CheXaid: deep learning assistance for physician diagnosis of tuberculosis using chest x-rays in patients with HIV
Open accessAbstract
Tuberculosis (TB) is the leading cause of preventable death in HIV-positive patients, and yet often remains undiagnosed and untreated. Chest x-ray is often used to assist in diagnosis, yet this presents additional challenges due to atypical radiographic presentation and radiologist shortages in regions where co-infection is most common. We developed a deep learning algorithm to diagnose TB using clinical information and chest x-ray images from 677 HIV-positive patients with suspected TB from two hospitals in South Africa. We then sought to determine whether the algorithm could assist clinicians in the diagnosis of TB in HIV-positive patients as a web-based diagnostic assistant. Use of the algorithm resulted in a modest but statistically significant improvement in clinician accuracy ( p = 0.002), increasing the mean clinician accuracy from 0.60 (95% CI 0.57, 0.63) without assistance to 0.65 (95% CI 0.60, 0.70) with assistance. However, the accuracy of assisted clinicians was significantly lower ( p < 0.001) than that of the stand-alone algorithm, which had an accuracy of 0.79 (95% CI 0.77, 0.82) on the same unseen test cases. These results suggest that deep learning assistance may improve clinician accuracy in TB diagnosis using chest x-rays, which would be valuable in settings with a high burden of HIV/TB co-infection. Moreover, the high accuracy of the stand-alone algorithm suggests a potential value particularly in settings with a scarcity of radiological expertise.
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Fusing imaging features from chest X-rays with clinical covariates improves the diagnosis of tuberculosis in individuals with HIV.
“Another bimodal study fused imaging features from chest X-rays with clinical covariates, improving the diagnosis of tuberculosis in individuals with HIV 130”
From Multimodal biomedical AI (2022) · cites this paper as [130]
A study focusing on tuberculosis diagnosis through chest X-rays demonstrated that among 13 participating physicians, 77% exhibited enhanced diagnostic accuracy when utilizing XAI for visual explanations compared to assessments without XAI.
“For example, a study in [26], focusing on tuberculosis diagnosis through chest X-rays, demonstrated that among 13 participating physicians, 77% exhibited enhanced diagnostic accuracy when utilizing XAI for visual explanations compared to assessments without XAI.”
From Evaluating Explainable Artificial Intelligence (XAI) techniques in chest radiology imaging through a human-centered Lens (2024) · cites this paper as [26]
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- Authors
- Rajpurkar, P., O’Connell, C., Schechter, A., Asnani, N., Li, J., Kiani, A., Ball, R. L., Mendelson, M., Maartens, G., van Hoving, D. J., Griesel, R., Ng, A. Y., Boyles, T. H., & Lungren, M. P.
- Affiliations
- Stanford University (US)
- Massachusetts General Hospital (US)
- University of Cape Town (ZA)
- Corresponding author
- Rajpurkar, Pranav
- ORCID
- Venue
- npj Digital Medicine
- Published
- 2020-09-09
- Volume
- 3
- Issue
- 1
- Pages
- 115-115
- ISSN
- 2398-6352 (ISSN-L)
- PMC
- PMC7481246
- OpenAlex
- W3084346490
- Cited by
- 137
- References
- 41
- Citations by year
- 2026: 14 · 2025: 24 · 2024: 23 · 2023: 28 · 2022: 30 · 2021: 16 · 2020: 2
- FWCI
- 9.25
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- npj Digital Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- 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 › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- tuberculosis diagnosis, HIV/TB coinfection, chest radiography, deep learning, clinical decision support, radiologist shortage, South Africa
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