Deep learning from head CT scans to predict elevated intracranial pressure.

Background and purpose: Elevated intracranial pressure (ICP) resulting from severe head injury or stroke poses a risk of secondary brain injury that requires neurosurgical intervention. However, currently available noninvasive monitoring techniques for predicting ICP are not sufficiently advanced. We aimed to develop a minimally invasive ICP prediction model using simple CT images to prevent secondary brain injury caused by elevated ICP.

Methods: We used the following three methods to determine the presence or absence of elevated ICP using midbrain-level CT images: (1) a deep learning model created using the Python (PY) programming language; (2) a model based on cistern narrowing and scaling of brainstem deformities and presence of hydrocephalus, analyzed using the statistical tool Prediction One (PO); and (3) identification of ICP by senior residents (SRs). We compared the accuracy of the validation and test data using fivefold cross-validation and visualized or quantified the areas of interest in the models.

Results: The accuracy of the validation data for the PY, PO, and SR methods was 83.68% (83.42%-85.13%), 85.71% (73.81%-88.10%), and 66.67% (55.96%-72.62%), respectively. Significant differences in accuracy were observed between the PY and SR methods. Test data accuracy was 77.27% (70.45%-77.2%), 84.09% (75.00%-85.23%), and 61.36% (56.82%-68.18%), respectively.

Conclusions: Overall, the outcomes suggest that these newly developed models may be valuable tools for the rapid and accurate detection of elevated ICP in clinical practice. These models can easily be applied to other sites, as a single CT image at the midbrain level can provide a highly accurate diagnosis.