Automated midline shift and intracranial pressure estimation based on brain CT images

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Neurosurgery

Background:

• Accurate estimation of midline shift and intracranial pressure (ICP) is crucial for diagnosing and managing severe brain injuries.
• Current methods for assessing these parameters can be time-consuming or invasive.
• Automated analysis of medical imaging offers a potential solution for faster and more objective assessments.

Purpose of the Study:

• To develop and evaluate an automated system for estimating midline shift and predicting intracranial pressure (ICP) using computed tomography (CT) images.
• To provide physicians with a rapid pre-screening tool to aid in decisions regarding invasive ICP monitoring.

Main Methods:

• An automated system was developed using CT scans, comprising two main components: midline shift estimation and ICP pre-screening.
• Midline shift estimation involved identifying the ideal midline based on skull symmetry and anatomical features, followed by ventricle segmentation and shape matching.
• ICP estimation incorporated features from CT scans (texture, blood amount) and clinical data (age, injury severity score), utilizing machine learning techniques like Support Vector Machines (SVMs).

Main Results:

• The automated midline shift estimation process demonstrated promising results, mimicking physician measurements.
• The ICP prediction model, built using machine learning, showed potential usefulness in evaluating ICP levels.
• The system's components aim to provide rapid, objective data to support clinical decision-making.

Conclusions:

• The developed automated system for midline shift estimation and ICP pre-screening shows potential for clinical application.
• This AI-driven approach can assist physicians in making timely decisions about invasive ICP monitoring.
• Further evaluation is warranted to fully integrate this tool into clinical workflows for neurotrauma management.