Evidence map›Paper›PMID 42606698›Full record

ArticleEuropean spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society2026

Detecting scoliosis at scale using automated Cobb angle analysis in the UK Biobank.

Marili Niglas, Brandon Whitcher, Dimitri Amiras, Marjola Thanaj, Nicolas Basty, Camilo Bell-Bradford, E Louise Thomas, Jimmy D Bell

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Article in European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Marili NiglasResearch Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK. m.niglas@westminster.ac.uk.
Brandon WhitcherResearch Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK.
Dimitri AmirasImaging Department, Imperial College Healthcare NHS Trust, London, UK.
Marjola ThanajResearch Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK.
Nicolas BastyResearch Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK.
Camilo Bell-BradfordResearch Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK.
E Louise ThomasResearch Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK.
Jimmy D BellResearch Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeAdult degenerative scoliosis arises after skeletal maturity in an initially normal spine, primarily driven by age-related degeneration. The Cobb angle, the angle between the most tilted vertebrae typically derived from radiographs, remains the clinical standard for assessing curvature severity, yet large-scale evaluation using MRI has not been feasible. This study developed an automated method for Cobb angle estimation from chemical-shift-based water-fat separation (Dixon) MRI and applied it to the UK Biobank to characterise the prevalence and curvature within the general population.

methodsAbdominal Dixon MRI data from 33,889 UK Biobank participants were analysed. Vertebral bone marrow compartments were segmented using a neural network based model, and spinal curvature was quantified using a centroid-based spline-fitting algorithm. Sex-stratified linear regression analyses were performed to explore associations between spinal curvature and anthropometric, socioeconomic, and health-related traits, including back pain and body composition.

resultsWhile scoliosis was clinically diagnosed or self-reported in only 0.5% of participants, the automated approach detected scoliosis (Cobb angle > 10°) in 28%, of which 95% were mild (< 25°). Females exhibited higher average Cobb angles and greater curvature across all age groups. Multivariable linear regression revealed significant associations between Cobb angle and age, paraspinal muscle fat infiltration, chronic back pain, and visceral adipose tissue in both sexes, and with iliopsoas muscle volume in males only (all p < 0.00055, Bonferroni-adjusted threshold).

conclusionThis fully automated approach enables large-scale, population-based assessment of spinal curvature, revealing adult scoliosis to be substantially under-recognised and closely linked to muscle quality and back pain.

Indexed as

Artificial intelligenceBack painCobb angleMagnetic resonance imagingScoliosisUK Biobank

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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.