abdani, S.R., and Zulkifley, N.H. (2020). COVID-19 Screening using a Lightweight Convolutional Neural Networks with Generative Adversarial Network Data Augmentation. Symmetry, 12.","DOI":"10.3390\/sym12091530"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/12\/3\/639\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:32:38Z","timestamp":1760135558000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/12\/3\/639"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,5]]},"references-count":53,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["diagnostics12030639"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics12030639","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,5]]}}}