Evidence map›Paper›PMID 40594615›Full record

ArticleScientific reports2025

Comparing the accuracy of computer-aided detection (CAD) software and radiologists from multiple countries for tuberculosis detection in chest X-Rays.

Zhi Zhen Qin, Martie Van der Walt, Sizulu Moyo, Farzana Ismail, Phaleng Maribe, Claudia M Denkinger, Sarah Zaidi, Rachael Barrett, Lindiwe Mvusi, Yolisa Tsibolane and 6 more

Abstract readComparative Study
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Trial
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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

16 authors.

Zhi Zhen QinStop TB Partnership, Geneva, Switzerland. zhizhen.qin@gmail.com.
Martie Van der WaltSouth Africa Medical Research Council, Pretoria, South Africa.
Sizulu MoyoHuman Sciences Research Council, Human and Social Capabilities Division, Cape Town, South Africa.
Farzana IsmailNational Institute for Communicable Diseases, Pretoria, South Africa.
Phaleng MaribeHuman Sciences Research Council, Human and Social Capabilities Division, Cape Town, South Africa.
Claudia M DenkingerDepartment of Infectious Disease and Tropical Medicine, Heidelberg University Hospital, German Center for Infection Research, Heidelberg, Germany.
Sarah ZaidiStop TB Partnership, Geneva, Switzerland.
Rachael BarrettStop TB Partnership, Geneva, Switzerland.
Lindiwe MvusiSouth African National Department of Health, Pretoria, South Africa.
Yolisa TsibolaneSouth African National Department of Health, Pretoria, South Africa.
Nkateko MkhondoWorld Health Organization, Pretoria, South Africa.
Khangelani ZumaHuman Sciences Research Council, Human and Social Capabilities Division, Cape Town, South Africa.
Samuel MandaSouth Africa Medical Research Council, Pretoria, South Africa.
Lisa KoeppelDepartment of Infectious Disease and Tropical Medicine, Heidelberg University Hospital, German Center for Infection Research, Heidelberg, Germany.
Thuli MthiyaneSouth Africa Medical Research Council, Pretoria, South Africa.
Jacob CreswellStop TB Partnership, Geneva, Switzerland.

Funding

World Health Organization 001
6 · The paper itself

Abstract

Nearly a third of TB cases go undetected annually. WHO recommends computer-aided detection (CAD) to enhance TB screening, with studies showing comparable performance to local radiologists. Using 774 chest X-rays from the South African National TB Prevalence Survey, we compared 12 CAD software with 11 radiologists from Nigeria, India, the UK, and the US, against a composite microbiological reference standard. Sensitivity, specificity and Cohen's kappa were calculated. Receiver-operating characteristic curves were developed for CAD and Euclidean distance assessed radiologists' alignment with the best-performing software. Binomial regression tested the impact of radiologists' characteristics on accuracy. Radiologist performance varied. On the restricted read, British radiologists had the highest sensitivity (78.7% [73.2-83.5%]) and Indian radiologists the lowest (67.1% [61.0-72.8%]). Specificity ranged from 75.8% (71.8-79.4%, Nigeria) to 84.3% (80.9-87.3%, the US). Radiologist performance was significantly impacted by HIV, prior TB, and age. The top CAD outperformed all except Indian radiologists when matching specificity. CAD with Conformité Européenne generally matched or surpassed radiologists. British radiologists' sensitivity was closest to the top CAD, while American radiologists were closest in specificity and overall. Experience, TB reads, and country had no significant impact on accuracy. CAD performed well against radiologists globally, highlighting potential to enhance access to care.

Indexed as

Diagnosis, Computer-AssistedRadiography, ThoracicRadiologistsSoftwareTuberculosisTuberculosis, PulmonaryAdultAgedFemaleHumansIndiaMaleMiddle AgedNigeriaROC CurveSensitivity and Specificity

Identifiers

PMID40594615
PMCPMC12215708

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Registered trials

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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.