Artificial intelligence for assessment of Stargardt macular atrophy.
Artificial intelligence for assessment of Stargardt macular atrophy.
复制标题
用于评估斯塔加特黄斑萎缩的人工智能。
DOI：
10.4103/1673-5374.339477
复制
发表时间：
2022-12
影响因子：
6.1
通讯作者：
Hu, Zhihong Jewel
中科院分区：
文献类型：
--
作者：
Wang, Ziyuan;Hu, Zhihong Jewel
关键词：
Stargardt病（也称为青少年黄斑变性或Stargardt黄斑变性）是一种遗传性视网膜疾病，可发生在儿童和年轻人的眼睛中。它是青少年发病的黄斑营养不良的最普遍形式，导致进行性（并且通常是严重的）视力丧失。Stargardt病的影像特征是在早期和中期出现肌无力，在晚期出现萎缩，这是由于细胞消耗和死亡。晚期Stargardt病的主要指标是萎缩的外观。眼底自体荧光是一种广泛使用的二维成像技术，可以帮助诊断疾病。相比之下，谱域光学相干断层扫描提供视网膜微观结构的三维可视化，从而允许各个视网膜层的状态。Stargardt病可能会对感光细胞节段以及其他视网膜外层造成不同程度的破坏。近年来，利用人工智能帮助处理此类疾病的应用数量呈指数级增长，这主要得益于使用深度学习进行图像识别的惊人成功。首先提供了关于人工智能深度学习方法用于眼底自发荧光图像的Stargardt萎缩筛查和分割的综述，然后使用人工智能深度学习结构对具有萎缩外观病变和斑点特征的自动视网膜层分割进行了综述。文章最后展望了使用人工智能来寻找早期风险因素或生物标志物，以帮助预测Stargardt疾病的进展。
Stargardt disease (also known as juvenile macular degeneration or Stargardt macular degeneration) is an inherited disorder of the retina, which can occur in the eyes of children and young adults. It is the most prevalent form of juvenile-onset macular dystrophy, causing progressive (and often severe) vision loss. Images with Stargardt disease are characterized by the appearance of flecks in early and intermediate stages, and the appearance of atrophy, due to cells wasting away and dying, in the advanced stage. The primary measure of late-stage Stargardt disease is the appearance of atrophy. Fundus autofluorescence is a widely available two-dimensional imaging technique, which can aid in the diagnosis of the disease. Spectral-domain optical coherence tomography, in contrast, provides three-dimensional visualization of the retinal microstructure, thereby allowing the status of the individual retinal layers. Stargardt disease may cause various levels of disruption to the photoreceptor segments as well as other outer retinal layers. In recent years, there has been an exponential growth in the number of applications utilizing artificial intelligence for help with processing such diseases, heavily fueled by the amazing successes in image recognition using deep learning. This review regarding artificial intelligence deep learning approaches for the Stargardt atrophy screening and segmentation on fundus autofluorescence images is first provided, followed by a review of the automated retinal layer segmentation with atrophic-appearing lesions and fleck features using artificial intelligence deep learning construct. The paper concludes with a perspective about using artificial intelligence to potentially find early risk factors or biomarkers that can aid in the prediction of Stargardt disease progression.
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影响因子：
4.4
作者：
Hu, Zhihong;Wu, Xiaodong;Sadda, Srinivas R.
通讯作者：
Sadda, Srinivas R.
影响因子：
3
作者：
Mishra Z;Wang Z;Sadda SR;Hu Z
通讯作者：
Hu Z
DOI：
10.1109/tpami.2006.19
发表时间：
2006-01-01
影响因子：
23.6
作者：
Li, K;Wu, XD;Sonka, M
通讯作者：
Sonka, M
影响因子：
8.1
作者：
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通讯作者：
Scholl, Hendrik P. N.
影响因子：
13.7
作者：
Strauss, Rupert W.;Ho, Alex;Scholl, Hendrik P. N.
通讯作者：
Scholl, Hendrik P. N.