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

• Published in: PloS one Vol. 18; no. 12; p. e0294899
• Main Authors: Arian, Arvin, Mehrabi Nejad, Mohammad-Mehdi, Zoorpaikar, Mostafa, Hasanzadeh, Navid, Sotoudeh-Paima, Saman, Kolahi, Shahriar, Gity, Masoumeh, Soltanian-Zadeh, Hamid
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 08.12.2023; Public Library of Science (PLoS)
• Subjects: Abnormalities; Accuracy; Analysis; Artificial intelligence; Automation; Care and treatment; Clinical outcomes; Comorbidity; Comparative analysis; Computed tomography; Correlation coefficient; Correlation coefficients; COVID-19; CT imaging; Data collection; Datasets; Deep learning; Diagnosis; Health aspects; Human error; Image analysis; Image processing; Infections; Infectious diseases; Lungs; Medical imaging; Medical imaging equipment; Medical prognosis; Oxygen content; Parenchyma; Prognosis; Regression analysis; Iran
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0294899

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. 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. 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. 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 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) independently remained as significant variables in the models. Our proposed model obtained an accuracy of 83.9%, a sensitivity of 79.1%, and a specificity of 88.6% in predicting critical outcomes. AI-assisted measurements are similar to quantitative radiologist-obtained measurements in determining lung involvement in COVID-19 subjects.