Pulmonary contusion: automated deep learning-based quantitative visualization

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Nathan Sarkar, Lei Zhang, Peter Campbell, Yuanyuan Liang, Guang Li, Mustafa Khedr, Udit Khetan, David Dreizin

Published: 15 Jun 2023, Last Modified: 02 Mar 2026Emergency RadiologyEveryoneRevisionsCC BY-SA 4.0
Abstract: Rapid automated CT volumetry of pulmonary contusion may predict progression to Acute Respiratory Distress Syndrome (ARDS) and help guide early clinical management in at-risk trauma patients. This study aims to train and validate state-of-the-art deep learning models to quantify pulmonary contusion as a percentage of total lung volume (Lung Contusion Index, or auto-LCI) and assess the relationship between auto-LCI and relevant clinical outcomes.