van Grinsven Mark J J P, Buitendijk Gabriëlle H S, Brussee Corina, van Ginneken Bram, Hoyng Carel B, Theelen Thomas, Klaver Caroline C W, Sánchez Clara I
Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands.
Department of Ophthalmology, Erasmus Medical Center, Rotterdam, The Netherlands Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands.
Invest Ophthalmol Vis Sci. 2015 Jan 8;56(1):633-9. doi: 10.1167/iovs.14-15019.
To examine human performance and agreement on reticular pseudodrusen (RPD) detection and quantification by using single- and multimodality grading protocols and to describe and evaluate a machine learning system for the automatic detection and quantification of reticular pseudodrusen by using single- and multimodality information.
Color fundus, fundus autofluoresence, and near-infrared images of 278 eyes from 230 patients with or without presence of RPD were used in this study. All eyes were scored for presence of RPD during single- and multimodality setups by two experienced observers and a developed machine learning system. Furthermore, automatic quantification of RPD area was performed by the proposed system and compared with human delineations.
Observers obtained a higher performance and better interobserver agreement for RPD detection with multimodality grading, achieving areas under the receiver operating characteristic (ROC) curve of 0.940 and 0.958, and a κ agreement of 0.911. The proposed automatic system achieved an area under the ROC of 0.941 with a multimodality setup. Automatic RPD quantification resulted in an intraclass correlation (ICC) value of 0.704, which was comparable with ICC values obtained between single-modality manual delineations.
Observer performance and agreement for RPD identification improved significantly by using a multimodality grading approach. The developed automatic system showed similar performance as observers, and automatic RPD area quantification was in concordance with manual delineations. The proposed automatic system allows for a fast and accurate identification and quantification of RPD, opening the way for efficient quantitative imaging biomarkers in large data set analysis.
通过使用单模态和多模态分级方案，研究人类在视网膜假性玻璃膜疣（RPD）检测和定量方面的表现及一致性，并描述和评估一种利用单模态和多模态信息自动检测和定量视网膜假性玻璃膜疣的机器学习系统。
本研究使用了230例有或无RPD患者的278只眼睛的彩色眼底、眼底自发荧光和近红外图像。在单模态和多模态设置下，由两名经验丰富的观察者和一个开发的机器学习系统对所有眼睛进行RPD存在情况评分。此外，由所提出的系统对RPD面积进行自动定量，并与人工划定结果进行比较。
观察者在多模态分级下对RPD检测具有更高的表现和更好的观察者间一致性，受试者操作特征（ROC）曲线下面积分别为0.940和0.958，κ一致性为0.911。所提出的自动系统在多模态设置下的ROC曲线下面积为0.941。自动RPD定量的组内相关系数（ICC）值为0.704，与单模态人工划定之间获得的ICC值相当。
使用多模态分级方法可显著提高观察者对RPD识别的表现和一致性。所开发的自动系统表现与观察者相似，且自动RPD面积定量与人工划定结果一致。所提出的自动系统能够快速、准确地识别和定量RPD，为大数据集分析中的高效定量成像生物标志物开辟了道路。
