Marius Vach, Daniel Weiss, Vivien Lorena Ivan, Christian Boschenriedter, Luisa Wolf, Thomas Beez, Björn B Hofmann, Christian Rubbert, Julian Caspers
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The aim of this work was to investigate whether AI, in particular deep learning, allows the identification of VPS models in cranial X‑rays.</p><p><strong>Methods: </strong>959 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.</p><p><strong>Results: </strong>The 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.</p><p><strong>Conclusion: </strong>Automatic 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.</p>","PeriodicalId":10391,"journal":{"name":"Clinical Neuroradiology","volume":" ","pages":""},"PeriodicalIF":2.8000,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Deep-Learning-based Automated Identification of Ventriculoperitoneal-Shunt Valve Models from Skull X-rays.\",\"authors\":\"Marius Vach, Daniel Weiss, Vivien Lorena Ivan, Christian Boschenriedter, Luisa Wolf, Thomas Beez, Björn B Hofmann, Christian Rubbert, Julian Caspers\",\"doi\":\"10.1007/s00062-024-01490-4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>Ventriculoperitoneal shunts (VPS) are an essential part of the treatment of hydrocephalus, with numerous valve models available with different ways of indicating pressure levels. 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引用次数: 0
摘要
脑室-腹膜分流术(VPS)是脑积水治疗的重要组成部分,有许多瓣膜模型可用于不同的指示压力水平的方法。通常需要在X射线上识别模型类型,以便使用匹配模板评估压力水平。人工智能(AI),特别是深度学习,非常适合自动化重复任务,例如识别不同的VPS阀门模型。这项工作的目的是研究人工智能,特别是深度学习,是否允许在颅骨X射线中识别VPS模型。方法:收集959例VPS患者的颅骨X线片,对图像质量和VPS瓣膜的完整可视化进行回顾。图像包括四种VPS模型:Codman Hakim (n = 774,81%)、Codman Certas Plus (n = 117,12%)、Sophysa Sophy Mini SM8 (n = 35,4%)和proGAV 2.0 (n = 33,3%)。使用分层五重交叉验证训练卷积神经网络(CNN)对数据集中的四种VPS模型类型进行分类。比较了在ImageNet数据集上预训练的经过微调的CNN和从零开始训练的模型。在交叉验证分割中评估平均性能和不确定度度量。结果:在5个交叉验证区间内,优化模型识别VPS瓣膜模型的平均准确率为0.98 ±0.01,宏观平均F1评分为0.93 ±0.04,召回率为0.94 ±0.03,精密度为0.95 ±0.08。结论:采用全自动预处理步骤和CNN对颅骨X射线中VPS瓣膜模型进行自动分类是可行的。这是一个令人鼓舞的发现,进一步探索自动化VPS阀模型识别和颅骨X射线压力水平读数的可能性。
Deep-Learning-based Automated Identification of Ventriculoperitoneal-Shunt Valve Models from Skull X-rays.
Introduction: 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 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.
Methods: 959 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.
Results: The 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.
Conclusion: Automatic 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.
期刊介绍:
Clinical Neuroradiology provides current information, original contributions, and reviews in the field of neuroradiology. An interdisciplinary approach is accomplished by diagnostic and therapeutic contributions related to associated subjects.
The international coverage and relevance of the journal is underlined by its being the official journal of the German, Swiss, and Austrian Societies of Neuroradiology.