ArticlePloS one2025
Using statistical modelling and machine learning in detecting bone properties: A systematic review protocol.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Modernizing Forensic Anthropology: A Data-driven Pipeline for Human Identification and Profiling.European journal of dentistry · 2026Article
- Explainable machine learning for osteoporosis detection in patients with osteopenia: model development and validation using routine clinical data from an Asian cohort.Frontiers in endocrinology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionOsteoporosis, a common condition characterised by decreased bone mass and microarchitectural deterioration, leading to increased fracture risk, is a significant health concern. Traditional diagnostic methods, such as Dual-energy X-ray Absorptiometry (DXA), have limitations in sensitivity and accessibility. However, the emergence of artificial intelligence (AI) and machine learning (ML) has brought promising tools capable of analysing complex medical data to enhance the detection and prediction of osteoporosis-related bone properties. This systematic review protocol outlines the methodology to evaluate the application and effectiveness of AI and ML methods in detecting bone properties and osteoporosis. It underscores their potential to revolutionise healthcare by providing more accurate and accessible osteoporosis detection and prediction tools.
methodsThis systematic review, which will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols (PRISMA-P) guidelines, will be comprehensive in its approach. A thorough search will be conducted across PubMed, Embase, IEEE Xplore, Scopus, Cochrane Library, and GitHub from their inception to March 2025. Studies involving adults aged 40 years and older that utilise AI/ML techniques to detect or predict bone density or other bone-related properties will be included. Two independent reviewers will perform screening, data extraction, and risk of bias assessments using appropriate tools such as RoB 2, ROBINS-I, QUADAS-2, PROBAST, and NOS. The comprehensive nature of this review ensures that no relevant study is overlooked. Data synthesis will involve narrative synthesis and, if applicable, meta-analysis using Review Manager (RevMan) and R software. DISCUSSION: This systematic review will comprehensively evaluate current AI and ML applications in detecting bone properties and osteoporosis. By identifying and analysing various AI/ML models and comparing them with traditional diagnostic methods, the review aims to highlight the effectiveness and potential of these technologies in clinical practice. The findings are expected to significantly impact healthcare professionals, researchers, and policymakers regarding advancements in AI/ML for bone health assessment and guide future research directions. Understanding the strengths and limitations of existing studies will be crucial in developing standardised protocols and facilitating the integration of AI/ML tools into routine osteoporosis screening and management. SYSTEMATIC REVIEW REGISTRATION: This Systematic Review Protocol was registered in PROSPERO (CRD42024587326).
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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.