Shin Younji, Cho Hyunsoo, Shin Yong Un, Seong Mincheol, Choi Jun Won, Lee Won June
Department of Electrical Engineering, Hanyang University, Seoul 04763, Korea.
Department of Ophthalmology, Hanyang University College of Medicine, Seoul 04763, Korea.
J Clin Med. 2022 Jun 2;11(11):3168. doi: 10.3390/jcm11113168.
In this retrospective, comparative study, we evaluated and compared the performance of two confocal imaging modalities in detecting glaucoma based on a deep learning (DL) classifier: ultra-wide-field (UWF) fundus imaging and true-colour confocal scanning. A total of 777 eyes, including 273 normal control eyes and 504 glaucomatous eyes, were tested. A convolutional neural network was used for each true-colour confocal scan (Eidon AF™, CenterVue, Padova, Italy) and UWF fundus image (Optomap™, Optos PLC, Dunfermline, UK) to detect glaucoma. The diagnostic model was trained using 545 training and 232 test images. The presence of glaucoma was determined, and the accuracy and area under the receiver operating characteristic curve (AUC) metrics were assessed for diagnostic power comparison. DL-based UWF fundus imaging achieved an AUC of 0.904 (95% confidence interval (CI): 0.861−0.937) and accuracy of 83.62%. In contrast, DL-based true-colour confocal scanning achieved an AUC of 0.868 (95% CI: 0.824−0.912) and accuracy of 81.46%. Both DL-based confocal imaging modalities showed no significant differences in their ability to diagnose glaucoma (p = 0.135) and were comparable to the traditional optical coherence tomography parameter-based methods (all p > 0.005). Therefore, using a DL-based algorithm on true-colour confocal scanning and UWF fundus imaging, we confirmed that both confocal fundus imaging techniques had high value in diagnosing glaucoma.
在这项回顾性比较研究中，我们基于深度学习（DL）分类器评估并比较了两种共聚焦成像模式在检测青光眼方面的性能：超广角（UWF）眼底成像和真彩色共聚焦扫描。总共对777只眼睛进行了测试，其中包括273只正常对照眼和504只青光眼眼。使用卷积神经网络对每张真彩色共聚焦扫描图像（Eidon AF™，CenterVue，意大利帕多瓦）和UWF眼底图像（Optomap™，Optos PLC，英国邓弗姆林）进行青光眼检测。诊断模型使用545张训练图像和232张测试图像进行训练。确定青光眼的存在，并评估准确性和受试者操作特征曲线（AUC）下面积指标以进行诊断能力比较。基于DL的UWF眼底成像的AUC为0.904（95%置信区间（CI）：0.861 - 0.937），准确性为83.62%。相比之下，基于DL的真彩色共聚焦扫描的AUC为0.868（95%CI：0.824 - 0.912），准确性为81.46%。两种基于DL的共聚焦成像模式在诊断青光眼的能力上均无显著差异（p = 0.135），并且与基于传统光学相干断层扫描参数的方法相当（所有p > 0.005）。因此，通过在真彩色共聚焦扫描和UWF眼底成像上使用基于DL的算法，我们证实这两种共聚焦眼底成像技术在诊断青光眼方面都具有很高的价值。
