%0 Journal Article
%T 人工智能技术在肺结节影像学诊断中的应用：进展、挑战与展望
Application of Artificial Intelligence Technology in Imaging Diagnosis of Pulmonary Nodules: Progress, Challenges and Prospects
%A 高杭宇
%A 杨阳
%A 李建英
%J Journal of Clinical Personalized Medicine
%P 1896-1902
%@ 2334-3443
%D 2024
%I Hans Publishing
%R 10.12677/jcpm.2024.34266
%X 肺结节是肺癌的重要征兆，早期诊断对提高患者生存率至关重要。传统的肺结节诊断方法存在一定的局限性，而人工智能技术的快速发展为解决这一问题提供了新的思路。本文综述了人工智能技术在肺结节影像学诊断中的应用进展，重点介绍了基于深度学习的肺结节检测、分类和恶性程度预测方法。同时，本文分析了当前人工智能在肺结节诊断中面临的挑战，包括数据标注不足、模型泛化能力有限和可解释性不足等问题。最后，本文展望了人工智能在肺结节影像学诊断中的未来发展方向，如多模态信息融合、数据共享与联邦学习以及知识引导的深度学习等，并强调了进一步加强医工交叉合作、构建基础数据库、突破关键技术瓶颈的重要性。
Lung nodules are an important sign of lung cancer, and early diagnosis is crucial to improve the survival rate of patients. Traditional methods for diagnosing pulmonary nodules have some limitations, but the rapid development of artificial intelligence technology provides a new way to solve this problem. In this paper, the application of artificial intelligence technology in the imaging diagnosis of lung nodules is reviewed, with emphasis on the detection, classification and malignant degree prediction methods of lung nodules based on deep learning. At the same time, this paper analyzes the current challenges faced by artificial intelligence in lung nodule diagnosis, including insufficient data annotation, limited model generalization ability and insufficient interpretability. Finally, this paper looks forward to the future development direction of artificial intelligence in the imaging diagnosis of pulmonary nodules, such as multi-modal information fusion, data sharing and federated learning, and knowledge-guided deep learning, and emphasizes the importance of further strengthening cross-medical cooperation, building basic databases, and breaking through key technical bottlenecks.
%K 人工智能，
%K 深度学习，
%K 肺结节，
%K 影像学诊断
Artificial Intelligence
%K Deep Learning
%K Pulmonary Nodules
%K Imaging Diagnosis
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=102984