A deep network DeepOpacityNet for detection of cataracts from color fundus photographs.
A deep network DeepOpacityNet for detection of cataracts from color fundus photographs.
复制标题
用于从彩色眼底照片中检测白内障的深度网络 DeepOpacityNet。
DOI：
10.1038/s43856-023-00410-w
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发表时间：
2023-12-16
期刊：
影响因子：
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通讯作者：
Lu, Zhiyong
中科院分区：
文献类型：
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作者：
Elsawy, Amr;Keenan, Tiarnan D L;Chen, Qingyu;Thavikulwat, Alisa T;Bhandari, Sanjeeb;Quek, Ten Cheer;Goh, Jocelyn Hui Lin;Tham, Yih-Chung;Cheng, Ching-Yu;Chew, Emily Y;Lu, Zhiyong
白内障的诊断通常需要眼科医生亲自进行评估。然而，彩色眼底照相(CFP)在眼科诊所外被广泛使用，这可以被利用来通过自动检测来增加白内障筛查的可及性。DeepOpacityNet是为了从CFP中检测白内障并突出与白内障相关的CFP特征而开发的。我们使用了来自年龄相关眼病研究2(AREDS2)数据集的2573名AREDS2参与者的17,514名CFP，其中8681名CFP被标记为白内障。从核性白内障的裂隙灯检查和皮质和后囊下白内障的前段照片的阅读中心分级中转移地面真实标签。DeepOpacityNet在独立的测试集(20%)上进行了内部验证，而在测试集的子集上进行了三名眼科医生(100 CFPS)的内部验证，在从新加坡眼病流行病学研究(SEED)获得的三个数据集上进行了外部验证，并进行了可视化以突出重要特征。在内部，DeepOpacityNet与其他最先进的方法相比，获得了0.66(95%可信区间：0.64-0.68)的卓越准确度和0.72(95%可信区间：0.70-0.74)的曲线下面积(AUC)。DeepOpacityNet的准确率为0.75，而表现最好的眼科医生的准确率为0.67。在外部，DeepOpacityNet在种子数据集上的AUC得分分别为0.86、0.88和0.89，证明了我们所提出的方法的泛化能力。可视化显示血管的可见性可能是无白内障的特征，而模糊的区域可能是有白内障的特征。DeepOpacityNet能够从AREDS2中的CFP中检测出白内障，其性能优于眼科医生，并产生可解释的结果。代码和模型可在https://github.com/ncbi/DeepOpacityNet(10.5281/zenod.10127002)上找到。白内障是眼睛中影响视力的混浊区域。诊断通常需要眼科医生亲自进行评估。在这项研究中，开发了一个计算机程序，可以从眼睛的专业照片中识别白内障。计算机程序成功地识别了白内障，并且比眼科医生更能识别这些白内障。该计算机程序可用于提高眼科诊所对白内障的诊断水平。Elsawy，Keenan，Chen等人。使用名为DeepOpacityNet的可解释的深度学习网络从彩色眼底摄影中检测白内障。DeepOpacityNet比眼科医生更准确地检测白内障，并证明没有血管是白内障存在的指示器。
Cataract diagnosis typically requires in-person evaluation by an ophthalmologist. However, color fundus photography (CFP) is widely performed outside ophthalmology clinics, which could be exploited to increase the accessibility of cataract screening by automated detection. DeepOpacityNet was developed to detect cataracts from CFP and highlight the most relevant CFP features associated with cataracts. We used 17,514 CFPs from 2573 AREDS2 participants curated from the Age-Related Eye Diseases Study 2 (AREDS2) dataset, of which 8681 CFPs were labeled with cataracts. The ground truth labels were transferred from slit-lamp examination of nuclear cataracts and reading center grading of anterior segment photographs for cortical and posterior subcapsular cataracts. DeepOpacityNet was internally validated on an independent test set (20%), compared to three ophthalmologists on a subset of the test set (100 CFPs), externally validated on three datasets obtained from the Singapore Epidemiology of Eye Diseases study (SEED), and visualized to highlight important features. Internally, DeepOpacityNet achieved a superior accuracy of 0.66 (95% confidence interval (CI): 0.64–0.68) and an area under the curve (AUC) of 0.72 (95% CI: 0.70–0.74), compared to that of other state-of-the-art methods. DeepOpacityNet achieved an accuracy of 0.75, compared to an accuracy of 0.67 for the ophthalmologist with the highest performance. Externally, DeepOpacityNet achieved AUC scores of 0.86, 0.88, and 0.89 on SEED datasets, demonstrating the generalizability of our proposed method. Visualizations show that the visibility of blood vessels could be characteristic of cataract absence while blurred regions could be characteristic of cataract presence. DeepOpacityNet could detect cataracts from CFPs in AREDS2 with performance superior to that of ophthalmologists and generate interpretable results. The code and models are available at https://github.com/ncbi/DeepOpacityNet (10.5281/zenodo.10127002). Cataracts are cloudy areas in the eye that impact sight. Diagnosis typically requires in-person evaluation by an ophthalmologist. In this study, a computer program was developed that can identify cataracts from specialist photographs of the eye. The computer program successfully identified cataracts and was better able to identify these than ophthalmologists. This computer program could be introduced to improve the diagnosis of cataracts in eye clinics. Elsawy, Keenan, Chen et al. detect cataracts from color fundus photography using an explainable deep learning network called DeepOpacityNet. DeepOpacityNet detects cataracts more accurately than ophthalmologists and demonstrates that the absence of blood vessels is an indicator that cataracts are present.