Deep Learning Detection of Aneurysm Clips for Magnetic Resonance Imaging Safety

IF 2.9 2区工程技术 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Journal of Digital Imaging Pub Date : 2024-01-12 DOI:10.1007/s10278-023-00932-8

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引用次数: 0

Abstract

Flagging the presence of metal devices before a head MRI scan is essential to allow appropriate safety checks. There is an unmet need for an automated system which can flag aneurysm clips prior to MRI appointments. We assess the accuracy with which a machine learning model can classify the presence or absence of an aneurysm clip on CT images. A total of 280 CT head scans were collected, 140 with aneurysm clips visible and 140 without. The data were used to retrain a pre-trained image classification neural network to classify CT localizer images. Models were developed using fivefold cross-validation and then tested on a holdout test set. A mean sensitivity of 100% and a mean accuracy of 82% were achieved. Predictions were explained using SHapley Additive exPlanations (SHAP), which highlighted that appropriate regions of interest were informing the models. Models were also trained from scratch to classify three-dimensional CT head scans. These did not exceed the sensitivity of the localizer models. This work illustrates an application of computer vision image classification to enhance current processes and improve patient safety.

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深度学习检测动脉瘤夹，确保磁共振成像安全

摘要在头部核磁共振成像扫描前标记金属装置的存在对于进行适当的安全检查至关重要。目前尚需一种能在磁共振成像预约前标记动脉瘤夹的自动系统。我们评估了机器学习模型对 CT 图像上是否存在动脉瘤夹进行分类的准确性。我们共收集了 280 张头部 CT 扫描图像，其中 140 张可见动脉瘤夹，140 张不可见。这些数据用于重新训练预先训练好的图像分类神经网络，以对 CT 定位器图像进行分类。使用五重交叉验证开发了模型，然后在保留测试集上进行了测试。平均灵敏度为 100%，平均准确率为 82%。预测结果使用 SHapley Additive exPlanations（SHAP）进行了解释，它强调了适当的感兴趣区为模型提供了信息。我们还从头开始训练模型，以便对三维 CT 头部扫描进行分类。这些都没有超过定位器模型的灵敏度。这项工作展示了计算机视觉图像分类在增强当前流程和提高患者安全方面的应用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Digital Imaging 医学-核医学

CiteScore

7.50

自引率

6.80%

发文量

192

审稿时长

6-12 weeks

期刊介绍： The Journal of Digital Imaging (JDI) is the official peer-reviewed journal of the Society for Imaging Informatics in Medicine (SIIM). JDI’s goal is to enhance the exchange of knowledge encompassed by the general topic of Imaging Informatics in Medicine such as research and practice in clinical, engineering, and information technologies and techniques in all medical imaging environments. JDI topics are of interest to researchers, developers, educators, physicians, and imaging informatics professionals. Suggested Topics PACS and component systems; imaging informatics for the enterprise; image-enabled electronic medical records; RIS and HIS; digital image acquisition; image processing; image data compression; 3D, visualization, and multimedia; speech recognition; computer-aided diagnosis; facilities design; imaging vocabularies and ontologies; Transforming the Radiological Interpretation Process (TRIP™); DICOM and other standards; workflow and process modeling and simulation; quality assurance; archive integrity and security; teleradiology; digital mammography; and radiological informatics education.