Wei Xiaolin, Liang Dabin, Zhang Zhitong, Thorpe Kevin E, Zhou Lingyun, Zhao Jinming, Qin Huifang, Liang Xiaoyan, Cui Zhezhe, Huang Yan, Huang Liwen, Lin Mei
Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.
Guangxi Zhuang Autonomous Region Center for Disease Control and Prevention, Guangxi Key Laboratory of Major Infectious Disease Prevention and Control and Biosafety Emergency Response, Nanning, Guangxi Zhuang Autonomous Region, China.
PLoS One. 2025 Apr 11;20(4):e0316073. doi: 10.1371/journal.pone.0316073. eCollection 2025.
Tuberculosis (TB) remains a significant public health challenge, particularly in rural areas of high-burden countries like China. Active case finding (ACF) and timely treatment have been proven effective in reducing TB prevalence, but the impact on the TB epidemic when employing new technologies in ACF is still unknown. This study aims to evaluate the effectiveness of a comprehensive ACF package utilizing mobile vans equipped with artificial intelligence (AI)-aided radiology and GeneXpert testing in reducing TB prevalence among high-risk populations in rural Guangxi, China.
A pragmatic cluster randomized controlled trial will be conducted in two counties of Guangxi, China. The trial will randomize 23 townships to intervention or control groups at approximately 1:1 ratio. The intervention group will receive an ACF campaign in Year 1 among high-risk populations, incorporating visited by mobile vans equipped with AI-based digital X-ray screening, symptom assessment, and sputum collection for GeneXpert testing. Control group participants receive usual care. TB patients identified in Year 1 will complete their treatment in Year 2. The primary outcome is the prevalence rate of bacteriologically confirmed TB among high-risk populations in Year 3. Process evaluation will explore acceptability, feasibility and adaptation of the intervention. We will conduct incremental costing study to inform future scale-up of the intervention in other settings.
This study will provide valuable insights into the effectiveness and feasibility of utilizing AI-equipped mobile vans and GeneXpert for TB ACF to reduce TB prevalence in rural settings. If successful, this model will contribute to possible solutions to achieve the WHO End TB Strategy by 2035.
ClinicalTrials.gov NCT06702774.
结核病仍然是一项重大的公共卫生挑战，在中国等高负担国家的农村地区尤为如此。主动病例发现（ACF）和及时治疗已被证明在降低结核病患病率方面有效，但在ACF中采用新技术对结核病流行的影响仍不清楚。本研究旨在评估利用配备人工智能（AI）辅助放射学和GeneXpert检测的移动车进行全面ACF方案在中国广西农村高危人群中降低结核病患病率的有效性。
将在中国广西的两个县进行一项实用的整群随机对照试验。该试验将以约1:1的比例将23个乡镇随机分为干预组或对照组。干预组将在第1年对高危人群开展ACF活动，包括由配备基于AI的数字X线筛查、症状评估和用于GeneXpert检测的痰液采集的移动车进行访视。对照组参与者接受常规护理。在第1年发现的结核病患者将在第2年完成治疗。主要结局是第3年高危人群中细菌学确诊结核病的患病率。过程评估将探讨干预措施的可接受性、可行性和适应性。我们将进行增量成本研究，为未来在其他环境中扩大干预规模提供依据。
本研究将为利用配备AI的移动车和GeneXpert进行结核病ACF以降低农村地区结核病患病率的有效性和可行性提供有价值的见解。如果成功，该模式将有助于为在2035年前实现世界卫生组织终止结核病战略提供可能的解决方案。
ClinicalTrials.gov NCT06702774。
