Development of a deep-learning system for detection of lattice degeneration, retinal breaks, and retinal detachment in tessellated eyes using ultra-wide-field fundus images: a pilot study,Graefe's Archive for Clinical and Experimental Ophthalmology

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Development of a deep-learning system for detection of lattice degeneration, retinal breaks, and retinal detachment in tessellated eyes using ultra-wide-field fundus images: a pilot study
Graefe's Archive for Clinical and Experimental Ophthalmology ( IF 2.4 ) Pub Date : 2021-02-04 , DOI: 10.1007/s00417-021-05105-3
Chenxi Zhang ₁ , Feng He ₁ , Bing Li ₁ , Hao Wang ₂ , Xixi He ₂ , Xirong Li _{3,

4} , Weihong Yu ₁ , Youxin Chen ₁

Affiliation

Purpose

To investigate the detection of lattice degeneration, retinal breaks, and retinal detachment in tessellated eyes using ultra-wide-field fundus imaging system (Optos) with convolutional neural network technology.

Methods

This study included 1500 Optos color images for tessellated fundus confirmation and peripheral retinal lesion (lattice degeneration, retinal breaks, and retinal detachment) assessment. Three retinal specialists evaluated all images and proposed the reference standard when an agreement was achieved. Then, 722 images were used to train and verify a combined deep-learning system of 3 optimal binary classification models trained using seResNext50 algorithm with 2 preprocessing methods (original resizing and cropping), and a test set of 189 images were applied to verify the performance compared to the reference standard.

Results

With optimal preprocessing approach (original resizing method for lattice degeneration and retinal detachment, cropping method for retinal breaks), the combined deep-learning system exhibited an area under curve of 0.888, 0.953, and 1.000 for detection of lattice degeneration, retinal breaks, and retinal detachment respectively in tessellated eyes. The referral accuracy of this system was 79.8% compared to the reference standard.

Conclusion

A deep-learning system is feasible to detect lattice degeneration, retinal breaks, and retinal detachment in tessellated eyes using ultra-wide-field images. And this system may be considered for screening and telemedicine.

中文翻译：

使用超广角眼底图像检测镶嵌眼中的晶格退化、视网膜断裂和视网膜脱离的深度学习系统的开发：一项初步研究

目的

研究使用超宽视野眼底成像系统 (Optos) 和卷积神经网络技术检测镶嵌眼中的晶格退化、视网膜破裂和视网膜脱离。

方法

该研究包括 1500 张 Optos 彩色图像，用于镶嵌眼底确认和周边视网膜病变（晶格变性、视网膜断裂和视网膜脱离）评估。三位视网膜专家对所有图像进行评估，并在达成一致意见后提出参考标准。然后，使用 722 张图像训练和验证使用 seResNext50 算法和 2 种预处理方法（原始调整大小和裁剪）训练的 3 个最佳二元分类模型的组合深度学习系统，并应用 189 张图像的测试集来验证性能与参考标准相比。