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Your search keyword '"Wu, Guangyao"' showing total 9 results
Start Over Author "Wu, Guangyao" Publisher wiley
9 results on '"Wu, Guangyao"'

1. Clinical evaluation of pulmonary quantitative computed tomography parameters for diagnosing eosinophilic chronic obstructive pulmonary disease: Characteristics and diagnostic performance

Author

Liu, Yumeng, primary, Lu, Chao, additional, Chen, Wenfang, additional, Liu, Zhenyu, additional, Wu, Songxiong, additional, Ye, Hai, additional, Lv, Yungang, additional, Peng, Zhengkun, additional, Wang, Panying, additional, Li, Guangyao, additional, Tan, Biwen, additional, and Wu, Guangyao, additional

Published
2024
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2. Mutually communicated model based on multi‐parametric MRI for automated segmentation and classification of prostate cancer

Author

Liu, Kewen, primary, Li, Piqiang, additional, Otikovs, Martins, additional, Ning, Xinzhou, additional, Xia, Liyang, additional, Wang, Xiangyu, additional, Yang, Lian, additional, Pan, Feng, additional, Zhang, Zhi, additional, Wu, Guangyao, additional, Xie, Han, additional, Bao, Qingjia, additional, Zhou, Xin, additional, and Liu, Chaoyang, additional

Published
2023
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3. Deep transfer learning of structural magnetic resonance imaging fused with blood parameters improves brain age prediction

Author

Ren, Bingyu, primary, Wu, Yingtong, additional, Huang, Liumei, additional, Zhang, Zhiguo, additional, Huang, Bingsheng, additional, Zhang, Huajie, additional, Ma, Jinting, additional, Li, Bing, additional, Liu, Xukun, additional, Wu, Guangyao, additional, Zhang, Jian, additional, Shen, Liming, additional, Liu, Qiong, additional, and Ni, Jiazuan, additional

Published
2021
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4. Single breath‐hold measurement of pulmonary gas exchange and diffusion in humans with hyperpolarized 129 Xe MR

Author

Xie, Junshuai, primary, Li, Haidong, additional, Zhang, Huiting, additional, Zhao, Xiuchao, additional, Shi, Lei, additional, Zhang, Ming, additional, Xiao, Sa, additional, Deng, He, additional, Wang, Ke, additional, Yang, Hao, additional, Sun, Xianping, additional, Wu, Guangyao, additional, Ye, Chaohui, additional, and Zhou, Xin, additional

Published
2019
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9. Deep transfer learning of structural magnetic resonance imaging fused with blood parameters improves brain age prediction.

Author

Ren B, Wu Y, Huang L, Zhang Z, Huang B, Zhang H, Ma J, Li B, Liu X, Wu G, Zhang J, Shen L, Liu Q, and Ni J

Subjects
Aging, Female, Humans, Machine Learning, Male, Neuroimaging, Brain diagnostic imaging, Brain pathology, Magnetic Resonance Imaging methods
Abstract

Machine learning has been applied to neuroimaging data for estimating brain age and capturing early cognitive impairment in neurodegenerative diseases. Blood parameters like neurofilament light chain are associated with aging. In order to improve brain age predictive accuracy, we constructed a model based on both brain structural magnetic resonance imaging (sMRI) and blood parameters. Healthy subjects (n = 93; 37 males; aged 50-85 years) were recruited. A deep learning network was firstly pretrained on a large set of MRI scans (n = 1,481; 659 males; aged 50-85 years) downloaded from multiple open-source datasets, to provide weights on our recruited dataset. Evaluating the network on the recruited dataset resulted in mean absolute error (MAE) of 4.91 years and a high correlation (r = .67, p <.001) against chronological age. The sMRI data were then combined with five blood biochemical indicators including GLU, TG, TC, ApoA1 and ApoB, and 9 dementia-associated biomarkers including ApoE genotype, HCY, NFL, TREM2, Aβ40, Aβ42, T-tau, TIMP1, and VLDLR to construct a bilinear fusion model, which achieved a more accurate prediction of brain age (MAE, 3.96 years; r = .76, p <.001). Notably, the fusion model achieved better improvement in the group of older subjects (70-85 years). Extracted attention maps of the network showed that amygdala, pallidum, and olfactory were effective for age estimation. Mediation analysis further showed that brain structural features and blood parameters provided independent and significant impact. The constructed age prediction model may have promising potential in evaluation of brain health based on MRI and blood parameters., (© 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Published
2022
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