Assessment of X-ray ankle joint image projection correctness with the use of machine learning algorithms

Abstract

Purpose: Accurate geometrical measurements of ankle joint (AJ) X-rays are essential for planning and executing orthopaedic procedures like alloplasty. Reliable assessment of the projection correctness of the AJ radiograms has to precede such measurements, and it is thus a vital step in the process. To create an artificial intelligence-based tool for automatic assessment of the correctness of the X-ray image projection of AJ. Material and methods: 1062 antero-posterior and lateral AJ X-rays were categorized into correct and rotated groups based on the literature. The database was split with an 80 : 10 : 10 ratio for training, validation, and test sets, respectively. Data analysis was conducted using 32 targeted neural networks, evaluating with binary metrics: accuracy, precision, recall, and F1 score. Results: The Xception neural network yielded the best results. Accuracies of 1.0, 0.849, and 0.888 were obtained for the training, validation, and test sets, respectively. The test set metrics achieved by Xception were as follows: precision - 0.935, recall - 0.879, and F1 score - 0.906. Conclusions: The model achieved high accuracy in recognizing the projection correctness compared to literature reports, which can directly result in a reduction in the workload for radiologists or orthopaedic specialists, as well as a reduced risk of misdiagnosis.

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