Tuberculosis (TB) remains a major public health challenge in the United States, with increasing numbers of reported cases in recent years. Delayed diagnosis contributes to ongoing transmission and poorer clinical outcomes, particularly among populations facing barriers to healthcare access. Migrant populations in New York City experience a disproportionately high burden of TB infection and disease, highlighting the need for innovative community-based screening strategies.
SPOT-TB (Screening with Portable X-rays for rapid recOgniTion of TB) is a prospective study evaluating a novel community-based TB screening strategy. The intervention combines mobile ultra-portable digital chest radiography, artificial intelligence-assisted image interpretation, tuberculosis symptom assessment, latent tuberculosis infection testing, and HIV testing to provide rapid, front-loaded screening while minimizing loss to follow-up. Participants with concerning screening findings are referred for appropriate clinical evaluation according to standard clinical practice.
The study will evaluate the feasibility, acceptability, and effectiveness of this integrated screening strategy for improving early identification of active pulmonary tuberculosis, facilitating linkage to care, and informing future community-based TB screening programs. The study will also provide information on TB and latent TB infection prevalence among migrant populations and the implementation of mobile AI-assisted screening in an urban U.S. setting.
