Department of Biomedical Engineering, Columbia University, New York, 10027, USA.
Department of Electrical Engineering, Columbia University, New York, 10027, USA.
Sci Rep. 2022 Feb 16;12(1):2585. doi: 10.1038/s41598-022-06273-w.
Within the next 1.5 decades, 1 in 7 U.S. adults is anticipated to suffer from age-related macular degeneration (AMD), a degenerative retinal disease which leads to blindness if untreated. Optical coherence tomography angiography (OCTA) has become a prime technique for AMD diagnosis, specifically for late-stage neovascular (NV) AMD. Such technologies generate massive amounts of data, challenging to parse by experts alone, transforming artificial intelligence into a valuable partner. We describe a deep learning (DL) approach which achieves multi-class detection of non-AMD vs. non-neovascular (NNV) AMD vs. NV AMD from a combination of OCTA, OCT structure, 2D b-scan flow images, and high definition (HD) 5-line b-scan cubes; DL also detects ocular biomarkers indicative of AMD risk. Multimodal data were used as input to 2D-3D Convolutional Neural Networks (CNNs). Both for CNNs and experts, choroidal neovascularization and geographic atrophy were found to be important biomarkers for AMD. CNNs predict biomarkers with accuracy up to 90.2% (positive-predictive-value up to 75.8%). Just as experts rely on multimodal data to diagnose AMD, CNNs also performed best when trained on multiple inputs combined. Detection of AMD and its biomarkers from OCTA data via CNNs has tremendous potential to expedite screening of early and late-stage AMD patients.
在未来 15 年内，预计每 7 个美国成年人中就有 1 个会患上与年龄相关的黄斑变性（AMD），这是一种退行性视网膜疾病，如果不治疗，可能导致失明。光学相干断层扫描血管造影术（OCTA）已成为 AMD 诊断的主要技术，特别是在晚期新生血管（NV）AMD 中。这些技术会生成大量的数据，仅靠专家难以分析，这使得人工智能成为了宝贵的合作伙伴。我们描述了一种深度学习（DL）方法，该方法可以从 OCTA、OCT 结构、2D B 扫描血流图像和高清（HD）5 线 B 扫描立方体的组合中，对非 AMD 与非新生血管性（NNV）AMD 与 NV AMD 进行多类检测；DL 还可以检测出与 AMD 风险相关的眼部生物标志物。多模态数据被用作 2D-3D 卷积神经网络（CNN）的输入。对于 CNN 和专家来说，脉络膜新生血管和地理萎缩被发现是 AMD 的重要生物标志物。CNN 对生物标志物的预测准确率高达 90.2%（阳性预测值高达 75.8%）。就像专家依赖多模态数据来诊断 AMD 一样，CNN 也在接受多种输入的联合训练时表现最佳。通过 CNN 从 OCTA 数据中检测 AMD 及其生物标志物，具有极大的潜力，可以加快对早期和晚期 AMD 患者的筛查。
