使用数字眼底图像的深度学习准确诊断青光眼性视神经病变：一项荟萃分析。
Deep Learning for Accurate Diagnosis of Glaucomatous Optic Neuropathy Using Digital Fundus Image: A Meta-Analysis.
作者信息
Islam Mohaimenul, Poly Tahmina Nasrin, Yang Hsuan Chia, Atique Suleman, Li Yu-Chuan Jack
机构信息
Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.
International Center for Health Information and Technology, Taipei Medical University, Taipei, Taiwan.
出版信息
Stud Health Technol Inform. 2020 Jun 16;270:153-157. doi: 10.3233/SHTI200141.
We conducted a study to evaluate the algorithms based on deep learning to automatically diagnosis of GON from digital fundus images. A systematic articles search was conducted in PubMed, EMBASE, Google Scholar for the study that investigated the performance of deep learning algorithms for the detection of GON. A total of eight studies were included in this study, of which 5 studies were used to conduct our meta-analysis. The pooled AUROC for detecting GON was 0.98. However, the sensitivity and specificity of deep learning to detect GON were 0.90 (95% CI: 0.90-0.91), and 0.94 (95%CI: 0.93-0.94), respectively.
我们开展了一项研究，以评估基于深度学习的算法对数字眼底图像进行青光眼自动诊断的效果。我们在PubMed、EMBASE、谷歌学术上系统检索了调查深度学习算法检测青光眼性能的研究。本研究共纳入8项研究，其中5项研究用于进行我们的荟萃分析。检测青光眼的合并曲线下面积（AUROC）为0.98。然而，深度学习检测青光眼的敏感性和特异性分别为0.90（95%置信区间：0.90 - 0.91）和0.94（95%置信区间：0.93 - 0.94）。
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