Progress and Future Direction of Artificial Intelligence-assisted 3D MRI Analysis in Orthopedics

Abstract

The convergence of artificial intelligence (AI) and 3D magnetic resonance imaging (MRI) is transforming orthopedic practice by overcoming traditional diagnostic limitations. This review synthesizes current advancements and future directions of AI-assisted 3D MRI analysis in orthopedics. Through critical evaluation of technical frameworks and clinical literature, we examine AI algorithms (including 3D CNNs and transformers), accelerated MRI acquisition techniques, and solutions for data heterogeneity and computational efficiency. Our analysis confirms that AI significantly enhances fracture classification accuracy, achieves exceptional segmentation precision for bone and cartilage structures, and reduces surgical complications through personalized planning and real-time navigation. Emerging strategies like federated learning address privacy concerns, while lightweight architectures optimize clinical deployment. Persistent challenges include data scarcity, model interpretability, and integration into healthcare systems. Future progress hinges on standardized multi-center validation, biomechanical simulation integration ("digital twins"), and regulatory alignment. AI-assisted 3D MRI promises to advance precision orthopedics but requires concerted collaboration across computational, engineering, and clinical domains to realize its full translational potential.

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