SynthesisBMC cancer2025
Accuracy of artificial intelligence in detecting tumor bone metastases: a systematic review and meta-analysis.
Synthesis in BMC cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 2 of them syntheses that pooled it.
What it found
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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.
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Who cites it
5 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence for bone metastases: a systematic review and meta-analysis of diagnostic and prognostic performance.Frontiers in oncology · 2026Pooled it
- Machine learning for the prediction of diabetes-related amputation: a systematic review and meta-analysis of diagnostic test accuracy.Clinical and experimental medicine · 2025Pooled it
- Enabling earlier detection of spinal lesions in CT imaging with artificial intelligence-a case study.Frontiers in artificial intelligence · 2026Article
- AI for Lesion Detection in Musculoskeletal Radiology.Journal of the Korean Society of Radiology · 2025Review
- Applications and Performance of Artificial Intelligence in Spinal Metastasis Imaging: A Systematic Review.Journal of clinical medicine · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
backgroundBone metastases (BM) represent a prevalent complication of tumors. Early and accurate diagnosis, however, is a significant hurdle for radiologists. Recently, artificial intelligence (AI) has emerged as a valuable tool to assist radiologists in the detection of BM. This meta-analysis was undertaken to evaluate the AI diagnostic accuracy for BM.
methodsTwo reviewers performed an exhaustive search of several databases, including Wei Pu (VIP) database, China National Knowledge Infrastructure (CNKI), Web of Science, Cochrane Library, Ovid-Embase, Ovid-Medline, Wan Fang database, and China Biology Medicine (CBM), from their inception to December 2024. This search focused on studies that developed and/or validated AI techniques for detecting BM in magnetic resonance imaging (MRI) or computed tomography (CT). A hierarchical model was used in the meta-analysis to calculate diagnostic odds ratio (DOR), negative likelihood ratio (NLR), positive likelihood ratio (PLR), area under the curve (AUC), specificity (SP), and pooled sensitivity (SE). The risk of bias and applicability were assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST), while the Transparent Reporting of a multivariable prediction model for individual prognosis or diagnosis-artificial intelligence (TRIPOD-AI) was employed for evaluating the quality of evidence.
resultThis review covered 20 articles, among them, 16 studies were included in the meta-analysis. The results revealed a pooled SE of 0.88 (0.82-0.92), a pooled SP of 0.89 (0.84-0.93), a pooled AUC of 0.95 (0.92-0.96), PLR of 8.1 (5.57-11.80), NLR of 0.14 (0.09-0.21) and DOR of 58 (31-109). When focusing on imaging algorithms. Based on ML, a pooled SE of 0.88 (0.77-0.92), SP 0.88 (0.82-0.92), and AUC 0.93 (0.91-0.95). Based on DL, a pooled SE of 0.89 (0.81-0.95), SP 0.89 (0.81-0.94), and AUC 0.95 (0.93-0.97).
conclusionThis meta-analysis underscores the substantial diagnostic value of AI in identifying BM. Nevertheless, in-depth large-scale prospective research should be carried out for confirming AI's clinical utility in BM management.
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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.