Venhuizen Freerk G, van Ginneken Bram, van Asten Freekje, van Grinsven Mark J J P, Fauser Sascha, Hoyng Carel B, Theelen Thomas, Sánchez Clara I
Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands 2Department of Ophthalmology, Radboud University Medical Center, Nijmegen, The Netherlands.
Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands.
Invest Ophthalmol Vis Sci. 2017 Apr 1;58(4):2318-2328. doi: 10.1167/iovs.16-20541.
To evaluate a machine learning algorithm that automatically grades age-related macular degeneration (AMD) severity stages from optical coherence tomography (OCT) scans.
A total of 3265 OCT scans from 1016 patients with either no signs of AMD or with signs of early, intermediate, or advanced AMD were randomly selected from a large European multicenter database. A machine learning system was developed to automatically grade unseen OCT scans into different AMD severity stages without requiring retinal layer segmentation. The ability of the system to identify high-risk AMD stages and to assign the correct severity stage was determined by using receiver operator characteristic (ROC) analysis and Cohen's κ statistics (κ), respectively. The results were compared to those of two human observers. Reproducibility was assessed in an independent, publicly available data set of 384 OCT scans.
The system achieved an area under the ROC curve of 0.980 with a sensitivity of 98.2% at a specificity of 91.2%. This compares favorably with the performance of human observers who achieved sensitivities of 97.0% and 99.4% at specificities of 89.7% and 87.2%, respectively. A good level of agreement with the reference was obtained (κ = 0.713) and was in concordance with the human observers (κ = 0.775 and κ = 0.755, respectively).
A machine learning system capable of automatically grading OCT scans into AMD severity stages was developed and showed similar performance as human observers. The proposed automatic system allows for a quick and reliable grading of large quantities of OCT scans, which could increase the efficiency of large-scale AMD studies and pave the way for AMD screening using OCT.
评估一种可根据光学相干断层扫描（OCT）图像自动对年龄相关性黄斑变性（AMD）严重程度进行分级的机器学习算法。
从一个大型欧洲多中心数据库中随机选取了1016例患者的3265张OCT图像，这些患者要么没有AMD迹象，要么有早期、中期或晚期AMD迹象。开发了一种机器学习系统，可在无需视网膜层分割的情况下，自动将未见过的OCT图像分级为不同的AMD严重程度阶段。分别使用受试者工作特征（ROC）分析和科恩kappa统计量（κ）来确定该系统识别高危AMD阶段以及分配正确严重程度阶段的能力。将结果与两名人类观察者的结果进行比较。在一个包含384张OCT图像的独立公开数据集中评估了可重复性。
该系统的ROC曲线下面积为0.980，敏感性为98.2%，特异性为91.2%。这与人类观察者的表现相比具有优势，人类观察者的敏感性分别为97.0%和99.4%，特异性分别为89.7%和87.2%。与参考标准具有良好的一致性（κ = 0.713），并且与人类观察者一致（分别为κ = 0.775和κ = 0.755）。
开发了一种能够将OCT图像自动分级为AMD严重程度阶段的机器学习系统，其表现与人类观察者相似。所提出的自动系统能够对大量OCT图像进行快速可靠的分级，这可以提高大规模AMD研究的效率，并为使用OCT进行AMD筛查铺平道路。
