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中国防痨杂志 ›› 2026, Vol. 48 ›› Issue (4): 550-555.doi: 10.19982/j.issn.1000-6621.20250473

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数字孪生技术在结核病患者管理中的研究进展

邱玉贤, 黄芳(), 杨小艺, 杨峰, 鲁华, 张天怡, 张杨, 姚蓉, 李园园   

  1. 成都市公共卫生临床医疗中心结核科,成都 610000
  • 收稿日期:2025-12-02 出版日期:2026-04-10 发布日期:2026-04-02
  • 通信作者: 黄芳,Email:736545650@qq.com
  • 基金资助:
    四川省卫生健康委员会科技项目(24QNMP049)

Research progress in digital twin technology for tuberculosis patient management

Qiu Yuxian, Huang Fang(), Yang Xiaoyi, Yang Feng, Lu Hua, Zhang Tianyi, Zhang Yang, Yao Rong, Li Yuanyuan   

  1. Department of Tuberculosis,Public Health Clinical Center of Chengdu,Chengdu 610000,China
  • Received:2025-12-02 Online:2026-04-10 Published:2026-04-02
  • Contact: Huang Fang,Email:736545650@qq.com
  • Supported by:
    Health Commission of Sichuan Province Medical Science and Technology Program(24QNMP049)

摘要:

近年来,数字孪生技术作为一种新兴概念,正逐步应用于结核病患者管理领域。数字孪生技术不仅在虚拟患者构建、药物研发、临床技能培训及医疗资源部署等领域展现出巨大的潜力,更在疾病风险预测与治疗依从性支持等方面显现出前瞻性价值。然而,当前国内外研究尚处于探索阶段,亟需整合现有研究成果以明确其发展脉络。基于此,本综述梳理了数字孪生的基本信息,阐述了数字孪生技术在结核病患者管理中的最新应用研究,分析了其所面临的挑战,并展望未来的发展方向,旨在为全球结核病数字化防治提供创新策略,为提升结核病患者的精准化管理提供借鉴。

关键词: 结核, 疾病管理, 计算机模拟, 预测, 综述文献(主题), 数字孪生

Abstract:

In recent years,digital twin (DT) technology,as an emerging concept,has gradually been applied to the management of tuberculosis patients. DT demonstrates significant potential in virtual patient modeling,drug development,clinical skills training,and public health resource allocation,while also showcasing foresight in disease risk prediction and treatment adherence support. However,current research efforts,both domestically and internationally,remain at an exploratory stage,necessitating the integration of existing findings to clarify its developmental trajectory. Accordingly,this review summarizes the fundamental information on DT,elaborates on the latest research applications of DT technology in tuberculosis patient management,analyzes challenges in technological implementation,and outlines future directions. By dong so,it aims to provide innovative strategies for global tuberculosis digital prevention and control,offering insights for enhancing precision-based management of tuberculosis patients.

Key words: Tuberculosis, Disease management, Computer simulation, Forecasting, Review literature as topic, Digital twin

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