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Accuracy of artificial intelligence CT quantification in predicting COVID-19 subjects’ prognosis

Arian et al., PLOS ONE, doi:10.1371/journal.pone.0294899
Dec 2023  
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Vitamin D for COVID-19
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Retrospective 90 hospitalized COVID-19 patients in Iran showing lower vitamin D levels in critical vs. non-critical patients (20 vs. 26, p = 0.18).
Arian et al., 8 Dec 2023, retrospective, Iran, peer-reviewed, 8 authors, study period November 2020 - January 2021. Contact:
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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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