用深度学习自动分析心脏CT，快速分类冠状动脉钙化程度
Predicting Coronary Artery Calcium Severity based on Non-Contrast Cardiac CT images using Deep Learning
- 用卷积神经网络分析非对比剂心脏CT图像，自动分六类钙化程度
- 准确率达96.5%，与人工评分一致性极高（kappa=0.962）
- 适合临床医生快速筛查心血管风险，提升诊断效率
心血管疾病全球致死率高。冠状动脉钙化（CAC）评分是评估动脉粥样硬化性心血管疾病风险的重要工具。当前评分依赖放射科医生和受训技术人员进行耗时的半自动分析。本研究旨在开发一种深度学习卷积神经网络（CNN）模型，将心脏非对比剂CT图像中的钙化程度分为六类临床类别。共获取68例患者扫描数据，使用心电门控GE Discovery 570心脏SPECT/CT设备采集，对应半自动钙化评分作为参考标签。数据集划分为训练、验证和测试集。模型在六分类任务中表现优异，共32例误判，其中26例为钙化程度高估。总体一致性达Cohen's kappa 0.962，整体准确率为96.5%，具有良好泛化能力。结果表明，模型输出与现有半自动实践高度一致，具备良好的测试数据泛化能力，证明了CNN模型在扩展六类临床分类中分层钙化评分的可行性。
原文摘要 · Abstract (English)
Cardiovascular disease causes high rates of mortality worldwide. Coronary artery calcium (CAC) scoring is a powerful tool to stratify the risk of atherosclerotic cardiovascular disease. Current scoring practices require time-intensive semiautomatic analysis of cardiac computed tomography by radiologists and trained radiographers. The purpose of this study is to develop a deep learning convolutional neural networks (CNN) model to classify the calcium score in cardiac, non-contrast computed tomography images into one of six clinical categories. A total of 68 patient scans were retrospectively obtained together with their respective reported semiautomatic calcium score using an ECG-gated GE Discovery 570 Cardiac SPECT/CT camera. The dataset was divided into training, validation and test sets. Using the semiautomatic CAC score as the reference label, the model demonstrated high performance on a six-class CAC scoring categorisation task. Of the scans analysed, the model misclassified 32 cases, tending towards overestimating the CAC in 26 out of 32 misclassifications. Overall, the model showed high agreement (Cohen's kappa of 0.962), an overall accuracy of 96.5% and high generalisability. The results suggest that the model outputs were accurate and consistent with current semiautomatic practice, with good generalisability to test data. The model demonstrates the viability of a CNN model to stratify the calcium score into an expanded set of six clinical categories.
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