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Related Concept Videos

Brain Imaging01:14

Brain Imaging

216
Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...
216

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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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Deep learning from head CT scans to predict elevated intracranial pressure.

Ryota Sato1, Yukinori Akiyama1, Takeshi Mikami1

  • 1Department of Neurosurgery, Sapporo Medical University, Sapporo, Japan.

Journal of Neuroimaging : Official Journal of the American Society of Neuroimaging
|October 10, 2024
PubMed
Summary
This summary is machine-generated.

New deep learning and statistical models can accurately predict elevated intracranial pressure (ICP) using CT scans, aiding in preventing secondary brain injury. These tools offer a rapid, minimally invasive approach for clinical diagnosis.

Keywords:
deep learningintracranial hypertensionintracranial pressureprediction model

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Area of Science:

  • Neurosurgery
  • Radiology
  • Artificial Intelligence in Medicine

Background:

  • Elevated intracranial pressure (ICP) from head injury or stroke risks secondary brain injury.
  • Current noninvasive ICP monitoring methods lack sufficient advancement.
  • Neurosurgical intervention is often required for elevated ICP.

Purpose of the Study:

  • Develop a minimally invasive ICP prediction model using simple CT images.
  • Prevent secondary brain injury caused by elevated ICP.
  • Enhance diagnostic capabilities for elevated ICP.

Main Methods:

  • Developed a deep learning model (PY) using Python for ICP prediction.
  • Created a statistical model (PO) based on cistern narrowing and brainstem deformities.
  • Compared model accuracy against senior resident (SR) identification.
  • Utilized fivefold cross-validation for accuracy assessment.

Main Results:

  • Validation data accuracy: PY (83.68%), PO (85.71%), SR (66.67%).
  • Test data accuracy: PY (77.27%), PO (84.09%), SR (61.36%).
  • Significant accuracy differences observed between PY and SR methods.

Conclusions:

  • Newly developed models show potential for rapid and accurate detection of elevated ICP.
  • These models can be valuable tools in clinical practice.
  • Single midbrain-level CT images enable highly accurate diagnoses with these models.