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10.5334/jbsr.2330 http://scihub22266oqcxt.onion/10.5334/jbsr.2330 33870080!8034398!33870080     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=33870080&cmd=llinks): Failed to open stream: HTTP request failed! 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Comparing Visual Scoring of Lung Injury with a Quantifying AI-Based Scoring in Patients with COVID-19 |pdf=|usr=}}{{33870080}} gab.com Text Comparing Visual Scoring of Lung Injury with a Quantifying AI-Based Scoring in Patients with COVID-19 JO: J Belg Soc Radiol http://www.kidney.de/pget.php?&tw=1&pmid=33870080 #FOAvip OBJECTIVES: Fast diagnosis of Coronavirus Disease 2019 (COVID-19), and the detection of high-risk patients are crucial but challenging in the pandemic outbreak. The aim of this study was to evaluate if deep learning-based software correlates well with the generally accepted visual-based scoring for quantification of the lung injury to help radiologist in triage and monitoring of COVID-19 patients. MATERIALS AND METHODS: In this retrospective study, the lobar analysis of lung opacities (% opacities) by means of a prototype deep learning artificial intelligence (AI)-based software was compared to visual scoring. The visual scoring system used five categories (0: 0%, 1: 0-5%, 2: 5-25%, 3: 25-50%, 4: 50-75% and 5: >75% involvement). The total visual lung injury was obtained by the sum of the estimated grade of involvement of each lobe and divided by five. RESULTS: The dataset consisted of 182 consecutive confirmed COVID-19 positive patients with a median age of 65 +/- 16 years, including 110 (60%) men and 72 (40%) women. There was a correlation coefficient of 0.89 (p < 0.001) between the visual and the AI-based estimates of the severity of lung injury. CONCLUSION: The study indicates a very good correlation between the visual scoring and AI-based estimates of lung injury in COVID-19. Twit Text FOAVip Comparing Visual Scoring of Lung Injury with a Quantifying AI-Based Scoring in Patients with COVID-19 ????J Belg Soc Radiol http://www.kidney.de/pget.php?&tw=1&pmid=33870080 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33870080 #FOAvip English Wikipedia <ref>{{Cite pmid|33870080}}</ref> Comparing Visual Scoring of Lung Injury with a Quantifying AI-Based Scoring in Patients with COVID-19 #MMPMID33870080 Biebau C; Dubbeldam A; Cockmartin L; Coudyze W; Coolen J; Verschakelen J; De Wever W J Belg Soc Radiol 2021[Apr]; 105 (1): 16 PMID33870080show ga OBJECTIVES: Fast diagnosis of Coronavirus Disease 2019 (COVID-19), and the detection of high-risk patients are crucial but challenging in the pandemic outbreak. The aim of this study was to evaluate if deep learning-based software correlates well with the generally accepted visual-based scoring for quantification of the lung injury to help radiologist in triage and monitoring of COVID-19 patients. MATERIALS AND METHODS: In this retrospective study, the lobar analysis of lung opacities (% opacities) by means of a prototype deep learning artificial intelligence (AI)-based software was compared to visual scoring. The visual scoring system used five categories (0: 0%, 1: 0-5%, 2: 5-25%, 3: 25-50%, 4: 50-75% and 5: >75% involvement). The total visual lung injury was obtained by the sum of the estimated grade of involvement of each lobe and divided by five. RESULTS: The dataset consisted of 182 consecutive confirmed COVID-19 positive patients with a median age of 65 +/- 16 years, including 110 (60%) men and 72 (40%) women. There was a correlation coefficient of 0.89 (p < 0.001) between the visual and the AI-based estimates of the severity of lung injury. CONCLUSION: The study indicates a very good correlation between the visual scoring and AI-based estimates of lung injury in COVID-19. äComparing Visual Scoring of Lung Injury with a Quantifying AI-Based Sc(epmc).pdf DeepDyve Pubget Overpricing