Quantification of COVID-19 Opacities on Chest CT - Evaluation of a Fully Automatic AI-approach to Noninvasively Differentiate Critical Versus Noncritical Patients.
Acad Radiol
; 28(8): 1048-1057, 2021 08.
Article
in English
| MEDLINE | ID: covidwho-1141556
ABSTRACT
OBJECTIVES:
To evaluate the potential of a fully automatic artificial intelligence (AI)-driven computed tomography (CT) software prototype to quantify severity of COVID-19 infection on chest CT in relationship with clinical and laboratory data.METHODS:
We retrospectively analyzed 50 patients with laboratory confirmed COVID-19 infection who had received chest CT between March and July 2020. Pulmonary opacifications were automatically evaluated by an AI-driven software and correlated with clinical and laboratory parameters using Spearman-Rho and linear regression analysis. We divided the patients into sub cohorts with or without necessity of intensive care unit (ICU) treatment. Sub cohort differences were evaluated employing Wilcoxon-Mann-Whitney-Test.RESULTS:
We included 50 CT examinations (mean age, 57.24 years), of whom 24 (48%) had an ICU stay. Extent of COVID-19 like opacities on chest CT showed correlations (all p < 0.001 if not otherwise stated) with occurrence of ICU stay (Râ¯=â¯0.74), length of ICU stay (Râ¯=â¯0.81), lethal outcome (Râ¯=â¯0.56) and length of hospital stay (Râ¯=â¯0.33, p < 0.05). The opacities extent was correlated with laboratory parameters neutrophil count (NEU) (Râ¯=â¯0.60), lactate dehydrogenase (LDH) (Râ¯=â¯0.60), troponin (TNTHS) (Râ¯=â¯0.55) and c-reactive protein (CRP) (Râ¯=â¯0.51). Differences (p < 0.001) between ICU group and non-ICU group concerned longer length of hospital stay (24.04 vs. 10.92 days), higher opacity score (12.50 vs. 4.96) and severity of laboratory data changes such as c-reactive protein (11.64 vs. 5.07 mg/dl, p < 0.01).CONCLUSIONS:
Automatically AI-driven quantification of opacities on chest CT correlates with laboratory and clinical data in patients with confirmed COVID-19 infection and may serve as non-invasive predictive marker for clinical course of COVID-19.Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Artificial Intelligence
/
Tomography, X-Ray Computed
/
COVID-19
Type of study:
Cohort study
/
Experimental Studies
/
Observational study
/
Prognostic study
Limits:
Humans
/
Middle aged
Language:
English
Journal:
Acad Radiol
Journal subject:
Radiology
Year:
2021
Document Type:
Article
Similar
MEDLINE
...
LILACS
LIS