CAD LUS4TB · Computer assisted diagnosis with lung ultrasound for community based pulmonary tuberculosis triage in Benin, Mali and South-Africa

With chest X-ray and molecular tests virtually absent at the primary healthcare care level, where most patients with presumptive TB present in sub-Saharan Africa (SSA), there is a need for accessible, affordable and scalable diagnostic tools for TB triage. CAD LUS4TB represents an interdisciplinary partnership spanning across Western (Francophone) and Southern-African regions with EU countries, aimed at enhancing access to effective TB triage to rule out TB disease among symptomatic adult patients presenting at the primary healthcare level. This initiative focuses on generating population-tailored evidence and advocating for the integration of computer-assisted diagnosis (CAD) using artificial intelligence (AI) to support the implementation of lung ultrasound (LUS) into healthcare policy. Unlike typical vertical triage tests, US has multiple other existing AI-assisted diagnostic tools and can facilitate a multi disease approach after TB exclusion, including for pneumonia and cardiovascular assessment. We propose to externally validate and deploy a novel digital technology adapting image-based analysis tools and software for mobile phone ultrasound applications. AI technology sharing serves as one of its key pillars. The adoption of CAD-LUS requires a comprehensive, interdisciplinary, translational approach to clinical research. Our consortium comprises these key fields, including clinical research, diagnostics, implementation science, social science, health economics and policy translation, as well as data/computer science. It addresses all expected outcomes and contributes to several specific expected impacts of this call. Evidence on the integration of CAD-LUS is expected to accelerate adoption of accessible triage tools for TB in SSA and support achieving target 3.3 of the Sustainable Development Goals. The CAD LUS4TB tool is anticipated to achieve a high diagnostic yield due to its user-friendliness, scalability and possibility to address multiple diseases.