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. The test set comprised 30 normal and 10 pathological radiographs. The first model was used for detecting the carpal region, and the second for segmenting a region between Gilula's 2nd and 3rd arcs. The output of the segmentation model, trained multiple times with varying random initial parameter values and augmentations, was then assigned a probability of being normal or pathological through ensemble averaging. In the study dataset, the algorithm achieved an overall F1-score of 0.880: 0.928 in the normal subgroup, with 1.0 precision, and 0.833 in the pathological subgroup, with 1.0 recall (or sensitivity), demonstrating that diagnosis of perilunate dislocation can be improved by automatic analysis of anteroposterior radiographs. LEVEL OF EVIDENCE: : III.