Peninsula Medical School, University of Plymouth, Plymouth Science Park, Plymouth, PL6 8BT, UK.
University Hospitals Plymouth NHS Trust, Plymouth, Devon, PL6 8DH, UK.
J Digit Imaging. 2022 Dec;35(6):1673-1680. doi: 10.1007/s10278-022-00663-2. Epub 2022 Jun 29.
Flagging the presence of cardiac devices such as pacemakers before an MRI scan is essential to allow appropriate safety checks. We assess the accuracy with which a machine learning model can classify the presence or absence of a pacemaker on pre-existing chest radiographs. A total of 7973 chest radiographs were collected, 3996 with pacemakers visible and 3977 without. Images were identified from information available on the radiology information system (RIS) and correlated with report text. Manual review of images by two board certified radiologists was performed to ensure correct labeling. The data set was divided into training, validation, and a hold-back test set. The data were used to retrain a pre-trained image classification neural network. Final model performance was assessed on the test set. Accuracy of 99.67% on the test set was achieved. Re-testing the final model on the full training and validation data revealed a few additional misclassified examples which are further analyzed. Neural network image classification could be used to screen for the presence of cardiac devices, in addition to current safety processes, providing notification of device presence in advance of safety questionnaires. Computational power to run the model is low. Further work on misclassified examples could improve accuracy on edge cases. The focus of many healthcare applications of computer vision techniques has been for diagnosis and guiding management. This work illustrates an application of computer vision image classification to enhance current processes and improve patient safety.
在进行 MRI 扫描前，标记心脏设备（如起搏器）的存在至关重要，这有助于进行适当的安全检查。我们评估了机器学习模型在已有胸部 X 光片上分类起搏器存在或不存在的准确性。共收集了 7973 张胸部 X 光片，其中 3996 张可见起搏器，3977 张不可见。这些图像是从放射信息系统（RIS）上的信息中识别出来的，并与报告文本相关联。由两名经过董事会认证的放射科医生对图像进行手动审查，以确保正确标记。数据集被分为训练集、验证集和保留测试集。使用这些数据重新训练预先训练的图像分类神经网络。在测试集上评估最终模型的性能。在测试集上实现了 99.67%的准确率。在完整的训练和验证数据上重新测试最终模型时，发现了一些额外的错误分类示例，我们进一步对其进行了分析。神经网络图像分类可用于筛选心脏设备的存在，除了当前的安全流程外，还可以在安全问卷之前提前通知设备的存在。运行模型所需的计算能力较低。进一步研究错误分类的示例可以提高边缘情况下的准确性。计算机视觉技术在许多医疗保健应用中的重点一直是诊断和指导管理。这项工作说明了计算机视觉图像分类在增强现有流程和提高患者安全性方面的应用。
