Perilunate dislocations are a rare but serious pathology, often undetected in the emergency setting. In this study, a Deep Learning algorithm is proposed to automatically detect perilunate dislocations in frontal radiographs. A total of 374 annotated frontal wrist radiographs, comprising 345 normal and 29 pathological ones from adolescents and adults aged 16 and above with skeletal maturity, were utilized to train, validate, and test two YOLOv8 deep neural models. The first model is responsible 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 augmentations, is then given a probability to be normal or pathological through ensemble averaging. On the considered dataset, the proposed algorithm achieves an overall F1-score of 0.880. The F1-score reaches 0.928 on the normal subgroup with a precision of 1.0, and 0.833 on the pathological subgroup with a recall (or sensitivity) of 1.0, demonstrating that the diagnosis of perilunate dislocations can be improved through automatic analysis of frontal radiographs.