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. While several automated deep learning (DL) systems have been developed for classifying color fundus photographs of individual eyes by AREDS severity score, none to date has utilized a patient-based scoring system that employs images from both eyes to assign a severity score. DeepSeeNet, a DL model, was developed to classify patients automatically by the AREDS Simplified Severity Scale (score 0-5) using bilateral color fundus images. DeepSeeNet was trained on 58,402 and tested on 900 images from the longitudinal follow up of 4,549 participants from AREDS. Gold standard labels were obtained using reading center grades. 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. DeepSeeNet performed better on patient-based, multi-class classification (accuracy=0.671; kappa=0.558) than retinal specialists (accuracy=0.599; kappa=0.467) with high AUCs in the detection of large drusen (0.94), pigmentary abnormalities (0.93) and late AMD (0.97), respectively. 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 systems to assist and enhance clinical decision-making processes in AMD patients such as early AMD detection and risk prediction for developing late AMD. DeepSeeNet is publicly available on https://github.com/ncbi-nlp/DeepSeeNet.

DeepSeeNet是一种基于深度学习的系统，使用双眼CFP图像来自动分类患者的年龄相关性黄斑变性病情，并通过AREDS简化严重程度评分患者级别，并表现出比视网膜专家更高的分类精度和透明度。

DeepSeeNet: A deep learning model for automated classification of
  patient-based age-related macular degeneration severity from color fundus
  photographs

DeepSeeNet：一种深度学习模型，用于自动分类基于患者色视网膜照片的年龄相关性黄斑变性严重程度