Deep Learning for Automated Classification of Hip Hardware on Radiographs.

Area of Science:

• Radiology
• Artificial Intelligence
• Orthopedic Surgery

Background:

• Radiographic assessment of orthopedic hardware is crucial for patient care.
• Manual classification by radiologists can be time-consuming and subject to inter-observer variability.

Purpose of the Study:

• To develop and evaluate a deep learning model for automated classification of orthopedic hardware on pelvic and hip radiographs.
• To assess the clinical utility of such a model in reducing radiologist workload and improving report consistency.

Main Methods:

• Retrospective analysis of 4279 pelvic and hip radiographs from 1073 patients.
• Training two convolutional neural networks (EfficientNet-B4, NFNet-F3) for hardware classification.
• Performance evaluation on an independent test set (851 studies) and comparison against five subspecialty radiologists.

Main Results:

• Both models achieved an area under the receiver operating characteristic curve (AUC) of 0.99 or greater for most hardware classes.
• The models demonstrated 97% accuracy, performing comparably to or better than human radiologists.
• Excellent inter-reader agreement was observed, with Cohen's kappa coefficients ranging from 0.96 to 0.97.

Conclusions:

• Deep learning models can accurately classify various orthopedic hip hardware types.
• The developed AI demonstrates high performance, comparable to subspecialty-trained radiologists.
• This technology holds potential for clinical implementation to aid in radiographic interpretation.