Performance of universal and stratified computer-aided detection thresholds for chest x-ray-based tuberculosis screening: a cross-sectional, diagnostic accuracy study

Authors

David Isooba
Achilles Katamba (ORCID: https://orcid.org/0000-0002-2347-4183)
Ryoichi Okura
Peter James Kitonsa (ORCID: https://orcid.org/0000-0003-2971-216X)
David W. Dowdy (ORCID: https://orcid.org/0000-0003-0481-7475)
Annet Nalutaaya (ORCID: https://orcid.org/0000-0003-3903-303X)
Emily A. Kendall (ORCID: https://orcid.org/0000-0002-0083-422X)
Susan Birabwa
Joowhan Sung (ORCID: https://orcid.org/0000-0002-4726-9651)
Keneth Ndyabayunga
Ivan Mugabi
Deborah Nantale
Violet Nakiiza
Jonathan Magezi

Institutions

Johns Hopkins University (US)
Makerere University (UG)

Publication Details

Journal: The Lancet Digital Health
Published: 2025-11-26
DOI: https://doi.org/10.1016/j.landig.2025.100934
Primary Topic: COVID-19 diagnosis using AI
Type: article
Field-Weighted Citation Impact: 0.00

Funders

National Institutes of Health

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article

Performance of universal and stratified computer-aided detection thresholds for chest x-ray-based tuberculosis screening: a cross-sectional, diagnostic accuracy study

David Isooba, Achilles Katamba, Ryoichi Okura, Peter James Kitonsa et al.

Nov 26, 2025

The Lancet Digital Health

COVID-19 diagnosis using AI

article

Performance of universal and stratified computer-aided detection thresholds for chest x-ray-based tuberculosis screening: a cross-sectional, diagnostic accuracy study

David Isooba, Achilles Katamba, Ryoichi Okura, Peter James Kitonsa, David W. Dowdy, Annet Nalutaaya, Emily A. Kendall, Susan Birabwa, Joowhan Sung, Keneth Ndyabayunga, Ivan Mugabi, Deborah Nantale, Violet Nakiiza, Jonathan Magezi

November 26, 2025article en

Abstract

No abstract available for this paper.

The Lancet Digital HealthVol. 7(12)

Johns Hopkins University (US), Makerere University (UG)

National Institutes of Health

Openalex Percentile: Top 49%

COVID-19 diagnosis using AI

DOI Open Access PDF OpenAlex

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