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

TL;DR

This paper presents a novel CNN-based method for early detection of hip periprosthetic joint infections using CT images, achieving high accuracy and F-score, which could improve patient outcomes.

Contribution

Introduces the first automatic detection method for hip infections from CT images using a novel ResNeSt CNN architecture.

Findings

High accuracy in infection detection

F-score demonstrates strong model performance

Effective analysis on over 100 patient samples

Abstract

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.