Evidence map›Paper›PMID 31479448›Full record

SynthesisPloS one2019

A systematic review of the diagnostic accuracy of artificial intelligence-based computer programs to analyze chest x-rays for pulmonary tuberculosis.

Miriam Harris, Amy Qi, Luke Jeagal, Nazi Torabi, Dick Menzies, Alexei Korobitsyn, Madhukar Pai, Ruvandhi R Nathavitharana, Faiz Ahmad Khan

Registry-linked trialAbstract readSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05989802 (Rapid Research in Diagnostics Development for Tuberculosis Network), which is not on this map. Cited by 84 papers, 9 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
84citing papers in PubMed, 9 pooled it
–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.

NCT05989802 narecruitingnot on this mapstarted 2024, after this paper: background citation

Rapid Research in Diagnostics Development for Tuberculosis Network (R2D2 Kids) and Assessing Diagnostics At Point-of-care for Tuberculosis in Children (ADAPT for Kids)

TypeinterventionalSponsorUniversity of California, San FranciscoRan2024 to 2026Enrolled2,100ConditionsTuberculosis, Diagnostics, Global HealthArmsOral swab molecular testing, Automated Cough Sound Analysis, Automated Lung Sound Analysis, Chest X Ray Computer Aided Detection
3 · Its place in the literature

Who cites it

84 citing papers in PubMed, 9 syntheses or guidelines pooled it.

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24 more citing papers are in PubMed but not listed here.

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

9 authors.

Miriam HarrisDepartment of Epidemiology and Biostatistics, McGill University, Montreal, Canada.ORCID 0000-0003-3870-4858
Amy QiDepartment of Medicine, McGill University Health Centre, Montreal, Canada.
Luke JeagalRespiratory Epidemiology and Clinical Research Unit, Montreal Chest Institute & Research Institute of the McGill University Health Centre, Montreal, Canada.
Nazi TorabiSt. Michael's Hospital, Li Ka Shing International Healthcare Education Centre, Toronto, Canada.ORCID 0000-0001-5447-9123
Dick MenziesDepartment of Epidemiology and Biostatistics, McGill University, Montreal, Canada.
Alexei KorobitsynLaboratories, Diagnostics & Drug Resistance Global TB Programme WHO, Geneva, Switzerland.
Madhukar PaiDepartment of Epidemiology and Biostatistics, McGill University, Montreal, Canada.
Ruvandhi R NathavitharanaDivision of Infectious Diseases, Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States of America.
Faiz Ahmad KhanDepartment of Epidemiology and Biostatistics, McGill University, Montreal, Canada.

Funding

Optimizing Diagnostic Strategies for TB Transmission Control in Health-Care FacilitiesK23AI132648 · NIAID · BETH ISRAEL DEACONESS MEDICAL CENTER · PI NATHAVITHARANA, RUVANDHI · 2018 to 2022
$945k
NIAID NIH HHS K23 AI132648World Health Organization 001
6 · The paper itself

Abstract

We undertook a systematic review of the diagnostic accuracy of artificial intelligence-based software for identification of radiologic abnormalities (computer-aided detection, or CAD) compatible with pulmonary tuberculosis on chest x-rays (CXRs). We searched four databases for articles published between January 2005-February 2019. We summarized data on CAD type, study design, and diagnostic accuracy. We assessed risk of bias with QUADAS-2. We included 53 of the 4712 articles reviewed: 40 focused on CAD design methods ("Development" studies) and 13 focused on evaluation of CAD ("Clinical" studies). Meta-analyses were not performed due to methodological differences. Development studies were more likely to use CXR databases with greater potential for bias as compared to Clinical studies. Areas under the receiver operating characteristic curve (median AUC [IQR]) were significantly higher: in Development studies AUC: 0.88 [0.82-0.90]) versus Clinical studies (0.75 [0.66-0.87]; p-value 0.004); and with deep-learning (0.91 [0.88-0.99]) versus machine-learning (0.82 [0.75-0.89]; p = 0.001). We conclude that CAD programs are promising, but the majority of work thus far has been on development rather than clinical evaluation. We provide concrete suggestions on what study design elements should be improved.

Indexed as

Area Under CurveArtificial IntelligenceDiagnosis, Computer-AssistedHumansPredictive Value of TestsQuality Assurance, Health CareRadiographySensitivity and SpecificitySoftwareTuberculosis, Pulmonary

Identifiers

PMID31479448
PMCPMC6719854

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.