Automated classifiers for early detection and diagnosis of retinopathy in diabetic eyes.
Automated classifiers for early detection and diagnosis of retinopathy in diabetic eyes.
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DOI：
10.1186/1471-2105-15-106
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发表时间：
2014-04-12
影响因子：
3
通讯作者：
DeBuc DC
中科院分区：
文献类型：
--
作者：
Somfai GM;Tátrai E;Laurik L;Varga B;Ölvedy V;Jiang H;Wang J;Smiddy WE;Somogyi A;DeBuc DC
人工神经网络（ANNs）已被用于对眼病进行分类，如糖尿病视网膜病变（DR）和青光眼。DR是发达国家工作年龄成年人失明的主要原因。通过整合视网膜结构的结构和光学特性测试测量，可以切实改善DR诊断程序的实施，为达到诊断提供重要和补充的信息。在这项研究中，我们评估了从光学相干断层扫描图像中提取的各种视网膜内层的几个结构和光学特征（厚度、全反射和分形维数）的能力，以训练贝叶斯神经网络来区分健康和糖尿病眼睛，有无轻度视网膜病变。在探索受试者眼睛是否健康（诊断状态，检验1）的概率时，我们发现，检测结果（健康状态）阳性的患者比例（检验1）正确诊断的概率最高的是外丛状层（OPL）和神经节细胞与内丛状层（GCL + IPL）组成的复合体的结构和光学性质特征（阳性预测值，PPV），分别为91%和89%。尽管训练数据集的大小不同，真负、TP和PPV值仍保持稳定（检验2）。23只轻度视网膜病变的糖尿病眼与38只无视网膜病变的糖尿病眼混合时，对视网膜神经纤维层特征、光感受器外节和视网膜色素上皮的敏感性、特异性和PPV均大于或接近0.70（试验3）。根据光学相干断层扫描数据的结构和光学特征进行训练的贝叶斯神经网络可以成功地区分有视网膜病变和没有视网膜病变的健康眼睛和糖尿病眼睛。贝叶斯径向基函数网络预测的OPL分形维数和GCL + IPL复合物的分形维数对糖尿病眼轻度视网膜病变有较好的诊断价值。此外，视网膜神经纤维层、光感受器外段和视网膜色素上皮的厚度和分形维数参数为糖尿病眼的轻度视网膜病变和非轻度视网膜病变的诊断分类提供了希望。
Artificial neural networks (ANNs) have been used to classify eye diseases, such as diabetic retinopathy (DR) and glaucoma. DR is the leading cause of blindness in working-age adults in the developed world. The implementation of DR diagnostic routines could be feasibly improved by the integration of structural and optical property test measurements of the retinal structure that provide important and complementary information for reaching a diagnosis. In this study, we evaluate the capability of several structural and optical features (thickness, total reflectance and fractal dimension) of various intraretinal layers extracted from optical coherence tomography images to train a Bayesian ANN to discriminate between healthy and diabetic eyes with and with no mild retinopathy. When exploring the probability as to whether the subject’s eye was healthy (diagnostic condition, Test 1), we found that the structural and optical property features of the outer plexiform layer (OPL) and the complex formed by the ganglion cell and inner plexiform layers (GCL + IPL) provided the highest probability (positive predictive value (PPV) of 91% and 89%, respectively) for the proportion of patients with positive test results (healthy condition) who were correctly diagnosed (Test 1). The true negative, TP and PPV values remained stable despite the different sizes of training data sets (Test 2). The sensitivity, specificity and PPV were greater or close to 0.70 for the retinal nerve fiber layer’s features, photoreceptor outer segments and retinal pigment epithelium when 23 diabetic eyes with mild retinopathy were mixed with 38 diabetic eyes with no retinopathy (Test 3). A Bayesian ANN trained on structural and optical features from optical coherence tomography data can successfully discriminate between healthy and diabetic eyes with and with no retinopathy. The fractal dimension of the OPL and the GCL + IPL complex predicted by the Bayesian radial basis function network provides better diagnostic utility to classify diabetic eyes with mild retinopathy. Moreover, the thickness and fractal dimension parameters of the retinal nerve fiber layer, photoreceptor outer segments and retinal pigment epithelium show promise for the diagnostic classification between diabetic eyes with and with no mild retinopathy.
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影响因子：
4.4
作者：
Bowd, Christopher;Hao, Jiucang;Goldbaum, Michael H.
通讯作者：
Goldbaum, Michael H.
影响因子：
6.7
作者：
Piao, Jingshu;Lee, Ji-Eun;Kim, Jin-Seok
通讯作者：
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影响因子：
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作者：
HAGEMAN, GS;MARMOR, MF;JOHNSON, LV
通讯作者：
JOHNSON, LV
影响因子：
3.8
作者：
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通讯作者：
Puliafito, CA
影响因子：
4.1
作者：
Gardner, GG;Keating, D;Elliott, AT
通讯作者：
Elliott, AT