PURPOSE In assessing the severity of age-related macular degeneration (AMD), the Age-Related Eye Disease Study (AREDS) Simplified Severity Scale predicts the risk of progression to late AMD. However, its manual use requires the time-consuming participation of expert practitioners. Although several automated deep learning systems have been developed for classifying color fundus photographs (CFP) of individual eyes by AREDS severity score, none to date has used a patient-based scoring system that uses images from both eyes to assign a severity score. DESIGN DeepSeeNet, a deep learning model, was developed to classify patients automatically by the AREDS Simplified Severity Scale (score 0-5) using bilateral CFP. PARTICIPANTS DeepSeeNet was trained on 58 402 and tested on 900 images from the longitudinal follow-up of 4549 participants from AREDS. Gold standard labels were obtained using reading center grades. METHODS DeepSeeNet simulates the human grading process by first detecting individual AMD risk factors (drusen size, pigmentary abnormalities) for each eye and then calculating a patient-based AMD severity score using the AREDS Simplified Severity Scale. MAIN OUTCOME MEASURES Overall accuracy, specificity, sensitivity, Cohen's kappa, and area under the curve (AUC). The performance of DeepSeeNet was compared with that of retinal specialists. RESULTS DeepSeeNet performed better on patient-based classification (accuracy = 0.671; kappa = 0.558) than retinal specialists (accuracy = 0.599; kappa = 0.467) with high AUC in the detection of large drusen (0.94), pigmentary abnormalities (0.93), and late AMD (0.97). DeepSeeNet also outperformed retinal specialists in the detection of large drusen (accuracy 0.742 vs. 0.696; kappa 0.601 vs. 0.517) and pigmentary abnormalities (accuracy 0.890 vs. 0.813; kappa 0.723 vs. 0.535) but showed lower performance in the detection of late AMD (accuracy 0.967 vs. 0.973; kappa 0.663 vs. 0.754). CONCLUSIONS By simulating the human grading process, DeepSeeNet demonstrated high accuracy with increased transparency in the automated assignment of individual patients to AMD risk categories based on the AREDS Simplified Severity Scale. These results highlight the potential of deep learning to assist and enhance clinical decision-making in patients with AMD, such as early AMD detection and risk prediction for developing late AMD. DeepSeeNet is publicly available on https://github.com/ncbi-nlp/DeepSeeNet.

中文翻译：

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DeepSeeNet：一种深度学习模型，用于根据彩色眼底照片对基于患者的年龄相关性黄斑变性严重程度进行自动分类。

目的 在评估年龄相关性黄斑变性 (AMD) 的严重程度时，年龄相关眼病研究 (AREDS) 简化严重程度量表可预测进展为晚期 AMD 的风险。然而，其手动使用需要专家从业者的耗时参与。尽管已经开发了几种自动深度学习系统，用于根据 AREDS 严重程度评分对单眼彩色眼底照片 (CFP) 进行分类，但迄今为止还没有一个系统使用基于患者的评分系统，该系统使用双眼图像来分配严重程度评分。设计 DeepSeeNet 是一种深度学习模型，旨在使用双侧 CFP 根据 AREDS 简化严重程度量表（分数 0-5）自动对患者进行分类。参与者 DeepSeeNet 在 58 402 名参与者上进行了训练，并在 AREDS 的 4549 名参与者的纵向随访中的 900 张图像上进行了测试。黄金标准标签是使用阅读中心的成绩获得的。方法 DeepSeeNet 模拟人类分级过程，首先检测每只眼睛的个体 AMD 风险因素（玻璃疣大小、色素异常），然后使用 AREDS 简化严重程度量表计算基于患者的 AMD 严重程度评分。主要观察指标 总体准确性、特异性、敏感性、Cohen's kappa 和曲线下面积 (AUC)。DeepSeeNet 的性能与视网膜专家的性能进行了比较。结果 DeepSeeNet 在基于患者的分类（准确度 = 0.671；kappa = 0.558）上比视网膜专家（准确度 = 0.599；kappa = 0.467）表现更好，在检测大玻璃疣（0.94）、色素异常（0.93）和晚期 AMD (0.97)。DeepSeeNet 在检测大玻璃膜疣（准确度 0.742 vs. 0.696；kappa 0.601 vs. 0.517）和色素异常（准确度 0.890 vs. 0.813；kappa 0.723 vs. 0.535）方面也优于视网膜专家，但在检测晚期 AMD 方面表现较差（准确度 0.967 与 0.973；kappa 0.663 与 0.754）。结论 通过模拟人类分级过程，DeepSeeNet 在根据 AREDS 简化严重程度量表自动将个体患者分配到 AMD 风险类别方面表现出较高的准确性和透明度。这些结果凸显了深度学习在协助和增强 AMD 患者临床决策方面的潜力，例如早期 AMD 检测和晚期 AMD 的风险预测。DeepSeeNet 在 https://github.com/ncbi-nlp/DeepSeeNet 上公开提供。