ReviewFrontiers in medicine2026
Artificial intelligence in osteoporosis assessment using CT imaging: a scoping review.
Review in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for opportunistic screening of osteoporosis across multiple imaging modalities: a systematic review.Frontiers in medicine · 2026Pooled it
- Multilevel Lumbar CT Attenuation Beyond L1: Comparison with QCT-Derived Volumetric Bone Mineral Density and Prevalent Fragility Fracture Status in a Diagnostic Referral Cohort.Diagnostics (Basel, Switzerland) · 2026Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: This scoping review aimed to systematically summarize and map current research on the application of artificial intelligence (AI) in CT-based osteoporosis assessment, with a focus on methodological approaches, anatomical target regions, and reported algorithmic performance across existing studies. Methods: PubMed, EMBASE, and Web of Science databases were searched for studies published between January 1995 and December 2025. Eligible studies applied AI, machine learning, or deep learning techniques to CT images for osteoporosis classification, bone mineral density (BMD) estimation, or fracture-risk prediction. Data extraction covered study characteristics, imaging sources, analytical workflows, and validation methods. Results: A total of 51 studies were included. Most were retrospective (84.3%) and single-center (84.3%), with nearly half conducted in China. Study objectives clustered around osteoporosis diagnosis (45.1%), opportunistic screening (39.2%), and fracture-risk prediction (15.7%). Diagnostic and screening models generally achieved high performance (AUC 0.80-0.997 and 0.781-0.99, respectively), whereas fracture-risk prediction showed more modest accuracy (AUC 0.702-0.92). Across studies, technical workflows varied widely, encompassing Hounsfield Units (HU)-based quantitative analyses, radiomics-based models, end-to-end deep learning, and multimodal approaches. Such methodological diversity, combined with inconsistent validation strategies, limits direct comparison and reduces overall generalizability. Conclusion: Current evidence shows that AI-enhanced CT can achieve diagnostic and screening performance comparable to DXA and QCT, although fracture-risk prediction still requires improvement through multimodal data integration. However, methodological heterogeneity and the lack of standardized workflows limit cross-study comparability and clinical translation. Integrating AI into routine CT pipelines may reduce screening costs, enable earlier detection and intervention, and help mitigate the global burden of osteoporosis.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.