Bragança Clerimar Paulo, Torres José Manuel, Macedo Luciano Oliveira, Soares Christophe Pinto de Almeida
ISUS Unit, Faculty of Science and Technology, University Fernando Pessoa, 4249-004 Porto, Portugal.
Department of Ophthalmology, Eye Hospital of Southern Minas Gerais State, Rua Joaquim Rosa 14, Itanhandu 37464-000, MG, Brazil.
Diagnostics (Basel). 2024 Mar 1;14(5):530. doi: 10.3390/diagnostics14050530.
The progress of artificial intelligence algorithms in digital image processing and automatic diagnosis studies of the eye disease glaucoma has been growing and presenting essential advances to guarantee better clinical care for the population. Given the context, this article describes the main types of glaucoma, traditional forms of diagnosis, and presents the global epidemiology of the disease. Furthermore, it explores how studies using artificial intelligence algorithms have been investigated as possible tools to aid in the early diagnosis of this pathology through population screening. Therefore, the related work section presents the main studies and methodologies used in the automatic classification of glaucoma from digital fundus images and artificial intelligence algorithms, as well as the main databases containing images labeled for glaucoma and publicly available for the training of machine learning algorithms.
人工智能算法在眼部疾病青光眼的数字图像处理和自动诊断研究方面取得了进展，并呈现出重要进展，以确保为民众提供更好的临床护理。在此背景下，本文描述了青光眼的主要类型、传统诊断形式，并介绍了该疾病的全球流行病学。此外，本文探讨了如何将使用人工智能算法的研究作为通过人群筛查辅助该疾病早期诊断的可能工具。因此，相关工作部分介绍了从数字眼底图像和人工智能算法自动分类青光眼所使用的主要研究和方法，以及包含为青光眼标注且可供机器学习算法训练的公开可用图像的主要数据库。
