ReviewJBMR plus2025
Critical evaluation of 3D-DXA and 3D-Shaper: methodological limitations and their implications.
Review in JBMR plus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Concerns regarding the critical evaluation of 3D-Shaper.JBMR plus · 2026Article
- Article
- Reliability of methods in assessing proximal femur bone quality for hip prosthesis selection.Scientific reports · 2026Article
- Use of DXA-derived 3D-modeling, as implemented by 3D-Shaper, for the assessment of fracture risk in a population-based setting.Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research · 2026Article
- Relationship between bone mineral density and strength derived from 3D-shaper and HR-pQCT in patients with X-linked osteoporosis related to PLS3.Archives of osteoporosis · 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
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
3D-DXA, as implemented in the software tool 3D-Shaper, is a software method that generates a 3D reconstruction of the proximal femur from a single 2D DXA image by registering a statistical model. Implementations of 3D-DXA aim to provide estimates of trabecular, cortical, and structural parameters, similar to those derived from quantitative computed tomography (QCT). As the inventor and developer of the software methods upon which 3D-DXA is built, I have been observing its adoption and widespread use with increasing concern. This article provides a critical evaluation of the methodological limitations inherent to 3D-DXA and discusses their implications for research and patient care. The primary issue is that the limited visibility of the cortex in a DXA image prevents 3D-DXA from accurately deriving cortical parameters. Instead, the software relies on predictions based on overall BMD rather than direct cortical measurements. This may lead to results that do not reflect actual cortical measurements. Additional concerns include the population bias due to the statistical model being derived from a specific demographic, and limited reconstruction accuracy by using single-view DXA images. These limitations have likely resulted in incorrect measurements and research outcomes, which have largely gone unrecognized due to the use of inappropriate performance assessment metrics and the absence of multiple comparison corrections in studies involving 3D-DXA. Despite these limitations, 3D-DXA has received regulatory approval in various countries, potentially compromising the accuracy of clinical diagnoses and treatment decisions. By highlighting these issues, this article aims to inform clinicians, researchers, and regulatory bodies about the significant limitations of 3D-DXA. It underscores the urgent need for a reevaluation of its use in research and clinical settings to prevent misinterpretation of results and to ensure patient safety.
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.