Researchers investigated combining tuberculin skin testing (TST) for tuberculosis infection with an active case-finding program in peri-urban Uganda. The study used mobile chest X-rays analyzed by qXR v3, an AI-powered computer-aided detection system from Qure.ai, to generate risk scores determining who needed further testing. Participants with lower qXR scores indicating minimal active TB disease risk were offered TST to identify latent infection, while those with higher scores received sputum testing. This integrated approach aimed to identify both active TB cases and individuals who could benefit from preventive treatment.
Back
The results revealed significant implementation challenges. Of over 33,000 eligible participants, only 12% received TST placement, and just 55% returned for results. While qXR efficiently triaged participants, practical barriers limited program reach. Financial constraints emerged as the primary obstacle, with transportation costs and time away from work cited most frequently. Each additional 10 minutes of travel time reduced TST completion likelihood. However, individuals with previous TB exposure or current symptoms showed greater engagement.
These findings underscore that while qXR enabled efficient screening triage, logistical challenges including site relocations, weather disruptions, and supply shortages compounded difficulties requiring return visits. Without streamlined processes addressing financial and motivational barriers, integrated screening programs may have limited impact. Future TB prevention efforts should focus on understanding local barriers and developing solutions such as point-of-care alternatives to make prevention more accessible in high-burden communities.
