Unlocking the Future of Orthopaedic Imaging: A Comprehensive Update on the Role and Benefits of The Medical Open Network for AI (MONAI)

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dc.contributor.authorAngthong, Chayanin
dc.contributor.authorAngthong, Wirana
dc.contributor.authorPongsakonpruttikul, Napat
dc.date.accessioned2026-08-06T10:53:20Z
dc.date.available2026-08-06T10:53:20Z
dc.date.issued2025-12-31
dc.description.abstractBACKGROUND: Deep learning (DL) has revolutionized orthopaedic imaging, transitioning from traditional radiomics-based analysis to powerful, data-driven diagnostic and prognostic models. However, a persistent lack of methodological standardization has limited clinical translation. The Medical Open Network for AI (MONAI), an open-source PyTorch-based framework, addresses this gap by providing domain-specific tools optimized for medical imaging. This review evaluates MONAI's role and benefits in orthopaedics across diagnosis, treatment planning, and outcomes prediction. MATERIAL AND METHODS: A comprehensive literature synthesis was conducted, examining studies utilizing MONAI for musculoskeletal imaging. We assessed technical attributes including architecture, data handling, loss functions, and multimodal integration and their applications in fracture detection, disease grading, surgical planning, and prognostic modeling. RESULTS: MONAI demonstrated superior efficiency in handling 3D/4D orthopaedic imaging data and managing class imbalance using specialized medical loss functions (e.g., Dice and Tversky). Diagnostic models achieved near-expert accuracy in fracture detection and quantitative osteoarthritis grading, providing explainable, and reproducible outputs. MONAI enabled automated, high-fidelity 3D reconstruction for personalized implant design and 3D printing integration. Prognostically, it outperformed surgeons in predicting arthroplasty complications, revealing latent imaging biomarkers. The framework's evolution into MONAI Multimodal - with agentic AI and radiomics integration - enhanced personalized, multimodal risk assessment. CONCLUSIONS: 1. MONAI establishes a standardized, transparent infrastructure that accelerates orthopaedic AI research and clinical translation. Its integration of domain-optimized architectures, multimodal data fusion, and explainable AI tools enables accurate diagnosis, individualized surgical planning, and reliable outcome prediction. 2. Adoption of MONAI-based pipelines is strongly recommended to promote reproducibility, regulatory readiness, and clinician trust in next generation of precision orthopaedic care.
dc.identifier.citationOrtopedia Traumatologia Rehabilitacja, 27(6), 269-277, 2025
dc.identifier.doi10.5604/01.3001.0055.6925
dc.identifier.issn20844336
dc.identifier.other2-s2.0-105035819430
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/17529
dc.sourceOrtopedia Traumatologia Rehabilitacja
dc.subjectartificial intelligence
dc.subjectdeep learning
dc.subjectimage segmentation; treatment outcome prediction
dc.subjectMONAI framework
dc.subjectorthopedic imaging
dc.titleUnlocking the Future of Orthopaedic Imaging: A Comprehensive Update on the Role and Benefits of The Medical Open Network for AI (MONAI)
dc.typeReview

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