ReviewInternational journal of burns and trauma2026
Using artificial intelligence for distinguishing benign and malignant vertebral compression fractures by computed tomography: a scoping review.
Review in International journal of burns and trauma, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe purpose of this scoping review is to compile the evidence regarding the use of computed tomography (CT) to distinguish between benign and malignant vertebral compression fractures (VCFs) in artificial intelligence (AI) applications.
methodsWe systematically searched PubMed/MEDLINE, DOAJ, and ScienceDirect through October 2025 for studies reporting AI-based diagnostic approaches (machine learning with radiomics features, deep learning with convolutional neural networks, or hybrid models) applied to CT images in adult patients with VCFs. Eligible studies required diagnostic performance metrics and definitive reference standards.
resultsSix retrospective studies from Asia and Europe have been found, involving 1,767 participants with 3,408 vertebrae, including 1,257 benign and 1,442 malignant vertebral fractures. The diagnostic performance of AI algorithms is high, achieving an area under the curve (AUC) score from 0.76 to 0.99. Deep learning approaches achieved performance comparable to experienced radiologists, while radiomics-based methods provided interpretable quantitative features. Hybrid models combining both approaches showed synergistic benefits. Three studies with external validation confirmed reasonable generalizability, though with some performance reduction in independent datasets.
conclusionsAI-assisted evaluation of CT scans also has good potential in differentiating benign from malignant VCFs, which could reduce unnecessary MRI referrals and improve the initial evaluation time. However, the data regarding this are limited because of retrospective studies with variable methods and limited multicenter data, which are still required to implement this in routine clinical practice.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.