Automated detection and classification of shoulder arthroplasty models using deep learning

Area of Science:

• Artificial Intelligence in Medical Imaging
• Orthopedic Surgery
• Machine Learning for Medical Diagnosis

Background:

• Accurate identification of total shoulder arthroplasty (TSA) models is crucial for patient care and research.
• Current methods for identifying TSA models can be time-consuming and require specialized expertise.
• Deep convolutional neural networks (DCNNs) offer a potential solution for automated implant recognition.

Purpose of the Study:

• To develop and evaluate DCNNs for detecting shoulder arthroplasty implants.
• To assess the DCNNs' ability to differentiate between TSA and reverse TSA (RTSA).
• To determine the DCNNs' performance in classifying five specific TSA models.

Main Methods:

• Utilized 482 radiography studies including native shoulders, RTSA, and five TSA models.
• Trained ResNet DCNN binary classifiers for implant detection, TSA vs. RTSA differentiation, and specific TSA model classification.
• Employed 20-fold augmentation, AUC-ROC analysis for performance assessment, and class activation mapping for feature interpretability.

Main Results:

• DCNN achieved an AUC-ROC of 1.0 for implant detection and 0.97 for TSA vs. RTSA differentiation.
• Class activation mapping highlighted the DCNN's focus on characteristic implant components.
• DCNNs classified five TSA models with AUC-ROCs ranging from 0.86 to 1.0, emphasizing unique design features.

Conclusions:

• DCNNs demonstrate high accuracy in identifying TSA implants and distinguishing between TSA and RTSA.
• The developed DCNNs can effectively classify five specific TSA models.
• This work provides a proof of concept for an automated arthroplasty atlas, facilitating rapid model identification.