Rhomberg, Thomas; Trivik-Barrientos, Felipe; Hakim, Arsany; Raabe, Andreas; Murek, Michael (2024). Applied deep learning in neurosurgery: identifying cerebrospinal fluid (CSF) shunt systems in hydrocephalus patients. Acta neurochirurgica, 166(69) Springer Vienna 10.1007/s00701-024-05940-3
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BACKGROUND
Over the recent decades, the number of different manufacturers and models of cerebrospinal fluid shunt valves constantly increased. Proper identification of shunt valves on X-ray images is crucial to neurosurgeons and radiologists to derive further details of a specific shunt valve, such as opening pressure settings and MR scanning conditions. The main aim of this study is to evaluate the feasibility of an AI-assisted shunt valve detection system.
METHODS
The dataset used contains 2070 anonymized images of ten different, commonly used shunt valve types. All images were acquired from skull X-rays or scout CT-images. The images were randomly split into a 80% training and 20% validation set. An implementation in Python with the FastAi library was used to train a convolutional neural network (CNN) using a transfer learning method on a pre-trained model.
RESULTS
Overall, our model achieved an F1-score of 99% to predict the correct shunt valve model. F1-scores for individual shunt valves ranged from 92% for the Sophysa Sophy Mini SM8 to 100% for several other models.
CONCLUSION
This technology has the potential to automatically detect different shunt valve models in a fast and precise way and may facilitate the identification of an unknown shunt valve on X-ray or CT scout images. The deep learning model we developed could be integrated into PACS systems or standalone mobile applications to enhance clinical workflows.
Item Type: |
Journal Article (Original Article) |
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Division/Institute: |
04 Faculty of Medicine > Department of Head Organs and Neurology (DKNS) > Clinic of Neurosurgery 04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic and Interventional Neuroradiology |
UniBE Contributor: |
Rhomberg, Thomas, Hakim, Arsany, Raabe, Andreas, Murek, Michael Konrad |
Subjects: |
600 Technology > 610 Medicine & health |
ISSN: |
0001-6268 |
Publisher: |
Springer Vienna |
Language: |
English |
Submitter: |
Pubmed Import |
Date Deposited: |
07 Feb 2024 10:00 |
Last Modified: |
08 Feb 2024 18:04 |
Publisher DOI: |
10.1007/s00701-024-05940-3 |
PubMed ID: |
38321344 |
Uncontrolled Keywords: |
AI CSF shunt Cerebrospinal fluid shunt Deep learning Hydrocephalus Transfer learning Ventriculoperitoneal shunt X-ray |
BORIS DOI: |
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URI: |
https://boris.unibe.ch/id/eprint/192648 |
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