用深度学习在无造影CT中识别肺栓塞，准确率达85%
Application of deep learning techniques in non-contrast computed tomography pulmonary angiogram for pulmonary embolism diagnosis
- 用3D卷积神经网络分析无造影CT图像
- 分类准确率85%，AUC达0.84
- 适合急症筛查，避免造影剂风险
肺栓塞是危及生命的疾病，早期诊断可显著降低死亡率。近年来，已有大量研究利用增强扫描的CT肺动脉造影结合深度学习进行肺栓塞诊断，但造影剂可能引发肾功能损伤，且需等待显影，急性患者易错过黄金治疗期。本研究旨在使用深度学习技术，通过3D卷积神经网络模型，自动识别非增强CT中的肺栓塞。该模型在非增强CT图像上的肺栓塞分类准确率达到85%，AUC为0.84，验证了其在肺栓塞诊断中的可行性。
原文摘要 · Abstract (English)
Pulmonary embolism is a life-threatening disease, early detection and treatment can significantly reduce mortality. In recent years, many studies have been using deep learning in the diagnosis of pulmonary embolism with contrast medium computed tomography pulmonary angiography, but the contrast medium is likely to cause acute kidney injury in patients with pulmonary embolism and chronic kidney disease, and the contrast medium takes time to work, patients with acute pulmonary embolism may miss the golden treatment time. This study aims to use deep learning techniques to automatically classify pulmonary embolism in CT images without contrast medium by using a 3D convolutional neural network model. The deep learning model used in this study had a significant impact on the pulmonary embolism classification of computed tomography images without contrast with 85\% accuracy and 0.84 AUC, which confirms the feasibility of the model in the diagnosis of pulmonary embolism.
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