Evidence map›Paper›PMID 39167230›Full record

SynthesisJournal of bone and mineral metabolism2024

Diagnostic accuracy of chest X-ray and CT using artificial intelligence for osteoporosis: systematic review and meta-analysis.

Norio Yamamoto, Akihiro Shiroshita, Ryota Kimura, Tomohiko Kamo, Hirofumi Ogihara, Takahiro Tsuge

Erratum issuedAbstract readMeta-AnalysisSystematic Review
PubMed Publisher
In one paragraph

Synthesis in Journal of bone and mineral metabolism, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Clinical and economic impact of deep learning-enhanced opportunistic osteoporosis screening using chest radiographs with and without the osteoporosis self-assessment tool for Asians (OSTA).Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2026
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  4. Explainable opportunistic osteoporosis screening from chest X-rays: a retrospective comparison of foundation models.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2026
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  6. Review
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Norio YamamotoDepartment of Epidemiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Japan. norio-yamamoto@umin.ac.jp.ORCID http://orcid.org/0000-0002-7902-9994
Akihiro ShiroshitaScientific Research WorkS Peer Support Group (SRWS-PSG), Osaka, Japan.
Ryota KimuraScientific Research WorkS Peer Support Group (SRWS-PSG), Osaka, Japan.
Tomohiko KamoScientific Research WorkS Peer Support Group (SRWS-PSG), Osaka, Japan.
Hirofumi OgiharaScientific Research WorkS Peer Support Group (SRWS-PSG), Osaka, Japan.
Takahiro TsugeDepartment of Epidemiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionArtificial intelligence (AI)-based systems using chest images are potentially reliable for diagnosing osteoporosis.

methodsWe performed a systematic review and meta-analysis to assess the diagnostic accuracy of chest X-ray and computed tomography (CT) scans using AI for osteoporosis in accordance with the diagnostic test accuracy guidelines. We included any type of study investigating the diagnostic accuracy of index test for osteoporosis. We searched MEDLINE, EMBASE, the Cochrane Central Register of Controlled Trials, and IEEE Xplore Digital Library on November 8, 2023. The main outcome measures were the sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) for osteoporosis and osteopenia. We described forest plots for sensitivity, specificity, and AUC. The summary points were estimated from the bivariate random-effects models. We summarized the overall quality of evidence using the Grades of Recommendation, Assessment, Development, and Evaluation approach.

resultsNine studies with 11,369 participants were included in this review. The pooled sensitivity, specificity, and AUC of chest X-rays for the diagnosis of osteoporosis were 0.83 (95% confidence interval [CI] 0.75, 0.89), 0.76 (95% CI 0.71, 0.80), and 0.86 (95% CI 0.83, 0.89), respectively (certainty of the evidence, low). The pooled sensitivity and specificity of chest CT for the diagnosis of osteoporosis and osteopenia were 0.83 (95% CI 0.69, 0.92) and 0.70 (95% CI 0.61, 0.77), respectively (certainty of the evidence, low and very low).

conclusionsThis review suggests that chest X-ray with AI has a high sensitivity for the diagnosis of osteoporosis, highlighting its potential for opportunistic screening. However, the risk of bias of patient selection in most studies were high. More research with adequate participants' selection criteria for screening tool will be needed in the future.

Indexed as

Artificial IntelligenceOsteoporosisTomography, X-Ray ComputedHumansRadiography, ThoracicSensitivity and SpecificityArtificial intelligenceChest radiographComputed tomographyOsteopeniaOsteoporosis

Identifiers

PMID39167230

What OpenQuestion holds

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Registered trials

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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.