Evidence map›Paper›PMID 40215230›Full record

ArticlePloS one2025

Active case finding using mobile vans with artificial intelligence aided radiology tests and sputum collection for rapid diagnostic tests to reduce tuberculosis prevalence among high-risk population in rural China: Protocol for a pragmatic trial.

Xiaolin Wei, Dabin Liang, Zhitong Zhang, Kevin E Thorpe, Lingyun Zhou, Jinming Zhao, Huifang Qin, Xiaoyan Liang, Zhezhe Cui, Yan Huang and 2 more

Registry-linked trialAbstract readClinical Trial Protocol
PubMed Full text at PMC Publisher
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06702774 (Active Case Finding Using Mobile Vans Equipped With Artificial Intelligence Aided Radiology Tests and Sputum Collection for Rapid Diagnostic Tests to Reduce Tuberculosis Prevalence Among High-risk Populations in Rural China), which is not on this map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT06702774 narecruitingnot on this map

Active Case Finding Using Mobile Vans Equipped With Artificial Intelligence Aided Radiology Tests and Sputum Collection for Rapid Diagnostic Tests to Reduce Tuberculosis Prevalence Among High-risk Populations in Rural China: a Pragmatic Cluster Randomized Controlled Trial

TypeinterventionalSponsorUniversity of TorontoRan2021 to 2025Enrolled72,000ConditionsTuberculosis (TB)ArmsActive case finding
3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Xiaolin WeiDalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.ORCID 0000-0002-3076-2650
Dabin LiangGuangxi 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.
Zhitong ZhangDalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.
Kevin E ThorpeDalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.
Lingyun ZhouGuangxi 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.
Jinming ZhaoGuangxi 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.
Huifang QinGuangxi 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.
Xiaoyan LiangGuangxi 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.
Zhezhe CuiGuangxi 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.
Yan HuangGuangxi 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.
Liwen HuangGuangxi 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.
Mei LinGuangxi 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.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTuberculosis (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.

methodsA 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. DISCUSSION: 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.

trial registrationClinicalTrials.gov NCT06702774.

Indexed as

Artificial IntelligenceMobile Health UnitsSputumTuberculosisAdultChinaDiagnostic Tests, RoutineFemaleHumansMaleMulticenter Studies as TopicPragmatic Clinical Trials as TopicPrevalenceRapid Diagnostic TestsRural Population

Identifiers

PMID40215230
PMCPMC11990735

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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.