Reviewnpj biomedical innovations2026
Advances in mechanical assessments of in vivo human lumbar spine tissues with noninvasive imaging techniques.
Review in npj biomedical innovations, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Radiomics in spinal research: a narrative review.Frontiers in bioengineering and biotechnology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Low back pain (LBP) is the leading cause of disability worldwide, yet clinical imaging remains largely limited to anatomical assessment, providing little insight into the spinal tissue mechanics underlying most idiopathic cases. This review highlights emerging noninvasive imaging technologies that enable in vivo quantification of intervertebral disc and spinal muscle mechanics, including radiography, ultrasound imaging, ultrasound elastography, magnetic resonance imaging, and magnetic resonance elastography. These approaches move beyond static morphology to capture spinal kinematics, load-dependent deformation, and tissue material properties under physiologically relevant conditions. Despite substantial technical progress, translation is hindered by inter-individual variability, limited symptomatic cohorts, and challenges in separating age-related changes from pathology. We discuss opportunities to accelerate clinical impact through development of normative mechanical datasets, dynamic and load-dependent imaging paradigms, and integration of imaging-derived mechanical biomarkers with computational modeling and machine learning. Together, these innovations position mechanics-based imaging to enable objective diagnosis, improved patient stratification, and mechanism-driven treatment of low back pain.
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Registered trials
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