Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 Oct;35(10):1681-1692.
doi: 10.1007/s00198-024-07179-1. Epub 2024 Jul 10.

Artificial intelligence-enhanced opportunistic screening of osteoporosis in CT scan: a scoping Review

Alberto Paderno  1   2 , Elmer Jeto Ataide Gomes  3 , Leonard Gilberg  3 , Leander Maerkisch  4 , Bianca Teodorescu  3   5 , Ali Murat Koç  3   6 , Mathias Meyer  7   8
Affiliations

Artificial intelligence-enhanced opportunistic screening of osteoporosis in CT scan: a scoping Review

Alberto Paderno et al. Osteoporos Int. 2024 Oct.

Erratum in

Abstract

Purpose: This scoping review aimed to assess the current research on artificial intelligence (AI)--enhanced opportunistic screening approaches for stratifying osteoporosis and osteopenia risk by evaluating vertebral trabecular bone structure in CT scans.

Methods: PubMed, Scopus, and Web of Science databases were systematically searched for studies published between 2018 and December 2023. Inclusion criteria encompassed articles focusing on AI techniques for classifying osteoporosis/osteopenia or determining bone mineral density using CT scans of vertebral bodies. Data extraction included study characteristics, methodologies, and key findings.

Results: Fourteen studies met the inclusion criteria. Three main approaches were identified: fully automated deep learning solutions, hybrid approaches combining deep learning and conventional machine learning, and non-automated solutions using manual segmentation followed by AI analysis. Studies demonstrated high accuracy in bone mineral density prediction (86-96%) and classification of normal versus osteoporotic subjects (AUC 0.927-0.984). However, significant heterogeneity was observed in methodologies, workflows, and ground truth selection.

Conclusions: The review highlights AI's promising potential in enhancing opportunistic screening for osteoporosis using CT scans. While the field is still in its early stages, with most solutions at the proof-of-concept phase, the evidence supports increased efforts to incorporate AI into radiologic workflows. Addressing knowledge gaps, such as standardizing benchmarks and increasing external validation, will be crucial for advancing the clinical application of these AI-enhanced screening methods. Integration of such technologies could lead to improved early detection of osteoporotic conditions at a low economic cost.

Keywords: Artificial intelligence; Bone mineral density; Computer tomography; Opportunistic screening; Osteoporosis; Spine.

PubMed Disclaimer

References

    1. Wright NC, Looker AC, Saag KG et al (2014) The recent prevalence of osteoporosis and low bone mass in the United States based on bone mineral density at the femoral neck or lumbar spine. J Bone Miner Res 29:2520–2526. https://doi.org/10.1002/jbmr.2269 - DOI - PubMed
    1. Singer A, Exuzides A, Spangler L et al (2015) Burden of illness for osteoporotic fractures compared with other serious diseases among postmenopausal women in the United States. Mayo Clin Proc 90:53–62. https://doi.org/10.1016/j.mayocp.2014.09.011 - DOI - PubMed
    1. Fink HA, Milavetz DL, Palermo L et al (2005) What proportion of incident radiographic vertebral deformities is clinically diagnosed and vice versa? J Bone Miner Res 20:1216–1222. https://doi.org/10.1359/JBMR.050314 - DOI - PubMed
    1. Curtis JR, Carbone L, Cheng H et al (2008) Longitudinal trends in use of bone mass measurement among older Americans, 1999–2005. J Bone Miner Res 23:1061–1067. https://doi.org/10.1359/jbmr.080232 - DOI - PubMed
    1. Kanis JA (2002) Diagnosis of osteoporosis and assessment of fracture risk. Lancet 359:1929–1936. https://doi.org/10.1016/S0140-6736(02)08761-5 - DOI - PubMed

Publication types

MeSH terms