Accuracy of artificial intelligence CT quantification in predicting COVID-19 subjects’ prognosis
Arvin Arian, Mohammad-Mehdi Mehrabi Nejad, Mostafa Zoorpaikar, Navid Hasanzadeh, Saman Sotoudeh-Paima, Shahriar Kolahi, Masoumeh Gity, Hamid Soltanian-Zadeh
PLOS ONE, doi:10.1371/journal.pone.0294899
Background Artificial intelligence (AI)-aided analysis of chest CT expedites the quantification of abnormalities and may facilitate the diagnosis and assessment of the prognosis of subjects with COVID-19.
Objectives This study investigates the performance of an AI-aided quantification model in predicting the clinical outcomes of hospitalized subjects with COVID-19 and compares it with radiologists' performance.
Subjects and methods A total of 90 subjects with COVID-19 (men, n = 59 [65.6%]; age, 52.9±16.7 years) were recruited in this cross-sectional study. Quantification of the total and compromised lung parenchyma was performed by two expert radiologists using a volumetric image analysis software and compared against an AI-assisted package consisting of a modified U-Net model for segmenting COVID-19 lesions and an off-the-shelf U-Net model augmented with COVID-19 data for segmenting lung volume. The fraction of compromised lung parenchyma (%CL) was calculated. Based on clinical results, the subjects were divided into two categories: critical (n = 45) and noncritical (n = 45). All admission data were compared between the two groups.
Results There was an excellent agreement between the radiologist-obtained and AI-assisted measurements (intraclass correlation coefficient = 0.88, P < 0.001). Both the AI-assisted and
Author Contributions Conceptualization: Arvin Arian. Validation: Shahriar Kolahi, Masoumeh Gity.
Data curation: Visualization: Navid Hasanzadeh. Writing -original draft: Arvin Arian, Mostafa Zoorpaikar, Saman Sotoudeh-Paima.
Writing -review & editing: Hamid Soltanian-Zadeh.
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'abstract': '<jats:sec id="sec001">\n'
'<jats:title>Background</jats:title>\n'
'<jats:p>Artificial intelligence (AI)-aided analysis of chest CT expedites the quantification '
'of abnormalities and may facilitate the diagnosis and assessment of the prognosis of subjects '
'with COVID-19.</jats:p>\n'
'</jats:sec>\n'
'<jats:sec id="sec002">\n'
'<jats:title>Objectives</jats:title>\n'
'<jats:p>This study investigates the performance of an AI-aided quantification model in '
'predicting the clinical outcomes of hospitalized subjects with COVID-19 and compares it with '
'radiologists’ performance.</jats:p>\n'
'</jats:sec>\n'
'<jats:sec id="sec003">\n'
'<jats:title>Subjects and methods</jats:title>\n'
'<jats:p>A total of 90 subjects with COVID-19 (men, n = 59 [65.6%]; age, 52.9±16.7 years) were '
'recruited in this cross-sectional study. Quantification of the total and compromised lung '
'parenchyma was performed by two expert radiologists using a volumetric image analysis '
'software and compared against an AI-assisted package consisting of a modified U-Net model for '
'segmenting COVID-19 lesions and an off-the-shelf U-Net model augmented with COVID-19 data for '
'segmenting lung volume. The fraction of compromised lung parenchyma (%CL) was calculated. '
'Based on clinical results, the subjects were divided into two categories: critical (n = 45) '
'and noncritical (n = 45). All admission data were compared between the two groups.</jats:p>\n'
'</jats:sec>\n'
'<jats:sec id="sec004">\n'
'<jats:title>Results</jats:title>\n'
'<jats:p>There was an excellent agreement between the radiologist-obtained and AI-assisted '
'measurements (intraclass correlation coefficient = 0.88, <jats:italic>P</jats:italic> < '
'0.001). Both the AI-assisted and radiologist-obtained %CLs were significantly higher in the '
'critical subjects (P = 0.009 and 0.02, respectively) than in the noncritical subjects. In the '
'multivariate logistic regression analysis to distinguish the critical subjects, an '
'AI-assisted %CL ≥35% (odds ratio [OR] = 17.0), oxygen saturation level of <88% (OR = '
'33.6), immunocompromised condition (OR = 8.1), and other comorbidities (OR = 15.2) '
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'accuracy of 83.9%, a sensitivity of 79.1%, and a specificity of 88.6% in predicting critical '
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'<jats:title>Conclusions</jats:title>\n'
'<jats:p>AI-assisted measurements are similar to quantitative radiologist-obtained '
'measurements in determining lung involvement in COVID-19 subjects.</jats:p>\n'
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'subject': ['Multidisciplinary'],
'container-title-short': 'PLoS ONE',
'published': {'date-parts': [[2023, 12, 8]]}}
