用深度学习自动分级眼底病，早筛效果显著。
Deep Learning-assisted AMD Staging based on OCT and OCT Angiography
- 基于病理特征图的模型表现最优，适合早期诊断。
- 三类模型均达高一致性（QWK≥0.83），生物标志物模型最佳。
- 特别适合眼科医生用于早期老年性黄斑变性筛查。
为开发并评估基于光学相干断层扫描（OCT）和OCT血管成像（OCTA）数据的深度学习模型，实现年龄相关性黄斑变性（AMD）严重程度的自动化分级。纳入271名年龄≥50岁、不同程度AMD的受试者。使用扫频源OCTA系统（SOLIX；Visionix/Optovue Inc., CA）获取中心黄斑6×6 mm OCT/OCTA体积数据。根据AREDS简化分级标准，将AMD分为四期（无AMD、早期、中期、晚期）。构建三种不同输入模态的深度学习模型：（1）基于分割病理特征（视网膜积液、玻璃疣、地图样萎缩［GA］、脉络膜新生血管［MNV］）生成的生物标志物图；（2）二维（2D）俯视OCT和OCTA投影图；（3）三维（3D）OCT/OCTA体积数据。采用EfficientNet架构，结合归一化输入、数据增强及五折交叉验证训练。共分析来自271名受试者351只眼的2,030个OCT/OCTA体积。所有模型在分级任务中表现优异，与参考标准具有高度一致性（QWK≥0.83）。其中，生物标志物模型整体性能最高（QWK=0.85±0.03），早期AMD检测F1-score达0.59±0.14；3D模型性能与2D OCT/OCTA模型相当（QWK=0.83±0.04 vs. 0.83±0.09），而2D模型精度最高（0.79±0.06），且对无AMD眼识别最准确。表明基于OCT/OCTA的深度学习模型可精准、自动完成AMD分期，其中生物标志物模型综合表现最佳，尤其利于早期检测。
原文摘要 · Abstract (English)
To develop and evaluate deep learning models for automated grading of age-related macular degeneration (AMD) severity using optical coherence tomography (OCT) and OCT angiography (OCTA) data. Two hundred seventy-one participants aged >= 50 years with varying AMD severities. Central macular 6 x 6 mm OCT/OCTA volumes were acquired using a swept-source OCTA system (SOLIX; Visionix/Optovue Inc., CA). AMD severity was graded into four stages (No AMD, Early AMD, Intermediate AMD, and Advanced AMD) according to the AREDS simplified severity scale. Three deep learning models were developed using different input modalities: (1) biomarker maps derived from segmented pathological features, including retinal fluid, drusen, geographic atrophy (GA), and macular neovascularization (MNV); (2) two-dimensional (2D) en face OCT and OCTA projections; and (3) three-dimensional (3D) OCT/OCTA volumes. EfficientNet-based architectures were trained using normalized inputs, data augmentation, and five-fold cross-validation. A total of 2,030 OCT/OCTA volumes from 351 eyes of 271 participants were analyzed. All models demonstrated strong AMD staging performance with substantial agreement with the reference standard (QWK >= 0.83). The biomarker-based model achieved the highest overall performance (QWK = 0.85 +/- 0.03, mean +/- standard deviation) and the best detection of early AMD (F1-score = 0.59 +/- 0.14). The 3D model achieved performance comparable to the 2D OCT/OCTA model (QWK = 0.83 +/- 0.04 vs. 0.83 +/- 0.09), while the 2D OCT/OCTA model showed the highest precision (0.79 +/- 0.06) and most accurately identified eyes without AMD. Deep learning models using OCT/OCTA data can accurately and automatically grade AMD severity. Among the evaluated approaches, the biomarker-based model provided the most balanced performance and showed particular value for early AMD detection.
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