A Deep Learning Approach for Automated Detection of Geographic Atrophy from Color Fundus Photographs
A Deep Learning Approach for Automated Detection of Geographic Atrophy from Color Fundus Photographs
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DOI：
10.1016/j.ophtha.2019.06.005
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发表时间：
2019-11-01
期刊：
影响因子：
13.7
通讯作者：
Chew, Emily Y.
中科院分区：
文献类型：
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作者：
Keenan, Tiarnan D.;Dharssi, Shazia;Chew, Emily Y.
目的：为了评估深度学习在从彩色眼底照片检测地图样萎缩（GA）中的效用，并探索在检测中央GA（CGA）中的潜在效用。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个额外的模型来检测不同场景中的CGA。参与者：共59812张彩色眼底照片，来自对4582名参与者的纵向随访，这些参与者都是眼动相关眼病研究（AREDS）数据集的参与者。金标准标签来自人类专家阅读中心评分员使用标准化protocol.Methods：训练深度学习模型以使用彩色眼底照片来预测来自无AMD至晚期AMD的眼睛群体的GA存在。训练第二个模型以预测来自相同群体的CGA存在。训练第三模型以预测来自具有GA的眼睛的子集的CGA存在。对于训练和测试，使用5重交叉验证。为了与人类临床医生的性能进行比较，将模型性能与88名视网膜专家的性能进行了比较。主要结果测量：曲线下面积（AUC），准确度，灵敏度，特异性和精确度。深度学习模型（GA检测、来自所有眼睛的CGA检测和来自GA眼睛的中心性检测）的AUC分别为0.933-0.976、0.939-0.976和0.827-0.888。GA检测模型的准确度、灵敏度、特异度和精密度为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），而视网膜专家组分别为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）。中心性检测模型的值为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：深度学习模型对GA的自动检测表现出较高的准确性。AUC不劣于人类视网膜专家。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上公开获取。由Elsevier代表美国眼科学会发布
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. Published by Elsevier on behalf of the American Academy of Ophthalmology