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Utilizing Artificial Intelligence for CSF Segmentation and Analysis in Head CT Imaging: A Systematic Review
Michał Bielówka1,2, Adam Mitręga1, Dominika Kaczyńska1
1Students' Scientific Association of Computer Analysis and Artificial Intelligence, Department of Radiology and Nuclear Medicine, Medical University of Silesia in Katowice, 40-752 Katowice, Poland.
Brain Sciences
|November 27, 2025
Summary
Artificial Intelligence (AI) models show high accuracy for segmenting cerebrospinal fluid (CSF) on CT scans, aiding neurological diagnostics. Further research is needed for clinical integration and standardization.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Neurology
Background:
- Intracranial volume changes impact neurological function.
- Artificial Intelligence (AI) offers potential in medical imaging analysis.
- Cerebrospinal fluid (CSF) analysis is crucial for diagnosing neurological disorders.
Purpose of the Study:
- To systematically review AI-based models for CSF segmentation and analysis on computed tomography (CT) scans.
- To evaluate the performance and applications of AI in CSF volumetric assessment.
Main Methods:
- Systematic review of 559 studies (14 included) across major databases (MEDLINE, Scopus, Web of Science, Embase, Cochrane).
- Data extraction on AI model design, datasets, and CSF segmentation performance metrics.
- Quality assessment using PRISMA 2020, JBI, AMSTAR 2, and CASP checklists.
Main Results:
- AI models, primarily Convolutional Neural Networks and Random Forests, demonstrated high CSF segmentation accuracy (Dice scores 0.75-0.95).
- Strong volumetric correlations (r up to 0.99) were observed between AI and manual measurements.
- Applications include hydrocephalus diagnosis, mass effect evaluation, and stroke outcome prediction.
Conclusions:
- AI-assisted CSF segmentation from CT images is accurate and efficient for neurological diagnostics.
- Challenges remain in dataset variability, algorithm consistency, and clinical validation.
- Future work should focus on standardization, diverse datasets, and clinical workflow integration.
