PDF(Pubmed)
Abstract：
UNASSIGNED: Timely diagnosis of eye diseases is paramount to obtaining the best treatment outcomes. OCT and OCT angiography (OCTA) have several advantages that lend themselves to early detection of ocular pathology; furthermore, the techniques produce large, feature-rich data volumes. However, the full clinical potential of both OCT and OCTA is stymied when complex data acquired using the techniques must be manually processed. Here, we propose an automated diagnostic framework based on structural OCT and OCTA data volumes that could substantially support the clinical application of these technologies.
UNASSIGNED: Cross sectional study.
UNASSIGNED: Five hundred twenty-six OCT and OCTA volumes were scanned from the eyes of 91 healthy participants, 161 patients with diabetic retinopathy (DR), 95 patients with age-related macular degeneration (AMD), and 108 patients with glaucoma.
UNASSIGNED: The diagnosis framework was constructed based on semisequential 3-dimensional (3D) convolutional neural networks. The trained framework classifies combined structural OCT and OCTA scans as normal, DR, AMD, or glaucoma. Fivefold cross-validation was performed, with 60% of the data reserved for training, 20% for validation, and 20% for testing. The training, validation, and test data sets were independent, with no shared patients. For scans diagnosed as DR, AMD, or glaucoma, 3D class activation maps were generated to highlight subregions that were considered important by the framework for automated diagnosis.
UNASSIGNED: The area under the curve (AUC) of the receiver operating characteristic curve and quadratic-weighted kappa were used to quantify the diagnostic performance of the framework.
UNASSIGNED: For the diagnosis of DR, the framework achieved an AUC of 0.95 ± 0.01. For the diagnosis of AMD, the framework achieved an AUC of 0.98 ± 0.01. For the diagnosis of glaucoma, the framework achieved an AUC of 0.91 ± 0.02.
UNASSIGNED: Deep learning frameworks can provide reliable, sensitive, interpretable, and fully automated diagnosis of eye diseases.
UNASSIGNED: Proprietary or commercial disclosure may be found after the references.
UNASSIGNED: Cross sectional study.
UNASSIGNED: Five hundred twenty-six OCT and OCTA volumes were scanned from the eyes of 91 healthy participants, 161 patients with diabetic retinopathy (DR), 95 patients with age-related macular degeneration (AMD), and 108 patients with glaucoma.
UNASSIGNED: The diagnosis framework was constructed based on semisequential 3-dimensional (3D) convolutional neural networks. The trained framework classifies combined structural OCT and OCTA scans as normal, DR, AMD, or glaucoma. Fivefold cross-validation was performed, with 60% of the data reserved for training, 20% for validation, and 20% for testing. The training, validation, and test data sets were independent, with no shared patients. For scans diagnosed as DR, AMD, or glaucoma, 3D class activation maps were generated to highlight subregions that were considered important by the framework for automated diagnosis.
UNASSIGNED: The area under the curve (AUC) of the receiver operating characteristic curve and quadratic-weighted kappa were used to quantify the diagnostic performance of the framework.
UNASSIGNED: For the diagnosis of DR, the framework achieved an AUC of 0.95 ± 0.01. For the diagnosis of AMD, the framework achieved an AUC of 0.98 ± 0.01. For the diagnosis of glaucoma, the framework achieved an AUC of 0.91 ± 0.02.
UNASSIGNED: Deep learning frameworks can provide reliable, sensitive, interpretable, and fully automated diagnosis of eye diseases.
UNASSIGNED: Proprietary or commercial disclosure may be found after the references.
摘要：
未经评估：及时诊断眼部疾病对于获得最佳治疗效果至关重要。OCT和OCT血管造影术（OCTA）有几个优点，有助于早期发现眼部病理；此外，这些技术产生了巨大的，功能丰富的数据量。然而,当使用OCT和OCTA采集的复杂数据必须手动处理时,OCT和OCTA的全部临床潜力受到阻碍.这里,我们提出了一种基于结构OCT和OCTA数据量的自动诊断框架,该框架可充分支持这些技术的临床应用.
未经评估：横断面研究。
未经评估：从91名健康参与者的眼睛扫描了五百二十六个OCT和OCTA卷，161例糖尿病视网膜病变（DR），95例年龄相关性黄斑变性（AMD），和108名青光眼患者。
UNASSIGNED：诊断框架是基于半序列3维（3D）卷积神经网络构建的。经过训练的框架将组合的结构OCT和OCTA扫描分类为正常，DR,AMD,或青光眼。进行了五次交叉验证，60%的数据保留用于训练，20%用于验证，20%用于测试。训练,验证,测试数据集是独立的，没有共享的病人。对于诊断为DR的扫描，AMD,或者青光眼，生成3D类激活图，以突出显示自动诊断框架认为重要的子区域。
UNASSIGNED：受试者工作特征曲线的曲线下面积（AUC）和二次加权κ用于量化框架的诊断性能。
未经评估：对于DR的诊断，该框架的AUC为0.95±0.01。对于AMD的诊断，该框架的AUC为0.98±0.01。对于青光眼的诊断，该框架的AUC为0.91±0.02。
UNASSIGNED：深度学习框架可以提供可靠的，敏感,可解释,和全自动诊断眼部疾病。
UNASSIGNED：在参考文献之后可以找到专有或商业披露。
未经评估：横断面研究。
未经评估：从91名健康参与者的眼睛扫描了五百二十六个OCT和OCTA卷，161例糖尿病视网膜病变（DR），95例年龄相关性黄斑变性（AMD），和108名青光眼患者。
UNASSIGNED：诊断框架是基于半序列3维（3D）卷积神经网络构建的。经过训练的框架将组合的结构OCT和OCTA扫描分类为正常，DR,AMD,或青光眼。进行了五次交叉验证，60%的数据保留用于训练，20%用于验证，20%用于测试。训练,验证,测试数据集是独立的，没有共享的病人。对于诊断为DR的扫描，AMD,或者青光眼，生成3D类激活图，以突出显示自动诊断框架认为重要的子区域。
UNASSIGNED：受试者工作特征曲线的曲线下面积（AUC）和二次加权κ用于量化框架的诊断性能。
未经评估：对于DR的诊断，该框架的AUC为0.95±0.01。对于AMD的诊断，该框架的AUC为0.98±0.01。对于青光眼的诊断，该框架的AUC为0.91±0.02。
UNASSIGNED：深度学习框架可以提供可靠的，敏感,可解释,和全自动诊断眼部疾病。
UNASSIGNED：在参考文献之后可以找到专有或商业披露。
