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

Aim: This paper proposes a novel Dynamic Cross-Attention enabled Cross-Swin Transformer approach for identifying orthopedic implants efficiently. Methods: Initially, low-dimensional features are captured by employing Hybrid Patch Embedding mechanism, while the Cross-Swin transformer constructs a hierarchical feature representation. Then, Linear Multi-head Self-Attention minimizes computational complexity and broadens the receptive field for identifying large-scale features. After that, the Efficient Channel Attention strategy facilitates cross-channel communication and captures inter-channel dependencies effectively, thus avoiding dimensionality reduction. Additionally, the individualized trade-off among local convolution and global attention is maintained by Adaptive mixture units. The multi-dimensional features are effectively fused by Attention Feature Fusion Unit for optimizing network efficiency. Furthermore, an improved genetic algorithm optimizes hyper-parameters, employing chaotic opposition and Tabu search algorithms to balance global and local optimization. Results: Overall, the proposed approach identifies orthopedic implants with simple calculations and attains greater accuracy of 99.03% compared to prior orthopedic implant identification systems in terms of some common assessing measures. Conclusions: The research findings show how well the proposed technique works to identify the manufacturer and the model of orthopedic implants accurately, aiding orthopedic surgeons in the pre-operative planning of revision surgery. Supplementary Information: The online version contains supplementary material available at 10.1007/s43465-025-01432-3.