Deep learning–based automated detection algorithm for active pulmonary tuberculosis on chest radiographs: diagnostic performance in systematic screening of asymptomatic individuals
Open accessAbstract
Objectives Performance of deep learning-based automated detection (DLAD) algorithms in systematic screening for active pulmonary tuberculosis is unknown. We aimed to validate DLAD algorithm for detection of active pulmonary tuberculosis and any radiologically identifiable relevant abnormality on chest radiographs (CRs) in this setting. Methods We performed out-of-sample testing of a pre-trained DLAD algorithm, using CRs from 19.686 asymptomatic individuals (ages, 21.3 ± 1.9 years) as part of systematic screening for tuberculosis between January 2013 and July 2018. Area under the receiver operating characteristic curves (AUC) for diagnosis of tuberculosis and any relevant abnormalities were measured. Accuracy measures including sensitivities, specificities, positive predictive values (PPVs), and negative predictive values (NPVs) were calculated at pre-defined operating thresholds (high sensitivity threshold, 0.16; high specificity threshold, 0.46). Results All five CRs from four individuals with active pulmonary tuberculosis were correctly classified as having abnormal findings by DLAD with specificities of 0.959 and 0.997, PPVs of 0.006 and 0.068, and NPVs of both 1.000 at high sensitivity and high specificity thresholds, respectively. With high specificity thresholds, DLAD showed comparable diagnostic measures with the pooled radiologists (p values > 0.05). For the radiologically identifiable relevant abnormality (n = 28), DLAD showed an AUC value of 0.967 (95% confidence interval, 0.938-0.996) with sensitivities of 0.821 and 0.679, specificities of 0.960 and 0.997, PPVs of 0.028 and 0.257, and NPVs of both 0.999 at high sensitivity and high specificity thresholds, respectively. Conclusions In systematic screening for tuberculosis in a low-prevalence setting, DLAD algorithm demonstrated excellent diagnostic performance, comparable with the radiologists in the detection of active pulmonary tuberculosis. Key points • Deep learning-based automated detection algorithm detected all chest radiographs with active pulmonary tuberculosis with high specificities and negative predictive values in systematic screening. • Deep learning-based automated detection algorithm had comparable diagnostic measures with the radiologists for detection of active pulmonary tuberculosis on chest radiographs. • For the detection of radiologically identifiable relevant abnormalities on chest radiographs, deep learning-based automated detection algorithm showed excellent diagnostic performance in systematic screening.
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The CAD-first model decreased physicians' workloads by limiting the number of CXRs for them to interpret to those rated as TB-presumptive.
“The CAD-first model decreased physicians’ workloads by limiting the number of CXRs for them to interpret to those rated as TB-presumptive; programmatic implementation in other settings has shown similar benefits. (34, 39)”
From Active case finding to detect symptomatic and subclinical pulmonary tuberculosis disease: implementation of computer-aided detection for chest radiography in Viet Nam (2024) · cites this paper as [34]
The Discussion says the algorithm designated 74 of 20,135 radiographs as positive and could reduce radiologists’ reading workload by more than 99% if used as a first reader. It proposes that radiologists read those positive radiographs preferentially; it does not report an implemented CAD-first model that actually decreased physicians’ workloads. (LLM, conf=0.84)
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References 31 · Cited by 47
Research works printed but not linked: 2
- Pande T, Cohen C, Pai M, Ahmad Khan F (2016) Computer-aided detection of pulmonary tuberculosis on digital chest radiographs: a systematic review. Int J Tuberc Lung Dis 20:1226-1230
- Jaeger S, Karargyris A, Candemir S et al (2014) Automatic tuber- culosis screening using chest radiographs. IEEE Trans Med Imaging 33:233-245
- Authors
- Lee, J. H., Park, S., Hwang, E. J., Goo, J. M., Lee, W. Y., Lee, S., Kim, H., Andrews, J. R., & Park, C. M.
- Affiliations
- Lunit (South Korea) (KR)
- Seoul National University (KR)
- Armed Forces Capital Hospital (KR)
- Stanford Medicine (US)
- Corresponding author
- Park, Chang Min
- ORCID
- Venue
- European Radiology
- Published
- 2020-08-28
- Volume
- 31
- Issue
- 2
- Pages
- 1069-1080
- ISSN
- 0938-7994 (ISSN-L)
- 1432-1084
- OpenAlex
- W3081760285
- Cited by
- 47
- References
- 31
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 8 · 2023: 13 · 2022: 11 · 2021: 3 · 2020: 1
- FWCI
- 3.42
- Type
- journal article
- Language
- en
- Open access
- green
- pure.amsterdamumc.nl
- Licence
- Locations
- European Radiology (publishedVersion)
- PubMed (publishedVersion)
- Pure Amsterdam UMC (acceptedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Seoul National University Open Repository (Seoul National University) (submittedVersion)
- Funders
- Lunit
- Seoul National University
- Seoul National University Hospital
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Health Sciences › Medicine › Infectious Diseases › Tuberculosis Research and Epidemiology
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- pulmonary tuberculosis, chest radiograph, deep learning, automated detection algorithm, tuberculosis screening, asymptomatic individuals, low-prevalence settings, diagnostic performance, negative predictive value, specificity, sensitivity, positive predictive value, area under the receiver operating characteristic, out-of-sample validation
- MeSH terms
- Deep Learning, Adult, Algorithms, Humans, Radiography, Sensitivity and Specificity, Radiography, Thoracic, Tuberculosis, Pulmonary, Young Adult
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