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Improving Tuberculosis Diagnosis Through AI (CAD4TB) and Stool- Xpert MTB/RIF: a prospective study from Oromia, Ethiopia

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Prospective study reveals computer-aided detection improves tuberculosis detection in Ethiopia, highlighting diagnostic accuracy challenges.

CAD4TB improved tuberculosis diagnosis, with sensitivity of 77% and specificity of 93%.
Stool Xpert showed high concordance with sputum Xpert testing, indicating reliable diagnostics.
The assessment involved 478 participants in a diagnostic accuracy study in Oromia, Ethiopia.
Findings suggest the combined use of CAD4TB and stool Xpert could enhance early detection in low-resource settings.

Guido et al. (2025) studied this question.

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Prospective study reveals computer-aided detection improves tuberculosis detection in Ethiopia, highlighting diagnostic accuracy challenges.