Kalra Gagan, Cetin Hasan, Whitney Jon, Yordi Sari, Cakir Yavuz, McConville Conor, Whitmore Victoria, Bonnay Michelle, Reese Jamie L, Srivastava Sunil K, Ehlers Justis P
Tony and Leona Campane Center for Excellence in Image-Guided Surgery and Advanced Imaging Research, Cole Eye Institute, Cleveland Clinic, Cleveland, OH 44195, USA.
Diagnostics (Basel). 2023 Mar 20;13(6):1178. doi: 10.3390/diagnostics13061178.
The development and testing of a deep learning (DL)-based approach for detection and measurement of regions of (EZ) to study progression in nonexudative age-related macular degeneration (AMD).
Used in DL model training and testing were 341 subjects with nonexudative AMD with or without geographic atrophy (GA). An independent dataset of 120 subjects were used for testing model performance for prediction of GA progression. Accuracy, specificity, sensitivity, and intraclass correlation coefficient (ICC) for DL-based percentage area measurement was calculated. Random forest-based feature ranking of was compared to previously validated quantitative OCT-based biomarkers.
The model achieved a detection accuracy of 99% (sensitivity = 99%; specificity = 100%) for . Automatic measurement achieved an accuracy of 90% (sensitivity = 90%; specificity = 84%) and the ICC compared to ground truth was high (0.83). In the independent dataset, higher baseline mean correlated with higher progression to GA at year 5 ( < 0.001). was a top ranked feature in the random forest assessment for GA prediction.
This report describes a novel high performance DL-based model for the detection and measurement of . This biomarker showed promising results in predicting progression in nonexudative AMD patients.
开发并测试一种基于深度学习（DL）的方法，用于检测和测量视网膜内界膜（EZ）区域，以研究非渗出性年龄相关性黄斑变性（AMD）的进展情况。
341例患有或未患有地图样萎缩（GA）的非渗出性AMD患者用于DL模型训练和测试。120例患者的独立数据集用于测试模型预测GA进展的性能。计算基于DL的EZ百分比面积测量的准确性、特异性、敏感性和组内相关系数（ICC）。将基于随机森林的EZ特征排名与先前验证的基于定量光学相干断层扫描（OCT）的生物标志物进行比较。
该模型对EZ的检测准确率达到99%（敏感性=99%；特异性=100%）。自动EZ测量的准确率为90%（敏感性=90%；特异性=84%），与真实值相比ICC较高（0.83）。在独立数据集中，较高的基线平均EZ与第5年较高的GA进展相关（P<0.001）。在GA预测的随机森林评估中，EZ是排名靠前的特征。
本报告描述了一种用于检测和测量EZ的新型高性能基于DL的模型。该生物标志物在预测非渗出性AMD患者的进展方面显示出有前景的结果。
