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Early detection of an infection prior to prosthesis removal (e.g., hips,knees or other areas) would provide significant benefits to patients.Currently, the detection task is carried out only retrospectively with alimited number of methods relying on biometric or other medical data. Theautomatic detection of a periprosthetic joint infection from tomography imagingis a task never addressed before. This study introduces a novel method forearly detection of the hip prosthesis infections analyzing Computed Tomographyimages. The proposed solution is based on a novel ResNeSt Convolutional NeuralNetwork architecture trained on samples from more than 100 patients. Thesolution showed exceptional performance in detecting infections with anexperimental high level of accuracy and F-score.

Early detection of hip periprosthetic joint infections through CNN on Computed Tomography images