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10.5152/dir.2020.20205 http://scihub22266oqcxt.onion/10.5152/dir.2020.20205 32815519!7837735!32815519     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=32815519&cmd=llinks): Failed to open stream: HTTP request failed! 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Determination of disease severity in COVID-19 patients using deep learning in chest X-ray images |pdf=|usr=}}{{32815519}} gab.com Text Determination of disease severity in COVID-19 patients using deep learning in chest X-ray images JO: Diagn Interv Radiol http://www.kidney.de/pget.php?&tw=1&pmid=32815519 #FOAvip PURPOSE: Chest X-ray plays a key role in diagnosis and management of COVID-19 patients and imaging features associated with clinical elements may assist with the development or validation of automated image analysis tools. We aimed to identify associations between clinical and radiographic features as well as to assess the feasibility of deep learning applied to chest X-rays in the setting of an acute COVID-19 outbreak. METHODS: A retrospective study of X-rays, clinical, and laboratory data was performed from 48 SARS-CoV-2 RT-PCR positive patients (age 60+/-17 years, 15 women) between February 22 and March 6, 2020 from a tertiary care hospital in Milan, Italy. Sixty-five chest X-rays were reviewed by two radiologists for alveolar and interstitial opacities and classified by severity on a scale from 0 to 3. Clinical factors (age, symptoms, comorbidities) were investigated for association with opacity severity and also with placement of central line or endotracheal tube. Deep learning models were then trained for two tasks: lung segmentation and opacity detection. Imaging characteristics were compared to clinical datapoints using the unpaired student's t-test or Mann-Whitney U test. Cohen's kappa analysis was used to evaluate the concordance of deep learning to conventional radiologist interpretation. RESULTS: Fifty-six percent of patients presented with alveolar opacities, 73% had interstitial opacities, and 23% had normal X-rays. The presence of alveolar or interstitial opacities was statistically correlated with age (P = 0.008) and comorbidities (P = 0.005). The extent of alveolar or interstitial opacities on baseline X-ray was significantly associated with the presence of endotracheal tube (P = 0.0008 and P = 0.049) or central line (P = 0.003 and P = 0.007). In comparison to human interpretation, the deep learning model achieved a kappa concordance of 0.51 for alveolar opacities and 0.71 for interstitial opacities. CONCLUSION: Chest X-ray analysis in an acute COVID-19 outbreak showed that the severity of opacities was associated with advanced age, comorbidities, as well as acuity of care. Artificial intelligence tools based upon deep learning of COVID-19 chest X-rays are feasible in the acute outbreak setting. Twit Text FOAVip Determination of disease severity in COVID-19 patients using deep learning in chest X-ray images ????Diagn Interv Radiol http://www.kidney.de/pget.php?&tw=1&pmid=32815519 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32815519 #FOAvip English Wikipedia <ref>{{Cite pmid|32815519}}</ref> Determination of disease severity in COVID-19 patients using deep learning in chest X-ray images #MMPMID32815519 Blain M; Kassin MT; Varble N; Wang X; Xu Z; Xu D; Carrafiello G; Vespro V; Stellato E; Ierardi AM; Meglio LD; D Suh R; A Walker S; Xu S; H Sanford T; B Turkbey E; Harmon S; Turkbey B; J Wood B Diagn Interv Radiol 2021[Jan]; 27 (1): 20-27 PMID32815519show ga PURPOSE: Chest X-ray plays a key role in diagnosis and management of COVID-19 patients and imaging features associated with clinical elements may assist with the development or validation of automated image analysis tools. We aimed to identify associations between clinical and radiographic features as well as to assess the feasibility of deep learning applied to chest X-rays in the setting of an acute COVID-19 outbreak. METHODS: A retrospective study of X-rays, clinical, and laboratory data was performed from 48 SARS-CoV-2 RT-PCR positive patients (age 60+/-17 years, 15 women) between February 22 and March 6, 2020 from a tertiary care hospital in Milan, Italy. Sixty-five chest X-rays were reviewed by two radiologists for alveolar and interstitial opacities and classified by severity on a scale from 0 to 3. Clinical factors (age, symptoms, comorbidities) were investigated for association with opacity severity and also with placement of central line or endotracheal tube. Deep learning models were then trained for two tasks: lung segmentation and opacity detection. Imaging characteristics were compared to clinical datapoints using the unpaired student's t-test or Mann-Whitney U test. Cohen's kappa analysis was used to evaluate the concordance of deep learning to conventional radiologist interpretation. RESULTS: Fifty-six percent of patients presented with alveolar opacities, 73% had interstitial opacities, and 23% had normal X-rays. The presence of alveolar or interstitial opacities was statistically correlated with age (P = 0.008) and comorbidities (P = 0.005). The extent of alveolar or interstitial opacities on baseline X-ray was significantly associated with the presence of endotracheal tube (P = 0.0008 and P = 0.049) or central line (P = 0.003 and P = 0.007). In comparison to human interpretation, the deep learning model achieved a kappa concordance of 0.51 for alveolar opacities and 0.71 for interstitial opacities. CONCLUSION: Chest X-ray analysis in an acute COVID-19 outbreak showed that the severity of opacities was associated with advanced age, comorbidities, as well as acuity of care. Artificial intelligence tools based upon deep learning of COVID-19 chest X-rays are feasible in the acute outbreak setting. |Adult[MESH] |Age Factors[MESH] |Aged[MESH] |COVID-19/*diagnosis/epidemiology/therapy/virology[MESH] |Comorbidity[MESH] |Deep Learning/*statistics & numerical data[MESH] |Feasibility Studies[MESH] |Female[MESH] |Humans[MESH] |Italy/epidemiology[MESH] |Male[MESH] |Middle Aged[MESH] |Radiography, Thoracic/classification/*methods[MESH] |Radiologists[MESH] |Retrospective Studies[MESH] |SARS-CoV-2/*genetics[MESH] |Severity of Illness Index[MESH]Determination of disease severity in COVID-19 patients using deep lear(epmc).pdf DeepDyve Pubget Overpricing