PDF(Pubmed)
Abstract：
UNASSIGNED: To explore the structural-functional loss relationship from optic-nerve-head- and macula-centred spectral-domain (SD) Optical Coherence Tomography (OCT) images in the full spectrum of glaucoma patients using deep-learning methods.
UNASSIGNED: A cohort comprising 5238 unique eyes classified as suspects or diagnosed with glaucoma was considered. All patients underwent ophthalmologic examination consisting of standard automated perimetry (SAP), macular OCT, and peri-papillary OCT on the same day. Deep learning models were trained to estimate G-pattern visual field (VF) mean deviation (MD) and cluster MD using retinal thickness maps from seven layers: retinal nerve fiber layer (RNFL), ganglion cell layer and inner plexiform layer (GCL + IPL), inner nuclear layer and outer plexiform layer (INL + OPL), outer nuclear layer (ONL), photoreceptors and retinal pigmented epithelium (PR + RPE), choriocapillaris and choroidal stroma (CC + CS), total retinal thickness (RT).
UNASSIGNED: The best performance on MD prediction is achieved by RNFL, GCL + IPL and RT layers, with R2 scores of 0.37, 0.33, and 0.31, respectively. Combining macular and peri-papillary scans outperforms single modality prediction, achieving an R2 value of 0.48. Cluster MD predictions show promising results, notably in central clusters, reaching an R2 of 0.56.
UNASSIGNED: The combination of multiple modalities, such as optic-nerve-head circular B-scans and retinal thickness maps from macular SD-OCT images, improves the performance of MD and cluster MD prediction. Our proposed model demonstrates the highest level of accuracy in predicting MD in the early-to-mid stages of glaucoma.
UNASSIGNED: Objective measures recorded with SD-OCT can optimize the number of visual field tests and improve individualized glaucoma care by adjusting VF testing frequency based on deep-learning estimates of functional damage.
UNASSIGNED: A cohort comprising 5238 unique eyes classified as suspects or diagnosed with glaucoma was considered. All patients underwent ophthalmologic examination consisting of standard automated perimetry (SAP), macular OCT, and peri-papillary OCT on the same day. Deep learning models were trained to estimate G-pattern visual field (VF) mean deviation (MD) and cluster MD using retinal thickness maps from seven layers: retinal nerve fiber layer (RNFL), ganglion cell layer and inner plexiform layer (GCL + IPL), inner nuclear layer and outer plexiform layer (INL + OPL), outer nuclear layer (ONL), photoreceptors and retinal pigmented epithelium (PR + RPE), choriocapillaris and choroidal stroma (CC + CS), total retinal thickness (RT).
UNASSIGNED: The best performance on MD prediction is achieved by RNFL, GCL + IPL and RT layers, with R2 scores of 0.37, 0.33, and 0.31, respectively. Combining macular and peri-papillary scans outperforms single modality prediction, achieving an R2 value of 0.48. Cluster MD predictions show promising results, notably in central clusters, reaching an R2 of 0.56.
UNASSIGNED: The combination of multiple modalities, such as optic-nerve-head circular B-scans and retinal thickness maps from macular SD-OCT images, improves the performance of MD and cluster MD prediction. Our proposed model demonstrates the highest level of accuracy in predicting MD in the early-to-mid stages of glaucoma.
UNASSIGNED: Objective measures recorded with SD-OCT can optimize the number of visual field tests and improve individualized glaucoma care by adjusting VF testing frequency based on deep-learning estimates of functional damage.
摘要：
■使用深度学习方法，从青光眼患者的全光谱中的视神经-头部和黄斑中心谱域（SD）光学相干断层扫描（OCT）图像中探索结构-功能丧失关系。
考虑了包含5238只被分类为疑似或诊断为青光眼的独特眼睛的队列。所有患者均接受眼科检查，包括标准自动视野检查（SAP）。黄斑OCT,和乳头周围的OCT在同一天。对深度学习模型进行了训练，以使用来自七个层的视网膜厚度图估计G模式视野（VF）平均偏差（MD）和聚类MD：视网膜神经纤维层（RNFL），神经节细胞层和内网状层（GCL+IPL），内部核层和外部丛状层（INL+OPL），外核层（ONL），光感受器和视网膜色素上皮（PR+RPE），脉络膜毛细血管和脉络膜基质（CC+CS），视网膜总厚度（RT）。
■通过RNFL实现了MD预测的最佳性能，GCL+IPL和RT层，R2评分分别为0.37、0.33和0.31。结合黄斑和乳头周围扫描优于单模态预测，实现0.48的R2值。集群MD预测显示出有希望的结果，特别是在中央集群中，达到0.56的R2。
■多种模态的组合，例如来自黄斑SD-OCT图像的视神经头圆形B扫描和视网膜厚度图，提高了MD和聚类MD预测的性能。我们提出的模型证明了在青光眼早期至中期预测MD的最高准确性。
■用SD-OCT记录的客观措施可以优化视野测试的数量，并通过根据功能损伤的深度学习估计调整VF测试频率来改善个性化青光眼护理。
考虑了包含5238只被分类为疑似或诊断为青光眼的独特眼睛的队列。所有患者均接受眼科检查，包括标准自动视野检查（SAP）。黄斑OCT,和乳头周围的OCT在同一天。对深度学习模型进行了训练，以使用来自七个层的视网膜厚度图估计G模式视野（VF）平均偏差（MD）和聚类MD：视网膜神经纤维层（RNFL），神经节细胞层和内网状层（GCL+IPL），内部核层和外部丛状层（INL+OPL），外核层（ONL），光感受器和视网膜色素上皮（PR+RPE），脉络膜毛细血管和脉络膜基质（CC+CS），视网膜总厚度（RT）。
■通过RNFL实现了MD预测的最佳性能，GCL+IPL和RT层，R2评分分别为0.37、0.33和0.31。结合黄斑和乳头周围扫描优于单模态预测，实现0.48的R2值。集群MD预测显示出有希望的结果，特别是在中央集群中，达到0.56的R2。
■多种模态的组合，例如来自黄斑SD-OCT图像的视神经头圆形B扫描和视网膜厚度图，提高了MD和聚类MD预测的性能。我们提出的模型证明了在青光眼早期至中期预测MD的最高准确性。
■用SD-OCT记录的客观措施可以优化视野测试的数量，并通过根据功能损伤的深度学习估计调整VF测试频率来改善个性化青光眼护理。
