Abstract:
The persistent shortage of radiologists in Uganda, particularly in rural and underserved regions,
has led to significant delays and inaccuracies in the diagnosis of trauma-related injuries and
musculoskeletal conditions. This project presents the design and implementation of an AI-powered
mobile application for interpreting bone X-ray images, specifically targeting the detection of
fractures, joint dislocations, and effusions. Leveraging a fine-tuned YOLOv8 convolutional neural
network, the system is optimized for deployment on low-end Android devices and supports both
online and offline operation, making it suitable for resource-constrained environments. The model
was trained and validated on a large, annotated dataset of X-ray images, achieving a precision of
91.2% and a recall of 89.7%. The application provides real-time analysis by highlighting detected
abnormalities with bounding boxes and confidence scores, thereby supporting frontline healthcare
workers in making timely and informed clinical decisions. Usability testing demonstrated that the
tool is accessible to non-specialists and can significantly reduce diagnostic delays. The project
aligns with national digital health strategies and global efforts to leverage artificial intelligence for
equitable healthcare delivery. It also offers a scalable blueprint for AI integration in medical
diagnostics across other low- and middle-income countries.
