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Updated: Dec 22, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

Artificial intelligence to codify lung CT in Covid-19 patients.

Maria Paola Belfiore¹, Fabrizio Urraro¹, Roberta Grassi¹

¹Department of Precision Medicine, University of Campania Luigi Vanvitelli, 80138, Naples, Italy.

La Radiologia Medica

|May 6, 2020

Summary

Artificial intelligence (AI) software aids in diagnosing COVID-19 pneumonia using computed tomography (CT) scans. This tool quantifies lung lesions, improving diagnostic accuracy and severity assessment for better patient management.

Keywords:

Artificial intelligence Sars-Cov-2 Structured report

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Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

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Published on: May 19, 2023

Area of Science:

Radiology
Medical Imaging
Artificial Intelligence

Background:

The COVID-19 pandemic necessitates rapid and accurate diagnostic tools.
Computed tomography (CT) is crucial for diagnosing COVID-19 pneumonia, even in asymptomatic patients.
High-resolution CT (HRCT) is employed for detailed lung imaging.

Purpose of the Study:

To introduce an artificial intelligence (AI) software tool for enhancing COVID-19 diagnosis via CT.
To demonstrate the utility of AI in objectively assessing the extent and severity of lung lesions in COVID-19 patients.

Main Methods:

Utilized Thoracic VCAR software (GE Healthcare) for CT image analysis.
Employed AI to differentiate and quantify lung pathologies like ground glass and consolidation.
Integrated quantitative AI assessments with qualitative radiological findings.

Main Results:

The AI software provides objective, quantitative measurements of lung involvement.
Thoracic VCAR generates concise reports, facilitating communication between radiologists and physicians.
AI accurately calculates the volume of affected lung parenchyma, distinguishing between ground glass and consolidation.

Conclusions:

AI software significantly enhances the diagnostic capabilities of CT for COVID-19 pneumonia.
Objective quantification of lung involvement by AI improves disease severity assessment.
AI tools are essential for providing precise evaluations of ventilated versus affected lung parenchyma.