Deep-Learning-based Automated Identification of Ventriculoperitoneal-Shunt Valve Models from Skull X-rays.

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Ventriculoperitoneal 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 amatching 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. 959 cranial X‑rays of patients with aVPS 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%). AConvolutional Neural Network (CNN) was trained using stratified five-fold cross-validation to classify the four VPS model types in the dataset. Afinetuned CNN pretrained on the ImageNet dataset as well as amodel trained from scratch were compared. The averaged performance and uncertainty metrics were evaluated across the cross-validation splits. The fine-tuned model identified VPS valve models with amean accuracy of 0.98 ± 0.01, macro-averaged F1 score of 0.93 ± 0.04, arecall of 0.94 ± 0.03 and aprecision of 0.95 ± 0.08 across the five cross-validation splits. Automatic classification of VPS valve models in skull X‑rays, using fully automatable preprocessing steps and aCNN, 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.