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10.1007/s00330-020-07269-8

http://scihub22266oqcxt.onion/10.1007/s00330-020-07269-8
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32945968!7499014!32945968
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suck abstract from ncbi


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pmid32945968      Eur+Radiol 2021 ; 31 (3): 1770-1779
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  • Initial chest radiographs and artificial intelligence (AI) predict clinical outcomes in COVID-19 patients: analysis of 697 Italian patients #MMPMID32945968
  • Mushtaq J; Pennella R; Lavalle S; Colarieti A; Steidler S; Martinenghi CMA; Palumbo D; Esposito A; Rovere-Querini P; Tresoldi M; Landoni G; Ciceri F; Zangrillo A; De Cobelli F
  • Eur Radiol 2021[Mar]; 31 (3): 1770-1779 PMID32945968show ga
  • OBJECTIVE: To evaluate whether the initial chest X-ray (CXR) severity assessed by an AI system may have prognostic utility in patients with COVID-19. METHODS: This retrospective single-center study included adult patients presenting to the emergency department (ED) between February 25 and April 9, 2020, with SARS-CoV-2 infection confirmed on real-time reverse transcriptase polymerase chain reaction (RT-PCR). Initial CXRs obtained on ED presentation were evaluated by a deep learning artificial intelligence (AI) system and compared with the Radiographic Assessment of Lung Edema (RALE) score, calculated by two experienced radiologists. Death and critical COVID-19 (admission to intensive care unit (ICU) or deaths occurring before ICU admission) were identified as clinical outcomes. Independent predictors of adverse outcomes were evaluated by multivariate analyses. RESULTS: Six hundred ninety-seven 697 patients were included in the study: 465 males (66.7%), median age of 62 years (IQR 52-75). Multivariate analyses adjusting for demographics and comorbidities showed that an AI system-based score >/= 30 on the initial CXR was an independent predictor both for mortality (HR 2.60 (95% CI 1.69 - 3.99; p < 0.001)) and critical COVID-19 (HR 3.40 (95% CI 2.35-4.94; p < 0.001)). Other independent predictors were RALE score, older age, male sex, coronary artery disease, COPD, and neurodegenerative disease. CONCLUSION: AI- and radiologist-assessed disease severity scores on CXRs obtained on ED presentation were independent and comparable predictors of adverse outcomes in patients with COVID-19. TRIAL REGISTRATION: ClinicalTrials.gov NCT04318366 ( https://clinicaltrials.gov/ct2/show/NCT04318366 ). KEY POINTS: * AI system-based score >/= 30 and a RALE score >/= 12 at CXRs performed at ED presentation are independent and comparable predictors of death and/or ICU admission in COVID-19 patients. * Other independent predictors are older age, male sex, coronary artery disease, COPD, and neurodegenerative disease. * The comparable performance of the AI system in relation to a radiologist-assessed score in predicting adverse outcomes may represent a game-changer in resource-constrained settings.
  • |*Deep Learning[MESH]
  • |*Radiography, Thoracic[MESH]
  • |Age Factors[MESH]
  • |Aged[MESH]
  • |Artificial Intelligence[MESH]
  • |COVID-19/*diagnostic imaging/epidemiology/mortality/physiopathology[MESH]
  • |Comorbidity[MESH]
  • |Coronary Artery Disease/epidemiology[MESH]
  • |Emergency Service, Hospital[MESH]
  • |Female[MESH]
  • |Hospitalization[MESH]
  • |Humans[MESH]
  • |Intensive Care Units/*statistics & numerical data[MESH]
  • |Italy/epidemiology[MESH]
  • |Male[MESH]
  • |Middle Aged[MESH]
  • |Mortality[MESH]
  • |Neurodegenerative Diseases/epidemiology[MESH]
  • |Prognosis[MESH]
  • |Proportional Hazards Models[MESH]
  • |Pulmonary Disease, Chronic Obstructive/epidemiology[MESH]
  • |Radiography[MESH]
  • |Retrospective Studies[MESH]
  • |SARS-CoV-2[MESH]
  • |Severity of Illness Index[MESH]
  • |Sex Factors[MESH]


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