A novel, automated, quantification of abnormal lung parenchyma in patients with COVID-19 infection: Initial description of feasibility and association with clinical outcome

Abstract

Objective Ground-glass opacities are the most frequent radiologic features of COVID-19 patients. We aimed to determine the feasibility of automated lung volume measurements, including ground-glass volumes, on the CT of suspected COVID-19 patients. Our goal was to create an automated and quantitative measure of ground-glass opacities from lung CT images that could be used clinically for diagnosis, triage and research. Design Single centre, retrospective, observational study. Measurements Demographic data, respiratory support treatment (synthetised in the maximal respiratory severity score) and CT-images were collected. Volume of abnormal lung parenchyma was measured with conventional semi-automatic software and with a novel automated algorithm based on voxels X-Ray attenuation. We looked for the relationship between the automated and semi-automated evaluations. The association between the ground-glass opacities volume and the maximal respiratory severity score was assessed. Main results Thirty-seven patients were included in the main outcome analysis. The mean duration of automated and semi-automated volume measurement process were 15 (2) and 93 (41) min, respectively ( p = 8.05*10 −8 ). The intraclass correlation coefficient between the semi-automated and automated measurement of ground-glass opacities and restricted normally aerated lung were both superior to 0.99. The association between the automated measured lung volume and the maximal clinical severity score was statistically significant for the restricted normally aerated ( p = 0.0097, effect-size: −385 mL) volumes and for the ratio of ground-glass opacities/restricted normally aerated volumes (p = 0.027, effect-size: 3.3). Conclusion The feasibility and preliminary validity of automated impaired lung volume measurements in a high-density COVID-19 cluster was confirmed by our results.

Abdominal Imaging Associates Body Composition with COVID-19 Severity

“…Thus, our observation of a reduction in lung volume following COVID-19 is consistent with published studies that have shown persistent lung abnormalities following COVID-19 infection, including reduced forced expiratory volume, vital capacity, and forced vital capacity measured by spirometry [48,49]. Moreover, CT scans showing both reduced lung volume [50] and impaired functional lung volume related to disease severity [51] have been reported. Other studies using pulmonary function tests have reported that lung volumes in patients with mild/moderate COVID-19 are normal [52].…”

The Applications of Artificial Intelligence in Chest Imaging of COVID-19 Patients: A Literature Review

“…A further retrospective study assessed the feasibility of an automated quantification process of GGOs (−700-−501 HU), one of the most significant lesions of COVID-19 pneumonia, and normally restricted parenchyma (−900-−701 HU). They affirmed that GGOs could be an objective biomarker for lung injury due to a statistically significant correlation between the measured volumes and a respiratory assessment severity score on 6 categories, from absence of hospitalization and inability to resume normal activity (1) to death (7) [114]. On the other hand, a software-based quantitative CT assessment of the normal lung parenchyma percentage (SQNLP) has proven to accurately predict ICU admission if <81.1% (sensitivity 86.5% and specificity 86.7%).…”

Prognostic findings for ICU admission in patients with COVID-19 pneumonia: baseline and follow-up chest CT and the added value of artificial intelligence

“…A French retrospective study focused on the automated quantification of GGOs, included in the range from − 700 to − 501 HU), and normally restricted parenchyma, included in the range from − 900 to − 701 HU. The latter was significantly lower in patients admitted to ICU, and GGOs were considered as a biomarker of pulmonary injury, considering a significant correlation between measured lung volumes and a respiratory assessment severity score (7 degrees: ranging from 1 (absence of hospitalization and inability to resume normal activity) to 7 (death)) [ 79 ].…”