This study validates the use of CycleGAN-generated wrist radiographs with digitally removed splints, specifically assessing their impact on fracture visualisation.

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.

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.

CycleGAN successfully removed splint densities from wrist radiographs with splints.

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.