AI-Driven Orthopedic Implant Identification in Indian Clinical Practice: A Dynamic Cross-Attention Swin Transformer...

Area of Science:

• Biomedical Engineering
• Computer Vision
• Artificial Intelligence

Background:

• Accurate identification of orthopedic implants is crucial for effective surgical planning.
• Existing methods face challenges in efficiency and accuracy for complex implant identification.

Purpose of the Study:

• To propose a novel Dynamic Cross-Attention enabled Cross-Swin Transformer for efficient and accurate orthopedic implant identification.
• To enhance pre-operative planning for orthopedic revision surgeries.

Main Methods:

• Utilized Hybrid Patch Embedding for low-dimensional feature capture and Cross-Swin Transformer for hierarchical feature representation.
• Employed Linear Multi-head Self-Attention and Efficient Channel Attention for computational efficiency and inter-channel dependency capture.
• Integrated Adaptive mixture units, Attention Feature Fusion Unit, and an improved genetic algorithm for hyper-parameter optimization.

Main Results:

• Achieved a high accuracy of 99.03% in identifying orthopedic implants.
• Demonstrated superior performance compared to prior orthopedic implant identification systems.
• The approach involves simple calculations for efficient identification.

Conclusions:

• The proposed technique accurately identifies the manufacturer and model of orthopedic implants.
• This advancement significantly aids orthopedic surgeons in pre-operative planning for revision surgery.
• The method offers a robust solution for orthopedic implant recognition.