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Updated: Oct 16, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
Published on: December 19, 2020
A new standard scoring for interstitial pneumonia based on quantitative analysis of ultrasonographic data: A study on
Fiorella Anna Lombardi1, Roberto Franchini1, Rocco Morello1
1National Research Council - Institute of Clinical Physiology, Lecce, Italy.
Objective:
To assess the effectiveness of 3 novel lung ultrasound (LUS)-based parameters: Pneumonia Score and Lung Staging for pneumonia staging and COVID Index, indicating the probability of SARS-CoV-2 infection.
Methods:
Adult patients admitted to the emergency department with symptoms potentially related to pneumonia, healthy volunteers and clinical cases from online accessible databases were evaluated. The patients underwent a clinical-epidemiological questionnaire and a LUS acquisition, following a 14-zone protocol. For each zone, a Pneumonia score from 0 to 4 was assigned by the algorithm and by an expert operator (kept blind with respect to the algorithm results) on the basis of the identified imaging signs and the patient Lung Staging was derived as the highest observed score. The output of the operator was considered as the ground truth. The algorithm calculated also the COVID Index by combining the automatically identified LUS markers with the questionnaire answers and compared with the nasopharyngeal swab results.
Results:
Overall, 556 patients were analysed. A high agreement between the algorithm assignments and the expert operator evaluations was observed, both for Pneumonia Score and Lung Staging, with the latter having sensitivity and specificity over 92% both in the discrimination between healthy/sick patients and between sick patients with mild/severe pneumonia. Regarding the COVID Index, an area under the curve of 0.826 was observed for the classification of patients with/without SARS-CoV-2.
Conclusion:
The proposed methodology allowed the identification and staging of patients suffering from pneumonia with high accuracy. Moreover, it provided the probability of being infected by SARS-CoV-2.
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