Literature DB >> 33441578

Artificial intelligence matches subjective severity assessment of pneumonia for prediction of patient outcome and need for mechanical ventilation: a cohort study.

Shadi Ebrahimian1, Fatemeh Homayounieh2, Marcio A B C Rockenbach3, Preetham Putha4, Tarun Raj4, Ittai Dayan2,3, Bernardo C Bizzo2,3, Varun Buch3, Dufan Wu2,5, Kyungsang Kim2,5, Quanzheng Li2,5, Subba R Digumarthy2, Mannudeep K Kalra2.   

Abstract

To compare the performance of artificial intelligence (AI) and Radiographic Assessment of Lung Edema (RALE) scores from frontal chest radiographs (CXRs) for predicting patient outcomes and the need for mechanical ventilation in COVID-19 pneumonia. Our IRB-approved study included 1367 serial CXRs from 405 adult patients (mean age 65 ± 16 years) from two sites in the US (Site A) and South Korea (Site B). We recorded information pertaining to patient demographics (age, gender), smoking history, comorbid conditions (such as cancer, cardiovascular and other diseases), vital signs (temperature, oxygen saturation), and available laboratory data (such as WBC count and CRP). Two thoracic radiologists performed the qualitative assessment of all CXRs based on the RALE score for assessing the severity of lung involvement. All CXRs were processed with a commercial AI algorithm to obtain the percentage of the lung affected with findings related to COVID-19 (AI score). Independent t- and chi-square tests were used in addition to multiple logistic regression with Area Under the Curve (AUC) as output for predicting disease outcome and the need for mechanical ventilation. The RALE and AI scores had a strong positive correlation in CXRs from each site (r2 = 0.79-0.86; p < 0.0001). Patients who died or received mechanical ventilation had significantly higher RALE and AI scores than those with recovery or without the need for mechanical ventilation (p < 0.001). Patients with a more substantial difference in baseline and maximum RALE scores and AI scores had a higher prevalence of death and mechanical ventilation (p < 0.001). The addition of patients' age, gender, WBC count, and peripheral oxygen saturation increased the outcome prediction from 0.87 to 0.94 (95% CI 0.90-0.97) for RALE scores and from 0.82 to 0.91 (95% CI 0.87-0.95) for the AI scores. AI algorithm is as robust a predictor of adverse patient outcome (death or need for mechanical ventilation) as subjective RALE scores in patients with COVID-19 pneumonia.

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Year:  2021        PMID: 33441578      PMCID: PMC7807029          DOI: 10.1038/s41598-020-79470-0

Source DB:  PubMed          Journal:  Sci Rep        ISSN: 2045-2322            Impact factor:   4.379


  14 in total

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Journal:  IEEE J Biomed Health Inform       Date:  2020-12-04       Impact factor: 5.772

4.  Initial chest radiographs and artificial intelligence (AI) predict clinical outcomes in COVID-19 patients: analysis of 697 Italian patients.

Authors:  Junaid Mushtaq; Renato Pennella; Salvatore Lavalle; Anna Colarieti; Stephanie Steidler; Carlo M A Martinenghi; Diego Palumbo; Antonio Esposito; Patrizia Rovere-Querini; Moreno Tresoldi; Giovanni Landoni; Fabio Ciceri; Alberto Zangrillo; Francesco De Cobelli
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Journal:  Radiology       Date:  2020-03-27       Impact factor: 11.105

7.  Predicting COVID-19 Pneumonia Severity on Chest X-ray With Deep Learning.

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10.  Chest X-ray in new Coronavirus Disease 2019 (COVID-19) infection: findings and correlation with clinical outcome.

Authors:  Diletta Cozzi; Marco Albanesi; Edoardo Cavigli; Chiara Moroni; Alessandra Bindi; Silvia Luvarà; Silvia Lucarini; Simone Busoni; Lorenzo Nicola Mazzoni; Vittorio Miele
Journal:  Radiol Med       Date:  2020-06-09       Impact factor: 3.469

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  8 in total

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Journal:  Int J Gen Med       Date:  2022-05-20

2.  The Prognostic Capacity of the Radiographic Assessment for Lung Edema Score in Patients With COVID-19 Acute Respiratory Distress Syndrome-An International Multicenter Observational Study.

Authors:  Christel M A Valk; Claudio Zimatore; Guido Mazzinari; Charalampos Pierrakos; Chaisith Sivakorn; Jutamas Dechsanga; Salvatore Grasso; Ludo Beenen; Lieuwe D J Bos; Frederique Paulus; Marcus J Schultz; Luigi Pisani
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3.  Chest Radiograph Scoring Alone or Combined with Other Risk Scores for Predicting Outcomes in COVID-19.

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Journal:  Radiology       Date:  2021-09-14       Impact factor: 11.105

Review 4.  Comprehensive literature review on the radiographic findings, imaging modalities, and the role of radiology in the COVID-19 pandemic.

Authors:  Aman Pal; Abulhassan Ali; Timothy R Young; Juan Oostenbrink; Akul Prabhakar; Amogh Prabhakar; Nina Deacon; Amar Arnold; Ahmed Eltayeb; Charles Yap; David M Young; Alan Tang; Subramanian Lakshmanan; Ying Yi Lim; Martha Pokarowski; Pramath Kakodkar
Journal:  World J Radiol       Date:  2021-09-28

5.  CT-based severity assessment for COVID-19 using weakly supervised non-local CNN.

Authors:  R Karthik; R Menaka; M Hariharan; Daehan Won
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6.  Automatic scoring of COVID-19 severity in X-ray imaging based on a novel deep learning workflow.

Authors:  Diana Litmanovich; Alex Proutski; Viacheslav V Danilov; Alexander Kirpich; Dato Nefaridze; Alex Karpovsky; Yuriy Gankin
Journal:  Sci Rep       Date:  2022-07-27       Impact factor: 4.996

7.  Association of Artificial Intelligence-Aided Chest Radiograph Interpretation With Reader Performance and Efficiency.

Authors:  Jong Seok Ahn; Shadi Ebrahimian; Shaunagh McDermott; Sanghyup Lee; Laura Naccarato; John F Di Capua; Markus Y Wu; Eric W Zhang; Victorine Muse; Benjamin Miller; Farid Sabzalipour; Bernardo C Bizzo; Keith J Dreyer; Parisa Kaviani; Subba R Digumarthy; Mannudeep K Kalra
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8.  The impact of reduction in intensity of mechanical ventilation upon venovenous ECMO initiation on radiographically assessed lung edema scores: A retrospective observational study.

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  8 in total

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