Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText. Kick-your-searchterm to multiple Engines kick-your-query now !>

A dictionary by aggregated review articles of nephrology, medicine and the life sciences Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

10.1007/s00330-020-07044-9 http://scihub22266oqcxt.onion/10.1007/s00330-020-07044-9 32617690!7331494!32617690     free     free     free Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 267.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Eur+Radiol 2020 ; 30 (12): 6517-6527 Nephropedia Template TP {{tp|p=32617690|t=2020. A deep learning approach to characterize 2019 coronavirus disease (COVID-19) pneumonia in chest CT images |pdf=|usr=}}{{32617690}} gab.com Text A deep learning approach to characterize 2019 coronavirus disease (COVID-19) pneumonia in chest CT images JO: Eur Radiol http://www.kidney.de/pget.php?&tw=1&pmid=32617690 #FOAvip OBJECTIVES: To utilize a deep learning model for automatic detection of abnormalities in chest CT images from COVID-19 patients and compare its quantitative determination performance with radiological residents. METHODS: A deep learning algorithm consisted of lesion detection, segmentation, and location was trained and validated in 14,435 participants with chest CT images and definite pathogen diagnosis. The algorithm was tested in a non-overlapping dataset of 96 confirmed COVID-19 patients in three hospitals across China during the outbreak. Quantitative detection performance of the model was compared with three radiological residents with two experienced radiologists' reading reports as reference standard by assessing the accuracy, sensitivity, specificity, and F1 score. RESULTS: Of 96 patients, 88 had pneumonia lesions on CT images and 8 had no abnormities on CT images. For per-patient basis, the algorithm showed superior sensitivity of 1.00 (95% confidence interval (CI) 0.95, 1.00) and F1 score of 0.97 in detecting lesions from CT images of COVID-19 pneumonia patients. While for per-lung lobe basis, the algorithm achieved a sensitivity of 0.96 (95% CI 0.94, 0.98) and a slightly inferior F1 score of 0.86. The median volume of lesions calculated by algorithm was 40.10 cm(3). An average running speed of 20.3 s +/- 5.8 per case demonstrated the algorithm was much faster than the residents in assessing CT images (all p < 0.017). The deep learning algorithm can also assist radiologists make quicker diagnosis (all p < 0.0001) with superior diagnostic performance. CONCLUSIONS: The algorithm showed excellent performance in detecting COVID-19 pneumonia on chest CT images compared with resident radiologists. KEY POINTS: * The higher sensitivity of deep learning model in detecting COVID-19 pneumonia were found compared with radiological residents on a per-lobe and per-patient basis. * The deep learning model improves diagnosis efficiency by shortening processing time. * The deep learning model can automatically calculate the volume of the lesions and whole lung. Twit Text FOAVip A deep learning approach to characterize 2019 coronavirus disease (COVID-19) pneumonia in chest CT images ????Eur Radiol http://www.kidney.de/pget.php?&tw=1&pmid=32617690 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32617690 #FOAvip English Wikipedia <ref>{{Cite pmid|32617690}}</ref> A deep learning approach to characterize 2019 coronavirus disease (COVID-19) pneumonia in chest CT images #MMPMID32617690 Ni Q; Sun ZY; Qi L; Chen W; Yang Y; Wang L; Zhang X; Yang L; Fang Y; Xing Z; Zhou Z; Yu Y; Lu GM; Zhang LJ Eur Radiol 2020[Dec]; 30 (12): 6517-6527 PMID32617690show ga OBJECTIVES: To utilize a deep learning model for automatic detection of abnormalities in chest CT images from COVID-19 patients and compare its quantitative determination performance with radiological residents. METHODS: A deep learning algorithm consisted of lesion detection, segmentation, and location was trained and validated in 14,435 participants with chest CT images and definite pathogen diagnosis. The algorithm was tested in a non-overlapping dataset of 96 confirmed COVID-19 patients in three hospitals across China during the outbreak. Quantitative detection performance of the model was compared with three radiological residents with two experienced radiologists' reading reports as reference standard by assessing the accuracy, sensitivity, specificity, and F1 score. RESULTS: Of 96 patients, 88 had pneumonia lesions on CT images and 8 had no abnormities on CT images. For per-patient basis, the algorithm showed superior sensitivity of 1.00 (95% confidence interval (CI) 0.95, 1.00) and F1 score of 0.97 in detecting lesions from CT images of COVID-19 pneumonia patients. While for per-lung lobe basis, the algorithm achieved a sensitivity of 0.96 (95% CI 0.94, 0.98) and a slightly inferior F1 score of 0.86. The median volume of lesions calculated by algorithm was 40.10 cm(3). An average running speed of 20.3 s +/- 5.8 per case demonstrated the algorithm was much faster than the residents in assessing CT images (all p < 0.017). The deep learning algorithm can also assist radiologists make quicker diagnosis (all p < 0.0001) with superior diagnostic performance. CONCLUSIONS: The algorithm showed excellent performance in detecting COVID-19 pneumonia on chest CT images compared with resident radiologists. KEY POINTS: * The higher sensitivity of deep learning model in detecting COVID-19 pneumonia were found compared with radiological residents on a per-lobe and per-patient basis. * The deep learning model improves diagnosis efficiency by shortening processing time. * The deep learning model can automatically calculate the volume of the lesions and whole lung. |*Algorithms[MESH] |*Betacoronavirus[MESH] |*Deep Learning[MESH] |*Pandemics[MESH] |COVID-19[MESH] |China/epidemiology[MESH] |Coronavirus Infections/*diagnosis/*epidemiology[MESH] |Female[MESH] |Humans[MESH] |Lung/*diagnostic imaging[MESH] |Male[MESH] |Middle Aged[MESH] |Pneumonia, Viral/*diagnosis/*epidemiology[MESH] |SARS-CoV-2[MESH]A deep learning approach to characterize 2019 coronavirus disease (COV(epmc).pdf DeepDyve Pubget Overpricing