人工智能冠状动脉钙化积分自动测量方法的临床有效性评估研究进展

Research Progress on Clinical Effectiveness Evaluation of Automatic Measurement Method for Artificial Intelligence Coronary Artery Calcium Score

摘要: 本课题的研究首先阐述了AI技术通过深度学习框架突破传统测量局限的核心突破，包括基于三维卷积神经网络的多任务学习模型实现钙化斑块精准分割与积分同步计算，以及能谱CT虚拟平扫技术消除额外辐射暴露的创新优势，显著提升大规模筛查效率与临床可及性。通过构建多维度评估体系，验证了AI模型在钙化积分量化、微小病灶检测及心血管事件预测中的高可靠性，尤其强调其在风险分层一致性、人工替代效能及动态监测能力方面的临床潜力。研究进一步从技术适配性、生理复杂性及交互作用三方面系统剖析挑战：技术层面聚焦设备协议差异、重建算法选择及模型泛化能力对测量稳定性的影响；生理维度深入钙化病理特征、患者个体差异及代谢交互作用对算法决策的潜在干扰；交互作用机制则揭示扫描参数、生理节律及多模态数据融合对结果优化的协同效应。最终提出动态阈值补偿、多模态特征融合及联邦学习优化等策略，构建了从技术研发到临床转化的全链条改进框架，为推动AI-CACS技术向精准心血管管理工具的演进提供方法学指导。

Abstract: This research first elaborates on the core breakthroughs of AI technology in breaking through traditional measurement limitations through deep learning frameworks, including the implementation of accurate segmentation and synchronous calculation of calcium scores of calcified plaques by a multi-task learning model based on a three-dimensional convolutional neural network, as well as the innovative advantage of spectral CT virtual non-contrast technology in eliminating additional radiation exposure, which significantly improves the efficiency of large-scale screening and clinical accessibility. By constructing a multi-dimensional evaluation system, the high reliability of the AI model in calcium score quantification, detection of small lesions, and prediction of cardiovascular events is verified, with particular emphasis on its clinical potential in terms of risk stratification consistency, manual replacement efficacy, and dynamic monitoring capability. The study further systematically analyzes the challenges from three aspects: technical adaptability, physiological complexity, and interaction mechanisms. At the technical level, it focuses on the impact of device protocol differences, reconstruction algorithm selection, and model generalization ability on measurement stability. In the physiological dimension, it delves into the potential interference of calcification pathological characteristics, individual patient differences, and metabolic interactions on algorithmic decision-making. The interaction mechanism reveals the synergistic effect of scanning parameters, physiological rhythms, and multimodal data fusion on result optimization. Finally, strategies such as dynamic threshold compensation, multimodal feature fusion, and federated learning optimization are proposed, and a full-chain improvement framework from technical research and development to clinical translation is constructed, providing methodological guidance for promoting the evolution of AI-CACS technology into a precise cardiovascular management tool.