ArticleClinical neuroradiology2025
Deep-Learning-based Automated Identification of Ventriculoperitoneal-Shunt Valve Models from Skull X-rays.
Article in Clinical neuroradiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Assessment of VP Shunt Valve Settings Using Reconstructions from Non-Contrast Head CT: A Comparative Study with Conventional Radiographs.Clinical neuroradiology · 2026Article
- AI-based detection of Certas Plus shunt valve settings in CT scans.Scientific reports · 2026Article
- Fully automated detection and identification of CSF shunt valves using YOLOv8 and a class-based reference image assignment as a safety mechanism.Scientific reports · 2025Article
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9 authors.
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Abstract
introductionVentriculoperitoneal shunts (VPS) are an essential part of the treatment of hydrocephalus, with numerous valve models available with different ways of indicating pressure levels. The model types often need to be identified on X‑rays to assess pressure levels using a matching template. Artificial intelligence (AI), in particular deep learning, is ideally suited to automate repetitive tasks such as identifying different VPS valve models. The aim of this work was to investigate whether AI, in particular deep learning, allows the identification of VPS models in cranial X‑rays.
methods959 cranial X‑rays of patients with a VPS were included and reviewed for image quality and complete visualization of VPS valves. The images included four VPS model types: Codman Hakim (n = 774, 81%), Codman Certas Plus (n = 117, 12%), Sophysa Sophy Mini SM8 (n = 35, 4%) and proGAV 2.0 (n = 33, 3%). A Convolutional Neural Network (CNN) was trained using stratified five-fold cross-validation to classify the four VPS model types in the dataset. A finetuned CNN pretrained on the ImageNet dataset as well as a model trained from scratch were compared. The averaged performance and uncertainty metrics were evaluated across the cross-validation splits.
resultsThe fine-tuned model identified VPS valve models with a mean accuracy of 0.98 ± 0.01, macro-averaged F1 score of 0.93 ± 0.04, a recall of 0.94 ± 0.03 and a precision of 0.95 ± 0.08 across the five cross-validation splits.
conclusionAutomatic classification of VPS valve models in skull X‑rays, using fully automatable preprocessing steps and a CNN, is feasible. This is an encouraging finding to further explore the possibility of automating VPS valve model identification and pressure level reading in skull X‑rays.
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