Camara José, Neto Alexandre, Pires Ivan Miguel, Villasana María Vanessa, Zdravevski Eftim, Cunha António
R. Escola Politécnica, Universidade Aberta, 1250-100 Lisboa, Portugal.
Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ciência, 3200-465 Porto, Portugal.
J Imaging. 2022 Jan 20;8(2):19. doi: 10.3390/jimaging8020019.
Artificial intelligence techniques are now being applied in different medical solutions ranging from disease screening to activity recognition and computer-aided diagnosis. The combination of computer science methods and medical knowledge facilitates and improves the accuracy of the different processes and tools. Inspired by these advances, this paper performs a literature review focused on state-of-the-art glaucoma screening, segmentation, and classification based on images of the papilla and excavation using deep learning techniques. These techniques have been shown to have high sensitivity and specificity in glaucoma screening based on papilla and excavation images. The automatic segmentation of the contours of the optic disc and the excavation then allows the identification and assessment of the glaucomatous disease's progression. As a result, we verified whether deep learning techniques may be helpful in performing accurate and low-cost measurements related to glaucoma, which may promote patient empowerment and help medical doctors better monitor patients.
人工智能技术目前正应用于从疾病筛查到活动识别以及计算机辅助诊断等不同的医疗解决方案中。计算机科学方法与医学知识的结合促进并提高了不同流程和工具的准确性。受这些进展的启发，本文进行了一项文献综述，重点关注基于深度学习技术的、利用视乳头和凹陷图像进行的青光眼筛查、分割和分类的最新技术。这些技术已被证明在基于视乳头和凹陷图像的青光眼筛查中具有高灵敏度和特异性。对视盘和凹陷轮廓的自动分割随后能够识别和评估青光眼疾病的进展。因此，我们验证了深度学习技术是否有助于进行与青光眼相关的准确且低成本的测量，这可能会增强患者的自主权，并帮助医生更好地监测患者。
