Open AccessDetection and characterization of COVID-19 findings in chest CT
Author(s) -
Andi Gashi,
Rahel A. KubikHuch,
Vasiliki Chatzaraki,
Anna Potempa,
Franziska Rauch,
Saša Grbić,
Benedikt Wiggli,
Andrée Friedl,
Tilo Niemann
Publication year - 2021
Publication title -
medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.59
H-Index - 148
eISSN - 1536-5964
pISSN - 0025-7974
DOI - 10.1097/md.0000000000027478
Subject(s) - medicine , covid-19 , asymptomatic , context (archaeology) , radiology , retrospective cohort study , nuclear medicine , pathology , outbreak , infectious disease (medical specialty) , paleontology , biology , disease
Abstract The COVID-19 pandemic has challenged institutions’ diagnostic processes worldwide. The aim of this study was to assess the feasibility of an artificial intelligence (AI)-based software tool that automatically evaluates chest computed tomography for findings of suspected COVID-19. Two groups were retrospectively evaluated for COVID-19-associated ground glass opacities of the lungs (group A: real-time polymerase chain reaction positive COVID patients, n = 108; group B: asymptomatic pre-operative group, n = 88). The performance of an AI-based software assessment tool for detection of COVID-associated abnormalities was compared with human evaluation based on COVID-19 reporting and data system (CO-RADS) scores performed by 3 readers. All evaluated variables of the AI-based assessment showed significant differences between the 2 groups ( P < .01). The inter-reader reliability of CO-RADS scoring was 0.87. The CO-RADS scores were substantially higher in group A (mean 4.28) than group B (mean 1.50). The difference between CO-RADS scoring and AI assessment was statistically significant for all variables but showed good correlation with the clinical context of the CO-RADS score. AI allowed to predict COVID positive cases with an accuracy of 0.94. The evaluated AI-based algorithm detects COVID-19-associated findings with high sensitivity and may support radiologic workflows during the pandemic.