Deep Learning-Based Classification of Inherited Retinal Diseases Using Fundus Autofluorescence.
Deep Learning-Based Classification of Inherited Retinal Diseases Using Fundus Autofluorescence.
复制标题
使用眼底自体荧光进行基于深度学习的遗传性视网膜疾病分类。
DOI：
10.3390/jcm9103303
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发表时间：
2020-10-14
影响因子：
3.9
通讯作者：
Souied E
中科院分区：
文献类型：
--
作者：
Miere A;Le Meur T;Bitton K;Pallone C;Semoun O;Capuano V;Colantuono D;Taibouni K;Chenoune Y;Astroz P;Berlemont S;Petit E;Souied E
背景近年来，深度学习越来越多地应用于各种眼科疾病。遗传性视网膜疾病（IRD）是一种罕见的遗传性疾病，在眼底自体荧光成像（FAF）上具有独特的表型。我们的目的是使用深度学习算法通过FAF图像自动分类不同的IRD。方法.在这项研究中，患有视网膜色素变性（RP），Best疾病（BD），Stargardt疾病（STGD）以及健康对照组的患者的FAF图像被用于训练多层深度卷积神经网络（CNN），以区分每种类型的IRD和正常FAF之间的FAF图像。CNN用389张FAF图像进行了训练和验证。使用了既定的增强技术。使用Adam优化器进行训练。对于随后的测试，然后使用94个未经训练的FAF图像对构建的分类器进行测试。结果对于遗传性视网膜疾病分类器，全局准确度为0.95。BD、RP、STGD和健康对照组的精确-召回曲线下面积（PRC-AUC）平均值分别为0.988、0.999、0.996和0.989。结论.这项研究描述了使用基于深度学习的算法来自动检测和分类FAF中的遗传性视网膜疾病。由此，创建的分类器显示出优异的结果。随着进一步的发展，该模型可能是一种诊断工具，并可能为未来的治疗方法提供相关信息。
Background. In recent years, deep learning has been increasingly applied to a vast array of ophthalmological diseases. Inherited retinal diseases (IRD) are rare genetic conditions with a distinctive phenotype on fundus autofluorescence imaging (FAF). Our purpose was to automatically classify different IRDs by means of FAF images using a deep learning algorithm. Methods. In this study, FAF images of patients with retinitis pigmentosa (RP), Best disease (BD), Stargardt disease (STGD), as well as a healthy comparable group were used to train a multilayer deep convolutional neural network (CNN) to differentiate FAF images between each type of IRD and normal FAF. The CNN was trained and validated with 389 FAF images. Established augmentation techniques were used. An Adam optimizer was used for training. For subsequent testing, the built classifiers were then tested with 94 untrained FAF images. Results. For the inherited retinal disease classifiers, global accuracy was 0.95. The precision-recall area under the curve (PRC-AUC) averaged 0.988 for BD, 0.999 for RP, 0.996 for STGD, and 0.989 for healthy controls. Conclusions. This study describes the use of a deep learning-based algorithm to automatically detect and classify inherited retinal disease in FAF. Hereby, the created classifiers showed excellent results. With further developments, this model may be a diagnostic tool and may give relevant information for future therapeutic approaches.
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影响因子：
19.5
作者：
Russakovsky, Olga;Deng, Jia;Fei-Fei, Li
通讯作者：
Fei-Fei, Li
影响因子：
3.4
作者：
Shah, Mital;Ledo, Ana Roomans;Rittscher, Jens
通讯作者：
Rittscher, Jens
影响因子：
4.4
作者：
Duncker, Tobias;Greenberg, Jonathan P.;Sparrow, Janet R.
通讯作者：
Sparrow, Janet R.
影响因子：
4.2
作者：
Lois, N;Halfyard, AS;Fitzke, FW
通讯作者：
Fitzke, FW
影响因子：
4
作者：
BUNDEY, S;CREWS, SJ
通讯作者：
CREWS, SJ