Attention-based Pin Site Image Classification in Orthopaedic Patients with External Fixators

TL;DR

This paper presents an attention-based deep learning model with ERRC to classify pin site infections from images, achieving high accuracy and efficiency, which could improve infection management in orthopaedic patients.

Contribution

It introduces a novel attention-based DL model with ERRC for pin site infection classification, emphasizing relevant regions and reducing model complexity.

Findings

Achieved an AUC of 0.975 in classification accuracy.

Reduced model parameters to 5.77 million.

Demonstrated potential for automated infection detection from images.

Abstract

Pin sites represent the interface where a metal pin or wire from the external environment passes through the skin into the internal environment of the limb. These pins or wires connect an external fixator to the bone to stabilize the bone segments in a patient with trauma or deformity. Because these pin sites represent an opportunity for external skin flora to enter the internal environment of the limb, infections of the pin site are common. These pin site infections are painful, annoying, and cause increased morbidity to the patients. Improving the identification and management of pin site infections would greatly enhance the patient experience when external fixators are used. For this, this paper collects and produces a dataset on pin sites wound infections and proposes a deep learning (DL) method to classify pin sites images based on their appearance: Group A displayed signs of inflammation or infection, while Group B showed no evident complications. Unlike studies that primarily focus on open wounds, our research includes potential interventions at the metal pin/skin interface. Our attention-based deep learning model addresses this complexity by emphasizing relevant regions and minimizing distractions from the pins. Moreover, we introduce an Efficient Redundant Reconstruction Convolution (ERRC) method to enhance the richness of feature maps while reducing the number of parameters. Our model outperforms baseline methods with an AUC of 0.975 and an F1-score of 0.927, requiring only 5.77 M parameters. These results highlight the potential of DL in differentiating pin sites only based on visual signs of infection, aligning with healthcare professional assessments, while further validation with more data remains essential.