REVIEW article
Front. Med.
Sec. Geriatric Medicine
Artificial intelligence in osteoporosis assessment using CT imaging: a scoping review
Hanwen Cheng 1
Yajun Zhang 2
Meng meng 1
Simin Liu 3
Yang Yang 1
Yuyang Ran 4
Yuhui Kou 1
1. Peking University People's Hospital, Beijing, China
2. Aerospace Science and Industry Corporation 731 hospital, Beijing, China
3. Guangdong Medical University, Zhanjiang, China
4. Changzhi Medical College, Changzhi, China
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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. Conclusions: 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
Summary
Keywords
artificial intelligence, computed tomography, diagnosis, Osteoporosis, review
Received
02 January 2026
Accepted
02 February 2026
Copyright
© 2026 Cheng, Zhang, meng, Liu, Yang, Ran and Kou. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Yuhui Kou
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