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10.1186/s12938-021-00863-x http://scihub22266oqcxt.onion/10.1186/s12938-021-00863-x 33743707!7980736!33743707     free     free     free Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Biomed+Eng+Online 2021 ; 20 (1): 27 Nephropedia Template TP {{tp|p=33743707|t=2021. Evaluation of lung involvement in COVID-19 pneumonia based on ultrasound images |pdf=|usr=}}{{33743707}} gab.com Text Evaluation of lung involvement in COVID-19 pneumonia based on ultrasound images JO: Biomed Eng Online http://www.kidney.de/pget.php?&tw=1&pmid=33743707 #FOAvip BACKGROUND: Lung ultrasound (LUS) can be an important imaging tool for the diagnosis and assessment of lung involvement. Ultrasound sonograms have been confirmed to illustrate damage to a person's lungs, which means that the correct classification and scoring of a patient's sonogram can be used to assess lung involvement. METHODS: The purpose of this study was to establish a lung involvement assessment model based on deep learning. A novel multimodal channel and receptive field attention network combined with ResNeXt (MCRFNet) was proposed to classify sonograms, and the network can automatically fuse shallow features and determine the importance of different channels and respective fields. Finally, sonogram classes were transformed into scores to evaluate lung involvement from the initial diagnosis to rehabilitation. RESULTS AND CONCLUSION: Using multicenter and multimodal ultrasound data from 104 patients, the diagnostic model achieved 94.39% accuracy, 82.28% precision, 76.27% sensitivity, and 96.44% specificity. The lung involvement severity and the trend of COVID-19 pneumonia were evaluated quantitatively. Twit Text FOAVip Evaluation of lung involvement in COVID-19 pneumonia based on ultrasound images ????Biomed Eng Online http://www.kidney.de/pget.php?&tw=1&pmid=33743707 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33743707 #FOAvip English Wikipedia <ref>{{Cite pmid|33743707}}</ref> Evaluation of lung involvement in COVID-19 pneumonia based on ultrasound images #MMPMID33743707 Hu Z; Liu Z; Dong Y; Liu J; Huang B; Liu A; Huang J; Pu X; Shi X; Yu J; Xiao Y; Zhang H; Zhou J Biomed Eng Online 2021[Mar]; 20 (1): 27 PMID33743707show ga BACKGROUND: Lung ultrasound (LUS) can be an important imaging tool for the diagnosis and assessment of lung involvement. Ultrasound sonograms have been confirmed to illustrate damage to a person's lungs, which means that the correct classification and scoring of a patient's sonogram can be used to assess lung involvement. METHODS: The purpose of this study was to establish a lung involvement assessment model based on deep learning. A novel multimodal channel and receptive field attention network combined with ResNeXt (MCRFNet) was proposed to classify sonograms, and the network can automatically fuse shallow features and determine the importance of different channels and respective fields. Finally, sonogram classes were transformed into scores to evaluate lung involvement from the initial diagnosis to rehabilitation. RESULTS AND CONCLUSION: Using multicenter and multimodal ultrasound data from 104 patients, the diagnostic model achieved 94.39% accuracy, 82.28% precision, 76.27% sensitivity, and 96.44% specificity. The lung involvement severity and the trend of COVID-19 pneumonia were evaluated quantitatively. |*Ultrasonography[MESH] |Algorithms[MESH] |COVID-19/*diagnostic imaging[MESH] |Databases, Factual[MESH] |False Positive Reactions[MESH] |Humans[MESH] |Image Processing, Computer-Assisted/methods[MESH] |Lung/*diagnostic imaging[MESH] |Models, Statistical[MESH] |Neural Networks, Computer[MESH] |Pneumonia/*diagnostic imaging[MESH] |Programming Languages[MESH] |Reproducibility of Results[MESH] |Sensitivity and Specificity[MESH]Evaluation of lung involvement in COVID-19 pneumonia based on ultrasou(epmc).pdf DeepDyve Pubget Overpricing