From Trauma CT to Pneumonia Risk
Deep learning-based quantification of pneumothorax, haemothorax and pulmonary contusion following blunt thoracic trauma
V.F. van Asperen (TU Delft - Mechanical Engineering)
Theo van Walsum – Mentor (Erasmus MC)
Matthieu M.E. Wijffels – Mentor (Erasmus MC)
Sven A.G. Meylaerts – Graduation committee member (Haaglanden Medical Center)
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Abstract
**Introduction:** Blunt thoracic trauma frequently results in pneumothorax (PTX), haemothorax (HTX), and pulmonary contusion (PC), injuries that may contribute to post-traumatic pulmonary complications such as pneumonia. Although computed tomography (CT) enables detailed assessment of these injuries, their extent is generally not quantified routinely. Automated image analysis may enable objective quantification of thoracic injury burden and provide quantitative imaging features for prediction of post-traumatic pneumonia. This study investigated whether PTX, HTX, and PC can be automatically and reliably quantified from admission CT and whether these quantitative imaging features can be integrated into pneumonia prediction.
**Methods:** PTX and HTX were segmented using a multi-class nnU-Net model with lung parenchyma as an auxiliary anatomical class. Model configurations and post-processing thresholds were optimised during development, and the final pipeline was evaluated on an independent test set. For PC, six clinical observers assessed contusion extent within twelve predefined lung regions. Inter- and intraobserver agreement were evaluated, and the mean assessment of the six observers was used as a consensus reference for development of a 3D convolutional neural network (CNN) for automated PC quantification. Finally, the automatically derived PTX, HTX, and PC measurements were evaluated as radiological features within an existing clinical–radiological model for post-traumatic pneumonia prediction.
**Results:** On independent testing, PTX achieved a sensitivity of 1.00, specificity of 0.80, and mean absolute volume error of 5.2 mL. For HTX, sensitivity was 0.81, specificity 0.82, and mean absolute volume error 35.4 mL. Including lung parenchyma as an auxiliary class significantly reduced HTX volume error and false-positive predictions. PC assessment showed moderate interobserver agreement and substantial variation in intraobserver agreement. Automated PC quantification achieved a patient-level mean absolute error of 5.70 percentage points and a calibration slope of 0.68 on independent testing, with better performance at patient than regional level. Integration of the resulting automated pulmonary injury measurements into the pneumonia prediction framework yielded a test area under the receiver operating characteristic curve (AUC) of 0.81, compared with 0.83 for the previously developed combined clinical–radiological model.
**Conclusion:** Automated quantification of pulmonary injury from trauma CT was feasible, although performance differed between injury types. The resulting PTX, HTX, and PC measurements could be integrated into the existing pneumonia prediction framework while largely preserving discrimination compared with the original combined model. External validation is required to determine whether these measurements and resulting risk estimates can provide clinically meaningful support for patient management.