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/s11548-020-02299-5 http://scihub22266oqcxt.onion/10.1007/s11548-020-02299-5 33484428!7822756!33484428     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       Int+J+Comput+Assist+Radiol+Surg 2021 ; 16 (3): 435-445 Nephropedia Template TP {{tp|p=33484428|t=2021. Association of AI quantified COVID-19 chest CT and patient outcome |pdf=|usr=}}{{33484428}} gab.com Text Association of AI quantified COVID-19 chest CT and patient outcome JO: Int J Comput Assist Radiol Surg http://www.kidney.de/pget.php?&tw=1&pmid=33484428 #FOAvip PURPOSE: Severity scoring is a key step in managing patients with COVID-19 pneumonia. However, manual quantitative analysis by radiologists is a time-consuming task, while qualitative evaluation may be fast but highly subjective. This study aims to develop artificial intelligence (AI)-based methods to quantify disease severity and predict COVID-19 patient outcome. METHODS: We develop an AI-based framework that employs deep neural networks to efficiently segment lung lobes and pulmonary opacities. The volume ratio of pulmonary opacities inside each lung lobe gives the severity scores of the lobes, which are then used to predict ICU admission and mortality with three different machine learning methods. The developed methods were evaluated on datasets from two hospitals (site A: Firoozgar Hospital, Iran, 105 patients; site B: Massachusetts General Hospital, USA, 88 patients). RESULTS: AI-based severity scores are strongly associated with those evaluated by radiologists (Spearman's rank correlation 0.837, [Formula: see text]). Using AI-based scores produced significantly higher ([Formula: see text]) area under the ROC curve (AUC) values. The developed AI method achieved the best performance of AUC = 0.813 (95% CI [0.729, 0.886]) in predicting ICU admission and AUC = 0.741 (95% CI [0.640, 0.837]) in mortality estimation on the two datasets. CONCLUSIONS: Accurate severity scores can be obtained using the developed AI methods over chest CT images. The computed severity scores achieved better performance than radiologists in predicting COVID-19 patient outcome by consistently quantifying image features. Such developed techniques of severity assessment may be extended to other lung diseases beyond the current pandemic. Twit Text FOAVip Association of AI quantified COVID-19 chest CT and patient outcome ????Int J Comput Assist Radiol Surg http://www.kidney.de/pget.php?&tw=1&pmid=33484428 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33484428 #FOAvip English Wikipedia <ref>{{Cite pmid|33484428}}</ref> Association of AI quantified COVID-19 chest CT and patient outcome #MMPMID33484428 Fang X; Kruger U; Homayounieh F; Chao H; Zhang J; Digumarthy SR; Arru CD; Kalra MK; Yan P Int J Comput Assist Radiol Surg 2021[Mar]; 16 (3): 435-445 PMID33484428show ga PURPOSE: Severity scoring is a key step in managing patients with COVID-19 pneumonia. However, manual quantitative analysis by radiologists is a time-consuming task, while qualitative evaluation may be fast but highly subjective. This study aims to develop artificial intelligence (AI)-based methods to quantify disease severity and predict COVID-19 patient outcome. METHODS: We develop an AI-based framework that employs deep neural networks to efficiently segment lung lobes and pulmonary opacities. The volume ratio of pulmonary opacities inside each lung lobe gives the severity scores of the lobes, which are then used to predict ICU admission and mortality with three different machine learning methods. The developed methods were evaluated on datasets from two hospitals (site A: Firoozgar Hospital, Iran, 105 patients; site B: Massachusetts General Hospital, USA, 88 patients). RESULTS: AI-based severity scores are strongly associated with those evaluated by radiologists (Spearman's rank correlation 0.837, [Formula: see text]). Using AI-based scores produced significantly higher ([Formula: see text]) area under the ROC curve (AUC) values. The developed AI method achieved the best performance of AUC = 0.813 (95% CI [0.729, 0.886]) in predicting ICU admission and AUC = 0.741 (95% CI [0.640, 0.837]) in mortality estimation on the two datasets. CONCLUSIONS: Accurate severity scores can be obtained using the developed AI methods over chest CT images. The computed severity scores achieved better performance than radiologists in predicting COVID-19 patient outcome by consistently quantifying image features. Such developed techniques of severity assessment may be extended to other lung diseases beyond the current pandemic. |*Artificial Intelligence[MESH] |Adult[MESH] |Aged[MESH] |Aged, 80 and over[MESH] |COVID-19/*diagnostic imaging[MESH] |Databases, Factual[MESH] |Female[MESH] |Hospitalization[MESH] |Humans[MESH] |Lung/diagnostic imaging[MESH] |Male[MESH] |Middle Aged[MESH] |Neural Networks, Computer[MESH] |Pandemics[MESH] |Prognosis[MESH] |Retrospective Studies[MESH] |Severity of Illness Index[MESH] |Thorax/*diagnostic imaging[MESH] |Tomography, X-Ray Computed/methods[MESH]Association of AI quantified COVID-19 chest CT and patient outcome (epmc).pdf DeepDyve Pubget Overpricing