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10.3233/XST-200735 http://scihub22266oqcxt.onion/10.3233/XST-200735 33164982!7990455!33164982     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Xray+Sci+Technol 2021 ; 29 (1): 1-17 Nephropedia Template TP {{tp|p=33164982|t=2021. Using artificial intelligence to assist radiologists in distinguishing COVID-19 from other pulmonary infections |pdf=|usr=}}{{33164982}} gab.com Text Using artificial intelligence to assist radiologists in distinguishing COVID-19 from other pulmonary infections JO: J Xray Sci Technol http://www.kidney.de/pget.php?&tw=1&pmid=33164982 #FOAvip BACKGROUND: Accurate and rapid diagnosis of coronavirus disease (COVID-19) is crucial for timely quarantine and treatment. PURPOSE: In this study, a deep learning algorithm-based AI model using ResUNet network was developed to evaluate the performance of radiologists with and without AI assistance in distinguishing COVID-19 infected pneumonia patients from other pulmonary infections on CT scans. METHODS: For model development and validation, a total number of 694 cases with 111,066 CT slides were retrospectively collected as training data and independent test data in the study. Among them, 118 are confirmed COVID-19 infected pneumonia cases and 576 are other pulmonary infection cases (e.g. tuberculosis cases, common pneumonia cases and non-COVID-19 viral pneumonia cases). The cases were divided into training and testing datasets. The independent test was performed by evaluating and comparing the performance of three radiologists with different years of practice experience in distinguishing COVID-19 infected pneumonia cases with and without the AI assistance. RESULTS: Our final model achieved an overall test accuracy of 0.914 with an area of the receiver operating characteristic (ROC) curve (AUC) of 0.903 in which the sensitivity and specificity are 0.918 and 0.909, respectively. The deep learning-based model then achieved a comparable performance by improving the radiologists' performance in distinguish COVOD-19 from other pulmonary infections, yielding better average accuracy and sensitivity, from 0.941 to 0.951 and from 0.895 to 0.942, respectively, when compared to radiologists without using AI assistance. CONCLUSION: A deep learning algorithm-based AI model developed in this study successfully improved radiologists' performance in distinguishing COVID-19 from other pulmonary infections using chest CT images. Twit Text FOAVip Using artificial intelligence to assist radiologists in distinguishing COVID-19 from other pulmonary infections ????J Xray Sci Technol http://www.kidney.de/pget.php?&tw=1&pmid=33164982 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33164982 #FOAvip English Wikipedia <ref>{{Cite pmid|33164982}}</ref> Using artificial intelligence to assist radiologists in distinguishing COVID-19 from other pulmonary infections #MMPMID33164982 Yang Y; Lure FYM; Miao H; Zhang Z; Jaeger S; Liu J; Guo L J Xray Sci Technol 2021[]; 29 (1): 1-17 PMID33164982show ga BACKGROUND: Accurate and rapid diagnosis of coronavirus disease (COVID-19) is crucial for timely quarantine and treatment. PURPOSE: In this study, a deep learning algorithm-based AI model using ResUNet network was developed to evaluate the performance of radiologists with and without AI assistance in distinguishing COVID-19 infected pneumonia patients from other pulmonary infections on CT scans. METHODS: For model development and validation, a total number of 694 cases with 111,066 CT slides were retrospectively collected as training data and independent test data in the study. Among them, 118 are confirmed COVID-19 infected pneumonia cases and 576 are other pulmonary infection cases (e.g. tuberculosis cases, common pneumonia cases and non-COVID-19 viral pneumonia cases). The cases were divided into training and testing datasets. The independent test was performed by evaluating and comparing the performance of three radiologists with different years of practice experience in distinguishing COVID-19 infected pneumonia cases with and without the AI assistance. RESULTS: Our final model achieved an overall test accuracy of 0.914 with an area of the receiver operating characteristic (ROC) curve (AUC) of 0.903 in which the sensitivity and specificity are 0.918 and 0.909, respectively. The deep learning-based model then achieved a comparable performance by improving the radiologists' performance in distinguish COVOD-19 from other pulmonary infections, yielding better average accuracy and sensitivity, from 0.941 to 0.951 and from 0.895 to 0.942, respectively, when compared to radiologists without using AI assistance. CONCLUSION: A deep learning algorithm-based AI model developed in this study successfully improved radiologists' performance in distinguishing COVID-19 from other pulmonary infections using chest CT images. |*Artificial Intelligence[MESH] |*Radiologists/statistics & numerical data[MESH] |Adult[MESH] |Aged[MESH] |Algorithms[MESH] |COVID-19/*diagnostic imaging[MESH] |Clinical Competence/statistics & numerical data[MESH] |Deep Learning[MESH] |Diagnosis, Differential[MESH] |Female[MESH] |Humans[MESH] |Lung/diagnostic imaging/pathology[MESH] |Male[MESH] |Middle Aged[MESH] |Respiratory Tract Infections/diagnostic imaging[MESH] |SARS-CoV-2[MESH] |Sensitivity and Specificity[MESH] |Tomography, X-Ray Computed/*methods[MESH]Using artificial intelligence to assist radiologists in distinguishing(epmc).pdf DeepDyve Pubget Overpricing