Improving Tuberculosis Diagnosis Through Artificial Intelligence (CAD4TB) and Stool Xpert MTB/RIF Testing: A Prospective Study From Oromia, Ethiopia.

ABSTRACT:

Background

Tuberculosis remains the leading cause of death by a single infectious agent globally, with Ethiopia among the highest tuberculosis- and human immunodeficiency virus/tuberculosis-burden countries. Diagnostic gaps-particularly among household contacts (HHCs) unable to expectorate-hinder early case detection. Computer-aided detection software for chest radiography and nonrespiratory molecular assays, such as stool-based Xpert MTB/RIF testing, represent promising strategies for scalable screening.

Methods

We conducted a prospective diagnostic accuracy study at St Luke Catholic Hospital, Oromia, Ethiopia, enrolling 478 participants (152 tuberculosis index patients and 326 HHCs). All HHCs ≥4 years underwent digital chest radiographic screening, with or without CAD4TB (

SUBMITTER: Guido G 

PROVIDER: S-EPMC12740636 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature