This invention relates to the field of intelligent
medical imaging diagnosis technology, specifically to an AI detection
system for occult
femoral neck fractures based on two-stage artifact contrast learning. The
system includes a
data acquisition and construction module, an artifact enhancement contrast pre-training module, a detection network fine-tuning module, an
inference and
visualization module, and a physician-assisted image reading module. Training and testing data are acquired through multi-center cohort construction. An anti-interference radiographic feature
encoder is obtained through artifact enhancement contrast pre-training. A single-stage detection network is then constructed by combining attention
feature aggregation and dynamic
upsampling modules to achieve precise detection. This invention can quickly output the probability of fracture presence, bounding boxes, and attention heatmaps. It can be embedded in clinical image reading workstations to provide real-time support, exhibits high sensitivity for occult
femoral neck fracture detection, strong multi-center generalization ability, significantly improves physician diagnostic efficiency, reduces the risk of missed diagnoses, and features fast
inference speed and strong
interpretability, making it suitable for clinical application in emergency trauma scenarios.