Suppression of immobilisation device on wrist radiography to improve fracture visualisation

Objectives: This study validates the use of CycleGAN-generated wrist radiographs with digitally removed splints, specifically assessing their impact on fracture visualisation. Materials and methods: We retrospectively collected wrist radiographs from 1748 patients who had imaging before and after splint application at a single institution. The dataset was divided into training (1696 patients, 5353 images) and testing sets (52 patients, 965 images). A CycleGAN-based model was trained to generate splint-free wrist radiographs (generated “splint-less”) from the original “splint” images. A pre-trained fracture detection model (YOLO8s) was used to assess fracture detection performance on three image groups: original “splint-less” radiographs, original “splint” radiographs, and generated “splint-less” radiographs. Two radiologists scored the generated images. Subtraction images quantified overall image alterations. Precision, recall, and F1 scores were used to compare fracture detection performance. Results: CycleGAN effectively generated splint-suppressed radiographs with minimal remaining splint density (< 10% remaining in 97.99%), hardware distortion (< 10% change in 100%), anatomical distortion (< 10% in 99.63%), and fracture lesion changes (< 10% change in 100%). New artefacts were rare (absent in 97.54%). Notably, the fracture detection model achieved higher precision (0.94 vs. 0.92), recall (0.63 vs. 0.5), and F1 score (0.75 vs. 0.65) on the generated “splint-less” radiographs compared to the original “splint” radiographs, approaching the performance on original “splint-less” radiographs (F1 0.71). Furthermore, greater image alterations by CycleGAN correlated with larger improvements in fracture detection. Conclusion: CycleGAN successfully removed splint densities from wrist radiographs with splints. Key Points: Question Can CycleGAN (Generative Adversarial Networks), designed for image-to-image translation, generate synthetic “splint-less” radiographs to improve fracture visualisation in follow-up radiographs? Findings Removal of splint densities from wrist radiographs using Generative Adversarial Networks preserved anatomical structures and improved the performance of a fracture detection model. Clinical relevance Generated splint-less radiographs can enhance the performance of wrist fracture detection in wrist radiographs, benefiting both human clinicians and AI-powered diagnostic tools.