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Ju Yin

As seen in: Nature, Wiley Online Library, Journal of Infection
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Articles

AI-assisted acceleration combined with deep learning of the knee: feasibility of anomaly detection

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By Xue Zhao, Yong Xiang, Ju Yin, Ming Lu
| Nature Verified
Abstract To evaluate the performance of deep learning reconstruction (DLR) for knee magnetic resonance imaging (MRI), and to assess the diagnostic value of combining DLR with artificial intelligence-assisted compressed sensing (ACS) technology in detecting knee abnormalities. This prospective study included 10 healthy volunteers and 40 patients with knee abnormalities. Patients were divided into Conventional (Con), ACS, and ACS + DLR groups.
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Research progress and trends of Mycoplasma pneumoniae pneumonia in children: a bibliometric and visualization analysis (1990s to post-COVID-19 era)

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By Jing Xiao, Yiyi Chen, Weiwei Jiao, Hui Qi, Jinrong Liu, Yuqing Wang, Baoping Xu, Ju Yin, Chengsong Zhao, Lin Sun
| Nature Verified
Abstract The COVID-19 pandemic has affected the epidemiology of Mycoplasma pneumoniae pneumonia (MPP). This study aimed to provide a comprehensive overview of research trends, challenges, and advancements in pediatric MPP, particularly in the post-COVID-19 era. A bibliometric analysis of pediatric MPP literature (1992–2025) from the Web of Science was conducted using VOSviewer and CiteSpace.
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Clinical epidemiological characteristics of hospitalized pediatric Mycoplasma pneumoniae pneumonia in China

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By Xinyu Wang, Ju Yin, Baoping Xu, Xue Guanhua, Hui XU, Yayuan Mei, Guoshuang Feng, Liwei Gao
| Journal of Infection Verified
Keywords Pediatric MPP Severe MPP Clinical epidemiology Multicenter study Introduction Mycoplasma pneumoniae(M. pneumoniae) is a leading cause of community-acquired pneumonia (CAP) in children, accounting for 10%–40% of pediatric CAP cases globally and exhibiting periodic epidemic cycles every 3–7 years.1–3 The COVID-19 pandemic profoundly altered the transmission dynamics of respiratory pathogens.
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