  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 a
limited number of methods relying on biometric or other medical data. The
automatic detection of a periprosthetic joint infection from tomography imaging
is a task never addressed before. This study introduces a novel method for
early detection of the hip prosthesis infections analyzing Computed Tomography
images. The proposed solution is based on a novel ResNeSt Convolutional Neural
Network architecture trained on samples from more than 100 patients. The
solution showed exceptional performance in detecting infections with an
experimental high level of accuracy and F-score.
