Evidence map›Paper›PMID 40864252›Full record

ArticleOsteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA2025

Artificial intelligence system for predicting areal bone mineral density from plain X-rays.

Huy Gia Nguyen, Dinh-Tan Nguyen, Thach Son Tran, Sai Ho Ling, Lan Thuc Ho-Pham, Tuan Van Nguyen

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In one paragraph

Article in Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Huy Gia Nguyen *School of Biomedical Engineering, University of Technology Sydney (UTS), City Campus (Broadway) Building 11, Level 10, PO BOX 123, Broadway, NSW, 2007, Australia.
Dinh-Tan Nguyen *School of Biomedical Engineering, University of Technology Sydney (UTS), City Campus (Broadway) Building 11, Level 10, PO BOX 123, Broadway, NSW, 2007, Australia.
Thach Son TranSchool of Biomedical Engineering, University of Technology Sydney (UTS), City Campus (Broadway) Building 11, Level 10, PO BOX 123, Broadway, NSW, 2007, Australia.
Sai Ho LingSchool of Biomedical Engineering, University of Technology Sydney (UTS), City Campus (Broadway) Building 11, Level 10, PO BOX 123, Broadway, NSW, 2007, Australia.
Lan Thuc Ho-PhamSaigon Precision Medicine Research Center, Ho Chi Minh City, Vietnam.
Tuan Van NguyenSchool of Biomedical Engineering, University of Technology Sydney (UTS), City Campus (Broadway) Building 11, Level 10, PO BOX 123, Broadway, NSW, 2007, Australia. tuanvan.nguyen@uts.edu.au.ORCID http://orcid.org/0000-0002-3246-6281

Funding

National Health and Medical Research Council APP1195305
6 · The paper itself

Abstract

introductionDual-energy X-ray absorptiometry (DXA) is the standard method for assessing areal bone mineral density (aBMD), diagnosing osteoporosis, and predicting fracture risk. However, DXA's availability is limited in resource-poor areas. This study aimed to develop an artificial intelligence (AI) system capable of estimating aBMD from standard radiographs.

methodsThe study was part of the Vietnam Osteoporosis Study, a prospective population-based research involving 3783 participants aged 18 years and older. A total of 7060 digital radiographs of the frontal pelvis and lateral spine were taken using the FCR Capsula XLII system (Fujifilm Corp., Tokyo, Japan). aBMD at the femoral neck and lumbar spine was measured with DXA (Hologic Horizon, Hologic Corp., Bedford, MA, USA). An ensemble of seven deep-learning models was used to analyze the X-rays and predict bone mineral density, termed "xBMD".

resultsThe correlation between xBMD and aBMD was evaluated using Pearson's correlation coefficients. The correlation between xBMD and aBMD at the femoral neck was strong (

conclusionsThese findings indicate that AI can accurately predict aBMD and identify individuals at high risk of fractures. This AI system could provide an efficient alternative to DXA for osteoporosis screening in settings with limited resources and high patient demand. An AI system developed to predict aBMD from X-rays showed strong correlations with DXA (

Indexed as

Artificial IntelligenceBone DensityOsteoporosisAbsorptiometry, PhotonAdultAgedAged, 80 and overDeep LearningFemaleFemur NeckHumansLumbar VertebraeMaleMiddle AgedOsteoporotic FracturesProspective StudiesArtificial intelligence; bone mineral densityFractureOsteoporosisPlain radiographyXBMD

Identifiers

PMID40864252
PMCPMC12628433

What Socratic holds

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LicenceCC BY-NC
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Registered trials

None linked

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 Socratic graph.