1.  Introduction

Recent advances in artificial intelligence (AI) and three-dimensional magnetic resonance imaging (3D MRI) have ushered in a new era in orthopaedics, fueling progress in diagnosis, treatment planning, and outcome assessment. Existing studies have demonstrated that AI can effectively process 3D MRI data to achieve high-precision segmentation of bones, cartilage, and soft tissues, facilitate early detection of osteoarthritis and bone tumours, and provide critical support for clinical decision-making. This review focuses on AI-assisted 3D MRI analysis in orthopaedics, systematically summarising its current progress and future development directions. This text delves into the technical frameworks, clinical applications, challenges, solutions, and upcoming technological and clinical innovations. A comprehensive review of relevant literature and integration of technical and clinical insights is used as the research method.

This work aims to provide a holistic perspective, offering practical guidance for addressing key issues like data enhancement and algorithm optimisation, and laying a foundation for promoting the clinical translation of AI-assisted 3D MRI technologies to improve orthopaedic care quality.

2.  Technical framework for integrating 3D MRI and AI

The convergence of artificial intelligence (AI) and three-dimensional magnetic resonance imaging (3D MRI) has forged a revolutionary paradigm in orthopaedics, enabling unprecedented precision in both diagnosis and treatment planning [1]. This integration harnesses volumetric data acquisition, advanced reconstruction algorithms, and sophisticated deep learning (DL) architectures to overcome longstanding musculoskeletal imaging limitations, such as the low resolution inherent in traditional 2D imaging and difficulty in capturing dynamic biomechanical information.

2.1.  Data acquisition and preprocessing

Modern orthopedic MRI protocols incorporate accelerated techniques to optimize imaging efficiency and quality. Compressed sensing (CS) combined with DL reconstruction reduces knee, shoulder, and ankle scan times by 38-75% while maintaining diagnostic quality, which is crucial for improving patient compliance, especially for those with limited endurance. Cone-beam volumetric technologies (e.g., WR-3D) enable weight-bearing, full-spine acquisitions with expanded z-axis coverage, providing unique biomechanical data that static, non-weight-bearing imaging cannot capture [2]. Advancements in technology address patient discomfort, improve image quality, and enhance data reliability through DL-based artifact correction, isotropic resampling, and adversarial learning, ensuring consistency and minimizing heterogeneity in multi-center studies.

2.2.  AI algorithm fundamentals and typology

AI transforms 3D MRI data into actionable clinical insights through three key paradigms. Supervised learning leverages convolutional neural networks (CNNs) to attain high accuracy (>98.7%) in detecting and classifying complex fractures. In osteoarthritis assessment, these models surpass traditional Kellgren-Lawrence staging by quantifying subtle cartilage texture and subchondral bone changes that are imperceptible to the naked eye. Unsupervised learning excels at uncovering latent patterns, such as trabecular bone abnormalities, and enables automatic clustering of tumor subtypes based on heterogeneity patterns that correlate with histological findings. It also identifies novel biomarkers for early osteoporosis risk prediction. Deep learning architectures include 3D CNNs (e.g., DenseVNet), which achieve Dice scores >0.97 in precise segmentation of bones and cartilage [3]. Transformer-based models capture long-range dependencies, allowing for holistic joint assessment; for example, they can precisely quantify scoliosis curvature angles by considering global spinal alignment in whole-spine analysis. Hybrid CNN-transformer networks, combining the local feature extraction strength of CNNs and the global context understanding of transformers, represent the state-of-the-art in comprehensive joint evaluation.

2.3.  Technical challenges

Several key hurdles must be addressed for clinical implementation. Data heterogeneity represents a primary challenge, though federated learning offers a viable solution: it enables model training across decentralized datasets without transferring sensitive patient data, thus preserving privacy while leveraging diverse data source [4]. Computational complexity poses another barrier, as high-resolution 3D volumes—such as 512³ knee MRI scans, which require over 12GB of GPU memory—exert significant strain on computational resources. To mitigate this, 2D-3D hybrid networks reduce parameters by 40% while maintaining segmentation accuracy (DSC >0.95), balancing efficiency and performance [5]. Algorithmic interpretability is also critical, as "black-box" model decisions hinder clinical trust. Uncertainty quantification (e.g., Monte Carlo dropout) highlights low-confidence regions, and transformer attention mapping visually explains which image areas influence diagnostic decisions, enhancing model transparency.

3.  Clinical applications in orthopedic practice

Artificial intelligence (AI) applied to 3D MRI analysis has significantly advanced orthopedic care, enabling earlier diagnosis, personalized surgical planning, and precise outcome prediction, demonstrating the tangible impact of computational imaging on patient management.

3.1.  Disease diagnosis and classification

AI algorithms, particularly 3D convolutional neural networks (CNNs), have revolutionized musculoskeletal disease detection by identifying subtle pathological changes earlier than conventional imaging. For osteoarthritis (OA), quantitative MRI analysis of meniscal morphology achieves high accuracy in predicting disease development years before radiographic changes [6]. Deep learning systems using 3D MRI features and clinical data are able to differentiate benign from malignant bone lesions, using quantitative texture analysis and transformer-based models [3].. For complex inflammatory conditions like seronegative spondyloarthropathy, AI models integrating multi-sequence MRI features recognize characteristic bone marrow edema and erosions, facilitating timely diagnosis and treatment [6].

3.2.  Surgical planning and navigation

The integration of AI with 3D MRI enables unprecedented surgical precision. In spinal deformity correction, automated systems eliminate observer variability in Cobb angle measurement, while weight-bearing 3D imaging provides comprehensive deformity analysis critical for planning [7]. This allows surgeons to precisely calculate vertebral rotation, sagittal balance, and load distribution. For joint replacement, AI-driven MRI analysis enables parametric implant design with submillimeter precision, informing selection and positioning based on automated measurements (e.g., tibial slope). Segmentation of bone quality from MRI predicts areas needing augmentation [1]. Combined with 3D printing, this facilitates patient-specific instruments, reducing operative time and blood loss [7]. Robotic systems integrating real-time navigation with preoperative 3D MRI plans also significantly improve accuracy in procedures like pedicle screw placement compared to freehand techniques [1].

3.3.  Treatment response assessment and prognosis

Quantitative AI analysis of longitudinal 3D MRI transforms outcome prediction. Deep learning algorithms track microstructural changes in fracture callus, predicting union or non-union months before radiographic visibility with high accuracy [8]. In osteoporosis management, 3D MRI texture analysis sensitively monitors changes in trabecular bone microarchitecture, detecting treatment responses earlier than dual-energy X-ray absorptiometry (DEXA). Preoperative AI models analyzing 3D MRI datasets now accurately forecast functional outcomes (e.g., pain resolution, improvement) following joint arthroplasty, spinal decompression, and fracture fixation [8]. These predictive capabilities optimize surgical timing, manage expectations, and identify high-risk patients needing intensified rehabilitation [1].

4.  Technical challenges and innovative solutions

The clinical translation of AI-assisted 3D MRI analysis in orthopedics encounters significant technical and socio-technical hurdles, necessitating continuous innovation to bridge the gap between research and real-world use. These challenges—encompassing data limitations, algorithms constraints, and barriers to clinical adoption—require targeted solutions to ensure AI tools can reliably support orthopedic practice, from diagnosis to post-treatment follow-up.

4.1.  Data limitations and enhancement strategies

The scarcity of large, high-quality, annotated datasets remains a primary bottleneck. Complex orthopedic anatomies—such as the detailed structures of joints, spinal segments, and soft tissue attachments—require expert segmentation, which is time-consuming and resource-intensive, limiting dataset growth. Key strategies address this critical issue: 3D Generative Adversarial Networks and cross-modal translation enhance training data for rare conditions, while generating realistic synthetic MRI volumes and expanding datasets for segmentation algorithms [9]. Few-shot Learning: Prototypical networks, using pre-trained feature extractors, achieve >85% accuracy with as few as 15 annotated cases per pathology by transferring knowledge from common musculoskeletal conditions like osteoarthritis and fractures [9].

4.2.  Algorithm optimization strategies 

The computational intensity of processing 3D MRI volumes—characterized by high resolution and volumetric complexity—coupled with the demand for models that generalize across diverse patient cohorts, continues to drive innovation in the field

Lightweight architectures, developed through Neural Architecture Search (NAS), enable the design of efficient, task-specific models. For cartilage segmentation, NAS-derived models reduce parameters by ~40% while maintaining >95% Dice similarity, easing deployment in clinical settings with limited computational power [10].

Multi-modal fusion, which integrates 3D MRI with radiographs or gait analysis via cross-attention mechanisms, enhances diagnostic precision. For example, systems predicting knee osteoarthritis progression achieve AUCs >0.93, outperforming imaging-only approaches by capturing both structural and functional data [10].

Uncertainty quantification, powered by Bayesian deep learning, generates voxel-wise uncertainty maps alongside segmentations. These maps highlight low-confidence regions—such as metal artifacts from prior surgeries—to guide radiologist review, thereby strengthening clinical trust in AI outputs [11].

4.3.  Clinical integration barriers 

Real-world adoption of AI-assisted 3D MRI in orthopaedics is hindered by practical obstacles that extend beyond technical performance alone.

Workflow integration remains a critical challenge, addressed through visual analytics that transform AI outputs into interactive 3D visualizations—such as pathology heatmaps and surgical risk zones—aligned with clinical reasoning. Natural language interfaces allow verbal queries about AI findings, aiding time-sensitive tasks like preoperative planning [1]. Regulatory pathways present another hurdle, primarily due to the "predicate gap"—a lack of previously approved comparable tools—slows approval, but frameworks like the FDA's SaMD Pre-Cert Program and EU MDR reference centers streamline evaluations for orthopedic AI tools [1,12]. Reimbursement mechanisms also require refinement. While value-based arrangements are emerging—for example, bundled payments for AI-optimized surgeries with 30% lower infection rates—specific Current Procedural Terminology (CPT) codes for AI-assisted image interpretation are still needed to ensure financial sustainability and drive widespread adoption [1].

5.  Future directions

The rapid evolution of AI-assisted 3D MRI analysis will transform orthopedic care through synergistic technological and clinical innovations.

5.1.  Technology-driven innovations

5.1.1.  Automated end-to-end systems

Future platforms will unify AI-driven MRI acquisition (e.g., accelerated scans with enhanced resolution), automated pathology quantification (e.g., cartilage thickness mapping), and generative reporting using large language models. These systems will synthesize quantitative imaging biomarkers with electronic health records to produce clinically contextualized diagnostic reports, minimizing manual interpretation [3,13].

5.1.2.  Privacy-preserving distributed learning

Cryptographic techniques like split learning—where institutions collaboratively train model segments on local data—and homomorphic encryption (enabling computation on encrypted MRI) will overcome data-sharing barriers. Early multi-center implementations for fracture detection achieve equivalent accuracy to centralized training while guaranteeing patient privacy through zero raw data exchange [13].

5.1.3.  Edge computing integration

Custom hardware accelerators (e.g., FPGA-based 3D CNN processors) will enable sub-second intraoperative MRI analysis directly on imaging devices. Real-time segmentation of residual tumor tissue during spinal procedures exemplifies this capability, where AI instantly highlights resection targets on updated scans without cloud dependency [1,14].

5.2.  Clinically-oriented advancements

5.2.1.  Robust multi-center validation

Large-scale initiatives must standardize protocols across diverse populations and imaging hardware to ensure generalizability. Critical focus includes mitigating algorithmic bias via fairness-aware learning—adjusting model weights to ensure consistent performance across ethnicities and body mass indices—validated through prospective trials at ≥15 global sites [15].

5.2.2.  Personalized orthopedic medicine

Integrating quantitative MRI biomarkers (e.g., trabecular bone texture) with multi-omics data (genomics/proteomics) will enable mechanistic disease profiling. Correlations between cartilage proteoglycan loss on MRI and COL2A1 gene expression allow non-invasive monitoring of molecular degeneration, while combining polygenic risk scores with MRI-based predictors improves osteoarthritis prevention targeting by 40% [16].

5.2.3.  Biomechanical simulation integration

Finite element analysis (FEA) pipelines will automatically convert 3D MRI segmentations into patient-specific biomechanical models. These simulate joint contact forces during dynamic activities (e.g., stair descent), predicting implant loosening risks and fracture susceptibility under physiological loads. The "digital twin" paradigm evolves these models throughout treatment, enabling virtual testing of surgical approaches before implementation [17].

6.  Conclusion

Artificial intelligence (AI) has significantly improved 3D MRI analysis in orthopedics, enhancing efficiency and precision in musculoskeletal imaging and reshaping clinical decision-making processes. AI-driven algorithms, such as 3D convolutional neural networks (CNNs), transformer-based models, and hybrid architectures, enable accurate segmentation of complex orthopedic structures, early detection of pathological changes, and personalized surgical planning. However, technical challenges remain unresolved, such as data heterogeneity and computational complexity. Research gaps exist, with most studies focusing on algorithm development and technical performance verification, while few explore the practical application of AI technologies in real clinical scenarios. There is also a lack of sufficient research on the long-term effectiveness of AI-assisted 3D MRI analysis in guiding treatment and predicting prognosis. To advance AI-assisted 3D MRI analysis, key directions include developing lightweight AI models, strengthening multi-center cooperation, improving clinical integration strategies, and improving regulatory frameworks and ethical norms for data use. By advancing these aspects, AI-assisted 3D MRI analysis is expected to play a more important role in orthopedics, promoting precision medicine and improving patient care quality.