Retinal detachment can lead to severe visual loss if not treated timely. The early diagnosis of retinal detachment can improve the rate of successful reattachment and the visual results, especially before macular involvement. Manual retinal detachment screening is time-consuming and labour-intensive, which is difficult for large-scale clinical applications. In this study, we developed a cascaded deep learning system based on the ultra-widefield fundus images for automated retinal detachment detection and macula-on/off retinal detachment discerning. The performance of this system is reliable and comparable to an experienced ophthalmologist. In addition, this system can automatically provide guidance to patients regarding appropriate preoperative posturing to reduce retinal detachment progression and the urgency of retinal detachment repair. The implementation of this system on a global scale may drastically reduce the extent of vision impairment resulting from retinal detachment by providing timely identification and referral.

中文翻译：

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使用超广角眼底图像检测视网膜脱离并识别黄斑状态的深度学习。

如果不及时治疗，视网膜脱离会导致严重的视力丧失。视网膜脱离的早期诊断可以提高成功的再附着率和视觉效果，尤其是在黄斑部受累之前。人工视网膜脱离筛查既费时又费力，这对于大规模临床应用而言是困难的。在这项研究中，我们开发了基于超广角眼底图像的级联深度学习系统，用于自动视网膜脱离检测和黄斑开/关视网膜脱离的识别。该系统的性能可靠，可与经验丰富的眼科医生媲美。此外，该系统可以自动为患者提供适当的术前姿势指导，以减少视网膜脱离的进展和视网膜脱离修复的紧迫性。