Wan Zaki Wan Mimi Diyana, Abdul Mutalib Haliza, Ramlan Laily Azyan, Hussain Aini, Mustapha Aouache
Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia.
Optometry and Vision Science Programme, Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Jalan Raja Muda Abdul Aziz, Kuala Lumpur 50300, Malaysia.
J Imaging. 2022 Feb 10;8(2):41. doi: 10.3390/jimaging8020041.
Advances in computing and AI technology have promoted the development of connected health systems, indirectly influencing approaches to cataract treatment. In addition, thanks to the development of methods for cataract detection and grading using different imaging modalities, ophthalmologists can make diagnoses with significant objectivity. This paper aims to review the development and limitations of published methods for cataract detection and grading using different imaging modalities. Over the years, the proposed methods have shown significant improvement and reasonable effort towards automated cataract detection and grading systems that utilise various imaging modalities, such as optical coherence tomography (OCT), fundus, and slit-lamp images. However, more robust and fully automated cataract detection and grading systems are still needed. In addition, imaging modalities such as fundus, slit-lamps, and OCT images require medical equipment that is expensive and not portable. Therefore, the use of digital images from a smartphone as the future of cataract screening tools could be a practical and helpful solution for ophthalmologists, especially in rural areas with limited healthcare facilities.
计算和人工智能技术的进步推动了互联健康系统的发展，间接影响了白内障治疗方法。此外，由于使用不同成像方式进行白内障检测和分级的方法不断发展，眼科医生能够做出具有显著客观性的诊断。本文旨在综述已发表的使用不同成像方式进行白内障检测和分级的方法的发展及局限性。多年来，所提出的方法在利用各种成像方式（如光学相干断层扫描（OCT）、眼底和裂隙灯图像）的自动白内障检测和分级系统方面取得了显著进展，并付出了合理努力。然而，仍需要更强大、完全自动化的白内障检测和分级系统。此外，眼底、裂隙灯和OCT图像等成像方式需要昂贵且不可携带的医疗设备。因此，将智能手机拍摄的数字图像用作未来的白内障筛查工具，对于眼科医生来说可能是一个切实可行且有帮助的解决方案，尤其是在医疗设施有限的农村地区。
