SynthesisFrontiers in bioengineering and biotechnology2026
Statistical shape modelling of the human lumbar spine: a scoping review characterising the research landscape and potential clinical applications.
Synthesis in Frontiers in bioengineering and biotechnology, 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
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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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0 citing papers in PubMed.
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: A systematic scoping review was conducted to characterise the current research landscape of statistical shape modelling of the lumbar spine, focusing on the proposed clinical applications of the method. Methods: MEDLINE, Embase, IEEE Xplore, and Scopus were searched for sources published before 17 June 2026. Sources were screened against predefined inclusion and exclusion criteria and data were extracted using a structured form in Covidence. Results: 2,930 records were identified for review. Following duplicate removal and screening, 85 studies remained for data extraction. Most studies were exploratory (57%) or descriptive (14%) in nature. The vertebral body was the most frequently isolated region, with L5 often excluded due to its anatomical distinctiveness. Reporting of population demographics and dataset composition was highly inconsistent, highlighting substantial heterogeneity in both methodology and population selection. Conclusion: The use of lumbar spine statistical shape models (SSMs) has been predominantly exploratory or descriptive. Whilst prognostic, diagnostic, and therapeutic applications have been proposed, none of the reviewed studies demonstrated a direct change to clinical practice or impact on patient care. No consensus exists on optimal approaches to multi-level modelling, vertebral level pooling, or anatomical boundary definition, and training datasets are predominantly drawn from Western, female-skewed populations. In the short term, SSMs are well suited to exploratory analyses of clinical populations, revealing patterns not captured by conventional measures. Progress will benefit from training data aligned to defined clinical questions, adequate sample sizes and demographic breadth, and consistent reporting of validation metrics such as compactness, generalisability and specificity. This will enable development towards a technology that can lead to meaningful clinical translation.
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