A Deep-Learning-Based Algorithm for Automatic Detection of Perilunate Dislocation in Frontal Wrist Radiographs.

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Abstract

This study proposes a deep-learning algorithm to automatically detect perilunate dislocation on anteroposterior wrist radiographs. A total of 374 annotated radiographs, 345 normal and 29 pathological, were used to train, validate and test two YOLO v8 deep neural models. The first model was used for detecting the carpal region, and the second for segmenting a region between Gilula’s second and third 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 this 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 the diagnosis of perilunate dislocation can be improved through automatic analysis of anteroposterior radiographs. Level of evidence: III.Copyright © 2024. Published by Elsevier Masson SAS.