TBScreen.AI: Study Protocol for an Inclusive AI-Based Chest X-Ray Screening System for Tuberculosis in Remote Areas of Indonesia

Background Indonesia ranks second in tuberculosis (TB) burden globally and continues to face a significant diagnosis gap, particularly in remote areas with limited access to radiologists. Approximately 14% of new TB cases are left undetected by TB services. Utilizing artificial intelligence-based computer-aided detection (AI-CAD) applied to chest X-ray (CXR) imaging offers a potential solution to improve TB screening. However, AI systems may exacerbate existing inequities if they are developed and implemented without consideration of gender, age, disability, ethnicity, and socioeconomic barriers to care. This research aims to develop and train an AI-CAD model that considers the balance of gender, age, ethnicity, and disability status. The study will validate an AI-CAD model for TB screening among different socio-cultural and disability status groups. Additionally, it will assess barriers to TB services and inclusive strategies for AI-CAD implementation in remote areas of Indonesia. Method This mixed-methods study comprises three integrated components. Part I focuses on the development of TBScreen.AI, an AI-CAD model trained on gender-, age-, ethnicity-, and disability-balanced CXR data from hospitals in Java and Papua. Part II uses a prospective cross-sectional design to evaluate the diagnostic accuracy of TBScreen.AI among individuals with presumptive TB attending four health facilities in Indonesia. AI-generated interpretations are compared with radiologist readings and the final patient diagnosis established by site physicians, based on comprehensive examinations, including anamnesis, physical examination, laboratory investigations, and follow-up assessments. Part III integrates quantitative and qualitative methods to assess access to TB services and to identify inclusive implementation strategies that support equitable deployment of AI-assisted screening. Discussion This study investigates the development of AI-CAD tools for TB case detection using digital chest radiography, while also considering gender equality, disability, and social inclusion. The implementation of an accurate and inclusive AI-CAD tool as a second interpreter of CXR images for TB case detection will prevent treatment delays due to radiologist unavailability and reduce waiting time for CXR image interpretation in remote areas with limited health resources.