PURPOSE To assess the utility of deep learning in the detection of geographic atrophy (GA) from color fundus photographs and to explore potential utility in detecting central GA (CGA). DESIGN A deep learning model was developed to detect the presence of GA in color fundus photographs, and 2 additional models were developed to detect CGA in different scenarios. PARTICIPANTS A total of 59 812 color fundus photographs from longitudinal follow-up of 4582 participants in the Age-Related Eye Disease Study (AREDS) dataset. Gold standard labels were from human expert reading center graders using a standardized protocol. METHODS A deep learning model was trained to use color fundus photographs to predict GA presence from a population of eyes with no AMD to advanced AMD. A second model was trained to predict CGA presence from the same population. A third model was trained to predict CGA presence from the subset of eyes with GA. For training and testing, 5-fold cross-validation was used. For comparison with human clinician performance, model performance was compared with that of 88 retinal specialists. MAIN OUTCOME MEASURES Area under the curve (AUC), accuracy, sensitivity, specificity, and precision. RESULTS The deep learning models (GA detection, CGA detection from all eyes, and centrality detection from GA eyes) had AUCs of 0.933-0.976, 0.939-0.976, and 0.827-0.888, respectively. The GA detection model had accuracy, sensitivity, specificity, and precision of 0.965 (95% confidence interval [CI], 0.959-0.971), 0.692 (0.560-0.825), 0.978 (0.970-0.985), and 0.584 (0.491-0.676), respectively, compared with 0.975 (0.971-0.980), 0.588 (0.468-0.707), 0.982 (0.978-0.985), and 0.368 (0.230-0.505) for the retinal specialists. The CGA detection model had values of 0.966 (0.957-0.975), 0.763 (0.641-0.885), 0.971 (0.960-0.982), and 0.394 (0.341-0.448). The centrality detection model had values of 0.762 (0.725-0.799), 0.782 (0.618-0.945), 0.729 (0.543-0.916), and 0.799 (0.710-0.888). CONCLUSIONS A deep learning model demonstrated high accuracy for the automated detection of GA. The AUC was noninferior to that of human retinal specialists. Deep learning approaches may also be applied to the identification of CGA. The code and pretrained models are publicly available at https://github.com/ncbi-nlp/DeepSeeNet.

中文翻译：

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一种从彩色眼底照片自动检测地理萎缩的深度学习方法。

目的评估深度学习在彩色眼底照片中检测地理萎缩（GA）的效用，并探索在检测中央GA（CGA）方面的潜在效用。设计开发了一个深度学习模型来检测彩色眼底照片中GA的存在，并开发了2个其他模型来检测不同场景下的CGA。参与者来自年龄相关性眼病研究（AREDS）数据集的4582名参与者的纵向随访中，共有59 812张彩色眼底照片。金标准标签来自人类专家阅读中心分级人员，使用的是标准化协议。方法训练了一个深度学习模型，以使用彩色眼底照片来预测从无AMD到晚期AMD的眼睛群体中GA的存在。训练了第二个模型来预测来自相同人群的CGA存在。训练了第三个模型，以从患有GA的眼睛子集中预测CGA的存在。为了进行培训和测试，使用了5倍交叉验证。为了与人类临床医生的表现进行比较，将模型表现与88位视网膜专家的表现进行了比较。主要观察指标曲线下的面积（AUC），准确性，敏感性，特异性和精密度。结果深度学习模型（GA检测，全眼CGA检测和GA眼中心度检测）的AUC分别为0.933-0.976、0.939-0.976和0.827-0.888。GA检测模型的准确性，敏感性，特异性和精密度分别为0.965（95％置信区间[CI]，0.959-0.971），0.692（0.560-0.825），0.978（0.970-0.985）和0.584（0.491-0.676）分别为0.975（0.971-0.980），0.588（0.468-0.707），0.982（0.978-0.985）和0.368（0.230-0）。505）。CGA检测模型的值分别为0.966（0.957-0.975），0.763（0.641-0.885），0.971（0.960-0.982）和0.394（0.341-0.448）。中心度检测模型的值分别为0.762（0.725-0.799），0.782（0.618-0.945），0.729（0.543-0.916）和0.799（0.710-0.888）。结论深度学习模型证明了GA自动检测的高精度。AUC不亚于人类视网膜专家。深度学习方法也可以应用于CGA的识别。代码和预训练模型可在https://github.com/ncbi-nlp/DeepSeeNet上公开获得。618-0.945），0.729（0.543-0.916）和0.799（0.710-0.888）。结论深度学习模型证明了GA自动检测的高精度。AUC不亚于人类视网膜专家。深度学习方法也可以应用于CGA的识别。代码和预训练模型可在https://github.com/ncbi-nlp/DeepSeeNet上公开获得。618-0.945），0.729（0.543-0.916）和0.799（0.710-0.888）。结论深度学习模型证明了GA自动检测的高精度。AUC不亚于人类视网膜专家。深度学习方法也可以应用于CGA的识别。代码和预训练模型可在https://github.com/ncbi-nlp/DeepSeeNet上公开获得。