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10.1016/j.compbiomed.2021.104210 http://scihub22266oqcxt.onion/10.1016/j.compbiomed.2021.104210 33550068!7813499!33550068     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 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 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       Comput+Biol+Med 2021 ; 130 (ä): 104210 Nephropedia Template TP {{tp|p=33550068|t=2021. A narrative review on characterization of acute respiratory distress syndrome in COVID-19-infected lungs using artificial intelligence |pdf=|usr=}}{{33550068}} gab.com Text A narrative review on characterization of acute respiratory distress syndrome in COVID-19-infected lungs using artificial intelligence JO: Comput Biol Med http://www.kidney.de/pget.php?&tw=1&pmid=33550068 #FOAvip COVID-19 has infected 77.4 million people worldwide and has caused 1.7 million fatalities as of December 21, 2020. The primary cause of death due to COVID-19 is Acute Respiratory Distress Syndrome (ARDS). According to the World Health Organization (WHO), people who are at least 60 years old or have comorbidities that have primarily been targeted are at the highest risk from SARS-CoV-2. Medical imaging provides a non-invasive, touch-free, and relatively safer alternative tool for diagnosis during the current ongoing pandemic. Artificial intelligence (AI) scientists are developing several intelligent computer-aided diagnosis (CAD) tools in multiple imaging modalities, i.e., lung computed tomography (CT), chest X-rays, and lung ultrasounds. These AI tools assist the pulmonary and critical care clinicians through (a) faster detection of the presence of a virus, (b) classifying pneumonia types, and (c) measuring the severity of viral damage in COVID-19-infected patients. Thus, it is of the utmost importance to fully understand the requirements of for a fast and successful, and timely lung scans analysis. This narrative review first presents the pathological layout of the lungs in the COVID-19 scenario, followed by understanding and then explains the comorbid statistical distributions in the ARDS framework. The novelty of this review is the approach to classifying the AI models as per the by school of thought (SoTs), exhibiting based on segregation of techniques and their characteristics. The study also discusses the identification of AI models and its extension from non-ARDS lungs (pre-COVID-19) to ARDS lungs (post-COVID-19). Furthermore, it also presents AI workflow considerations of for medical imaging modalities in the COVID-19 framework. Finally, clinical AI design considerations will be discussed. We conclude that the design of the current existing AI models can be improved by considering comorbidity as an independent factor. Furthermore, ARDS post-processing clinical systems must involve include (i) the clinical validation and verification of AI-models, (ii) reliability and stability criteria, and (iii) easily adaptable, and (iv) generalization assessments of AI systems for their use in pulmonary, critical care, and radiological settings. Twit Text FOAVip A narrative review on characterization of acute respiratory distress syndrome in COVID-19-infected lungs using artificial intelligence ????Comput Biol Med http://www.kidney.de/pget.php?&tw=1&pmid=33550068 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33550068 #FOAvip English Wikipedia <ref>{{Cite pmid|33550068}}</ref> A narrative review on characterization of acute respiratory distress syndrome in COVID-19-infected lungs using artificial intelligence #MMPMID33550068 Suri JS; Agarwal S; Gupta SK; Puvvula A; Biswas M; Saba L; Bit A; Tandel GS; Agarwal M; Patrick A; Faa G; Singh IM; Oberleitner R; Turk M; Chadha PS; Johri AM; Miguel Sanches J; Khanna NN; Viskovic K; Mavrogeni S; Laird JR; Pareek G; Miner M; Sobel DW; Balestrieri A; Sfikakis PP; Tsoulfas G; Protogerou A; Misra DP; Agarwal V; Kitas GD; Ahluwalia P; Teji J; Al-Maini M; Dhanjil SK; Sockalingam M; Saxena A; Nicolaides A; Sharma A; Rathore V; Ajuluchukwu JNA; Fatemi M; Alizad A; Viswanathan V; Krishnan PK; Naidu S Comput Biol Med 2021[Mar]; 130 (ä): 104210 PMID33550068show ga COVID-19 has infected 77.4 million people worldwide and has caused 1.7 million fatalities as of December 21, 2020. The primary cause of death due to COVID-19 is Acute Respiratory Distress Syndrome (ARDS). According to the World Health Organization (WHO), people who are at least 60 years old or have comorbidities that have primarily been targeted are at the highest risk from SARS-CoV-2. Medical imaging provides a non-invasive, touch-free, and relatively safer alternative tool for diagnosis during the current ongoing pandemic. Artificial intelligence (AI) scientists are developing several intelligent computer-aided diagnosis (CAD) tools in multiple imaging modalities, i.e., lung computed tomography (CT), chest X-rays, and lung ultrasounds. These AI tools assist the pulmonary and critical care clinicians through (a) faster detection of the presence of a virus, (b) classifying pneumonia types, and (c) measuring the severity of viral damage in COVID-19-infected patients. Thus, it is of the utmost importance to fully understand the requirements of for a fast and successful, and timely lung scans analysis. This narrative review first presents the pathological layout of the lungs in the COVID-19 scenario, followed by understanding and then explains the comorbid statistical distributions in the ARDS framework. The novelty of this review is the approach to classifying the AI models as per the by school of thought (SoTs), exhibiting based on segregation of techniques and their characteristics. The study also discusses the identification of AI models and its extension from non-ARDS lungs (pre-COVID-19) to ARDS lungs (post-COVID-19). Furthermore, it also presents AI workflow considerations of for medical imaging modalities in the COVID-19 framework. Finally, clinical AI design considerations will be discussed. We conclude that the design of the current existing AI models can be improved by considering comorbidity as an independent factor. Furthermore, ARDS post-processing clinical systems must involve include (i) the clinical validation and verification of AI-models, (ii) reliability and stability criteria, and (iii) easily adaptable, and (iv) generalization assessments of AI systems for their use in pulmonary, critical care, and radiological settings. |*Artificial Intelligence[MESH] |*SARS-CoV-2[MESH] |*Severity of Illness Index[MESH] |*Tomography, X-Ray Computed[MESH] |COVID-19/*diagnostic imaging[MESH] |Humans[MESH]A narrative review on characterization of acute respiratory distress s(epmc).pdf DeepDyve Pubget Overpricing