![]() ![]() ![]() The reference standard was determined when an agreement was achieved among all 3 ophthalmologists, or adjudicated by another retinal specialist if disagreements existed. All images were classified by 3 experienced ophthalmologists. Methods: A total of 5,606 UWF images from 2,566 participants were used to train and verify a DL system. Therefore, we aimed to develop and evaluate a deep learning (DL) system for automated identifying NPRLs based on ultra-widefield fundus (UWF) images. However, screening NPRLs is time-consuming and labor-intensive. #These authors contributed equally to this work.īackground: Lattice degeneration and/or retinal breaks, defined as notable peripheral retinal lesions (NPRLs), are prone to evolving into rhegmatogenous retinal detachment which can cause severe visual loss. Zhongwen Li 1#, Chong Guo 1#, Danyao Nie 2#, Duoru Lin 1, Yi Zhu 1,3, Chuan Chen 1,3, Li Zhang 1, Fabao Xu 1, Chenjin Jin 1, Xiayin Zhang 1, Hui Xiao 1, Kai Zhang 1,4, Lanqin Zhao 1, Shanshan Yu 1, Guoming Zhang 2, Jiantao Wang 2, Haotian Lin 1ġState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China 2Shenzhen Ophthalmic Center, Jinan University, Shenzhen 518001, China 3Department of Molecular and Cellular Pharmacology, University of Miami Miller School of Medicine, Miami, Florida, USA 4School of Computer Science and Technology, Xidian University, Xi’an 710071, ChinaĬontributions: (I) Conception and design: Z Li, C Guo, D Nie, J Wang, H Lin (II) Administrative support: H Lin (III) Provision of study materials or patients: J Wang, G Zhang, D Nie (IV) Collection and assembly of data: Z Li, D Lin, L Zhang, F Xu, X Zhang, H Xiao, L Zhao, S Yu (V) Data analysis and interpretation: Z Li, C Guo, D Nie, L Zhang, F Xu, H Lin, D Lin, Y Zhu, C Chen, C Jin, K Zhang, J Wang, G Zhang, H Xiao, L Zhao (VI) Manuscript writing: All authors (VII) Final approval of manuscript: All authors. ![]() Policy of Dealing with Allegations of Research Misconduct.Policy of Screening for Plagiarism Process. ![]()
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