Elsevier

Academic Radiology

Volume 28, Issue 8, August 2021, Pages 1048-1057
Academic Radiology

Original Investigation
Quantification of COVID-19 Opacities on Chest CT – Evaluation of a Fully Automatic AI-approach to Noninvasively Differentiate Critical Versus Noncritical Patients

https://doi.org/10.1016/j.acra.2021.03.001Get rights and content

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 (R = 0.74), length of ICU stay (R = 0.81), lethal outcome (R = 0.56) and length of hospital stay (R = 0.33, p < 0.05). The opacities extent was correlated with laboratory parameters: neutrophil count (NEU) (R = 0.60), lactate dehydrogenase (LDH) (R = 0.60), troponin (TNTHS) (R = 0.55) and c-reactive protein (CRP) (R = 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.

Key Words

COVID-19
SARS-CoV-2 infection
Pneumonia
Viral
Artificial Intelligence
Chest-CT

Abbreviations

AI
Artificial intelligence
ARDS
Acute respiratory distress syndrome
BIL
Bilirubin
COVID-19
Coronavirus disease 2019
CRP
C-reactive protein
CT
Computed tomography
DDI
D-dimers
DI2IN
Deep Image to Image Network
DICOM
Digital Imaging and Communications in Medicine
GGO
Ground-glass opacities
GTP
Alanine aminotransferase
HR-CT
High-resolution computer tomography
HST
Urea
HU
Hounsfield units
ICU
Intensive care unit
IL-6
Interleukin-6
KREA
Creatinine
LAC
Lactate
LDH
Lactate dehydrogenase
LEU
White blood cell count
LYM
Lymphocyte count
NEU
Neutrophil count
PACS
Picture archiving and communication system
PCT
Procalcitonin
PHO
Percentage of high opacity
RIS
Radiology information system
RT-PCR
Real-time reverse transcription polymerase-chain-reaction
SARS-CoV-2
Severe acute respiratory syndrome coronavirus type
THR
Thrombocyte count
TNTHS
Troponin
TPZ
Quick value
VRT
Volume rendering technique
WHO
World Health Organization

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