Bin Wang
5 Papers
Bin Wang is an academic researcher. The author has contributed to research in topics: Medicine & Fundus (uterus). The author has an hindex of 1, co-authored 3 publications.
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Papers
Application of artificial intelligence system for screening multiple fundus diseases in Chinese primary healthcare settings: a real-world, multicentre and cross-sectional study of 4795 cases
Chufeng Gu,Yujie Wang,Yan-hua Jiang,Feiping Xu,Shasha Wang,Ruiqiang Li,Wen Yuan,Nurbiyimu Abudureyimu,Ying Wang,Yulan Lu,Xiaolong Li,Tao Wu,Li Dong,Yuzhong Chen,Bin Wang,Yuncheng Zhang,Wen Bin Wei,Qinghua Qiu,Zhi Zheng,Deng Liu,Jili Chen +20 more
TL;DR: Wang et al. as mentioned in this paper evaluated the performance of the Airdoc retinal artificial intelligence system (ARAS) for detecting multiple fundus diseases in real-world scenarios in primary healthcare settings and investigated the fundus disease spectrum based on ARAS.
Artificial Intelligence Model for Antiinterference Cataract Automatic Diagnosis: A Diagnostic Accuracy Study
Xing Wu,Di Xu,Tong Ma,Zhaohui Li,Zi-ming Ye,Fei Wang,Xiang Yang Gao,Bin Wang,Yu Chen,Zhaohui Wang,Ji Chen,Yunfeng Hu,Zong Yuan Ge,Da Wang,Qiang Zeng +14 more
TL;DR: An efficient antiinterference AI model for cataract diagnosis is proposed, which could achieve accurateCataract screening even with the interference of poor-quality images and help the government formulate a more accurate aid policy.
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Trends in the Prevalence of Common Retinal and Optic Nerve Diseases in China: An Artificial Intelligence Based National Screening
Ruiheng Zhang,Li Dong,X. Fu,Lin Hua,Wen-Da Zhou,He Yan Li,Haotian Wu,Chuyao Yu,Yi-Tong Li,Xuhan Shi,Yangjie Ou,Bing Zhang,Bin Wang,Zhiqiang Ma,Yuan Luo,Meng Yang,Xiangang Chang,Zhaohui Wang,Wen-guo Wei +18 more
TL;DR: This artificial intelligence-based national screening study applied a previously developed deep learning algorithm to monitor the prevalence of major retinal and optic nerve diseases over a wide geographic area and observed increased prevalence of diabetic retinopathy, hypertensive retinopathy, retinal vein occlusion, and macular hole nationwide.
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Deep Learning Detection of Early Retinal Peripheral Degeneration From Ultra-Widefield Fundus Photographs of Asymptomatic Young Adult (17–19 Years) Candidates to Airforce Cadets
TL;DR: A two-stage deep learning–based framework to detect early retinal peripheral degeneration using UWF images from the Chinese Air Force cadets’ medical selection between February 2016 and June 2022 achieves competitive performance compared to existing baselines while also demonstrating significantly faster inference time.
Automated segmentation of choroidal neovascularization on optical coherence tomography angiography images of neovascular age-related macular degeneration patients based on deep learning
TL;DR: Wang et al. as discussed by the authors used ResNeSt block as their basic backbone, which learns better feature representations through group convolution and split-attention mechanisms, and developed a spatial pyramid pooling module, which uses different receptive fields to extract contextual information at different scales to better segment CNVs of different sizes.