基于数字孪生的肺结节诊断

Diagnosis of pulmonary nodules using digital twins

为解决肺结节诊断难度大的问题,提出基于数字孪生的肺结节诊断技术思路。采用智能算法、模型融合、虚实交互等关键技术建立了肺结节诊断的数字孪生框架。通过重建算法构建了肺三维虚拟模型,建立了肺结节诊断系统的虚拟实体;利用深度学习技术对数据进行迭代分析生成肺结节检测模型,与三维虚拟模型结合提升交互性,实现了对肺结节的智能检测;通过随访更新患者数据构建肺结节的动态预测模型,完成了肺结节分析与诊断,满足了可视化监控、健康管理等需求。以实际案例进行了肺结节的检测与诊断,验证了所建系统的可行性。

To solve the difficult diagnosis problem of pulmonary nodules,the technical idea of pulmonary nodule diagnosis based on digital twin was proposed.Using key technologies such as intelligent algorithm,model fusion,and virtual-real interaction,a digital twin framework for pulmonary nodule diagnosis was established.The three-dimensional virtual model of the lung was constructed through the reconstruction algorithm,and the virtual entity of the pulmonary nodule diagnosis system was established.Using deep learning technology,the data was iteratively analyzed to generate a lung nodule detection model,which was combined with the three-dimensional virtual model to improve the interactivity and realize the intelligent detection of lung nodules.Finally,the dynamic prediction model of pulmonary nodules was constructed through follow-up update of patient data,the analysis and diagnosis of pulmonary nodules were completed,and the requirements of visual monitoring and health management were realized.The detection and diagnosis of pulmonary nodules were carried out with practical cases,and the feasibility of the proposed system was verified.