Survey of AI-Powered Approaches for Osteoporosis Diagnosis in Medical Imaging

2025-09-29

AI Summary

Osteoporosis progresses insidiously, and early radiographic identification is critical for preventing fragility fractures; however, existing AI research remains fragmented and lacks systematic integration. This paper introduces the first triaxial unified framework spanning imaging modalities (DXA, X-ray, CT, MRI), clinical tasks (risk prediction, diagnosis, subtyping), and AI methodologies (classical ML, CNNs, Transformers, self-supervised learning, XAI), underpinned by a PRISMA-guided systematic review of 127 studies. Key contributions include: (1) construction of a domain-specific knowledge graph and a technology roadmap; (2) identification of three critical bottlenecks—data scarcity, insufficient external validation, and limited interpretability; and (3) establishment of an interdisciplinary collaboration paradigm for AI developers, radiologists, and clinicians to advance precision early screening and individualized management of osteoporosis.

Technology Category

• Knowledge Representation and Reasoning: Ontologies
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Osteoporosis silently erodes skeletal integrity worldwide; however, early detection through imaging can prevent most fragility fractures. Artificial intelligence (AI) methods now mine routine Dual-energy X-ray Absorptiometry (DXA), X-ray, Computed Tomography (CT), and Magnetic Resonance Imaging (MRI) scans for subtle, clinically actionable markers, but the literature is fragmented. This survey unifies the field through a tri-axial framework that couples imaging modalities with clinical tasks and AI methodologies (classical machine learning, convolutional neural networks (CNNs), transformers, self-supervised learning, and explainable AI). Following a concise clinical and technical primer, we detail our Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-guided search strategy, introduce the taxonomy via a roadmap figure, and synthesize cross-study insights on data scarcity, external validation, and interpretability. By identifying emerging trends, open challenges, and actionable research directions, this review provides AI scientists, medical imaging researchers, and musculoskeletal clinicians with a clear compass to accelerate rigorous, patient-centered innovation in osteoporosis care. The project page of this survey can also be found on Github.

Problem

• Surveying AI methods for osteoporosis diagnosis using medical imaging
• Addressing fragmented literature through unified tri-axial framework
• Identifying challenges like data scarcity and interpretability in osteoporosis care

Innovation

• Tri-axial framework links imaging with AI methods
• PRISMA-guided systematic review methodology
• Synthesizes insights on data scarcity and interpretability