Abdani Siti Raihanah, Zulkifley Mohd Asyraf, Shahrimin Mohamad Ibrani, Zulkifley Nuraisyah Hani
Faculty of Humanities, Management and Science, Universiti Putra Malaysia (Bintulu Campus), Bintulu 97008, Sarawak, Malaysia.
Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia.
Diagnostics (Basel). 2022 Mar 5;12(3):639. doi: 10.3390/diagnostics12030639.
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.
翼状胬肉是一种导致纤维血管组织向角膜区域生长的眼部疾病。在早期，除了给患者带来轻微不适外，它并不是一种有害的病症。然而，一旦组织侵入角膜区域，就会开始影响患者的视力，如果长入瞳孔区域，影响会更严重。因此，需要尽早识别这种病症以阻止其生长，可使用简单的眼药水和太阳镜。这种病症的相关风险因素之一是教育水平低，这就解释了为什么大多数患者不了解这种病症。因此，开发一种基于手机摄像头等简单成像方式的自动化翼状胬肉筛查系统很重要，这样许多人都可以进行评估。在自动化翼状胬肉筛查系统开发的早期阶段，支持向量机和人工神经网络等传统机器学习技术是检测翼状胬肉组织存在的实际算法。然而，随着深度学习时代的到来，加上有大量的训练数据，深度学习网络在翼状胬肉病症筛查中取代了传统网络。深度学习网络已成功用于三个主要目的，即对图像进行分类以确定是否存在翼状胬肉组织、通过目标检测方法定位病变组织以及在像素级别对病变组织进行语义分割。这篇综述文章总结了自动化翼状胬肉筛查系统的类型、严重程度、风险因素和现有技术水平。本文还讨论了一些用于分类和分割任务的可用数据集。总之，计算机辅助翼状胬肉筛查系统将造福全世界许多人，特别是提醒他们有可能患有这种病症，以便在早期就可以给出预防措施建议。
