A deep learning-based algorithm for automatic detection of perilunate dislocation in frontal wrist radiographs.

This study proposes a Deep Learning algorithm to automatically detect perilunate dislocation in anteroposterior wrist radiographs. A total of 374 annotated radiographs, 345 normal and 29 pathological, of skeletally mature adolescents and adults aged ≥16 years were used to train, validate and test two YOLOv8 deep neural models. The training set included 245 normal and 15 pathological radiographs; the pathological training set was supplemented by 240 radiographs obtained by data augmentation.