Evidence map›Paper›PMID 39702597›Full record

SynthesisScientific reports2024

Artificial intelligence in risk prediction and diagnosis of vertebral fractures.

Srikar R Namireddy, Saran S Gill, Amaan Peerbhai, Abith G Kamath, Daniele S C Ramsay, Hariharan Subbiah Ponniah, Ahmed Salih, Dragan Jankovic, Darius Kalasauskas, Jonathan Neuhoff and 3 more

Abstract readMeta-AnalysisSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 1 pooled it
–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

18 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Machine learning-based prediction of perioperative complications in spine surgery: a large-scale model development and validation study.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026
    Article
  4. Review
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  7. Clinical implementation of AI for vertebral fracture detection in CT aligned with fracture liaison services: high prevalence of undiagnosed vertebral fractures.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2026
    Article
  8. Article
  9. Review
  10. Article
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  12. Article
  13. Is orthopaedics entering the age of generative AI?-A narrative review of current applications challenges and future directions.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2026
    Review
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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

13 authors.

Srikar R NamireddyImperial Brain & Spine Initiative, Imperial College London, London, UK.
Saran S GillImperial Brain & Spine Initiative, Imperial College London, London, UK.
Amaan PeerbhaiImperial Brain & Spine Initiative, Imperial College London, London, UK.
Abith G KamathImperial Brain & Spine Initiative, Imperial College London, London, UK.
Daniele S C RamsayImperial Brain & Spine Initiative, Imperial College London, London, UK.
Hariharan Subbiah PonniahImperial Brain & Spine Initiative, Imperial College London, London, UK.
Ahmed SalihImperial Brain & Spine Initiative, Imperial College London, London, UK.
Dragan JankovicDepartment of Neurosurgery, University Medical Center Mainz, Langenbeckstraße 1, Mainz, Germany.
Darius KalasauskasDepartment of Neurosurgery, University Medical Center Mainz, Langenbeckstraße 1, Mainz, Germany.
Jonathan NeuhoffCenter for Spinal Surgery and Neurotraumatology, Berufsgenossenschaftliche Unfallklinik Frankfurt am Main, Frankfurt, Germany.
Andreas KramerDepartment of Neurosurgery, University Medical Center Mainz, Langenbeckstraße 1, Mainz, Germany.
Salvatore RussoDepartment of Neurosurgery, Imperial College Healthcare NHS Trust, London, UK.
Santhosh G ThavarajasingamImperial Brain & Spine Initiative, Imperial College London, London, UK. santhoshgthava@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the increasing prevalence of vertebral fractures, accurate diagnosis and prognostication are essential. This study assesses the effectiveness of AI in diagnosing and predicting vertebral fractures through a systematic review and meta-analysis. A comprehensive search across major databases selected studies utilizing AI for vertebral fracture diagnosis or prognosis. Out of 14,161 studies initially identified, 79 were included, with 40 undergoing meta-analysis. Diagnostic models were stratified by pathology: non-pathological vertebral fractures, osteoporotic vertebral fractures, and vertebral compression fractures. The primary outcome measure was AUROC. AI showed high accuracy in diagnosing and predicting vertebral fractures: predictive AUROC = 0.82, osteoporotic vertebral fracture diagnosis AUROC = 0.92, non-pathological vertebral fracture diagnosis AUROC = 0.85, and vertebral compression fracture diagnosis AUROC = 0.87, all significant (p < 0.001). Traditional models had the highest median AUROC (0.90) for fracture prediction, while deep learning models excelled in diagnosing all fracture types. High heterogeneity (I² > 99%, p < 0.001) indicated significant variation in model design and performance. AI technologies show considerable promise in improving the diagnosis and prognostication of vertebral fractures, with high accuracy. However, observed heterogeneity and study biases necessitate further research. Future efforts should focus on standardizing AI models and validating them across diverse datasets to ensure clinical utility.

Indexed as

Artificial IntelligenceSpinal FracturesFractures, CompressionHumansOsteoporotic FracturesPrognosisRisk AssessmentAIArtificial intelligenceMachine learningMLNon-pathological vertebral fracturesOF, VFOsteoporotic vertebral fracturesVertebral compression fractures

Identifiers

PMID39702597
PMCPMC11659610

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.