ArticleSpine deformity2025
Smartphone-based surface topography app accurately detects clinically significant scoliosis.
Article in Spine deformity, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Reliability and validity of adolescent idiopathic scoliosis diagnosis based on mobile phone AI scoliosis screening algorithm and infrared 3D spine assessment system.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 · 2026Article
- Developmental Postural Modeling: A Historical and Conceptual Narrative Review of Posture Assessment.Journal of clinical medicine · 2026Review
- Radiation-free Cobb angle estimation in adolescent scoliosis using surface topography and a linear regression model.Spine deformity · 2026Article
- Article
- Three-Dimensional Surface Topography for the Assessment of Spinal Alignment: A Cross-Sectional Study of Biomechanical Correlates.Diagnostics (Basel, Switzerland) · 2026Article
- 3D topographic acquisitions to predict spinal curvature in adolescent idiopathic scoliosis : a prospective validation study.Bone & joint open · 2026Article
- Vertebra-Level Completeness Analysis in Thoracolumbar Ultrasound Using a YOLO-Based Detection Framework.Sensors (Basel, Switzerland) · 2026Article
- A Modified Dot-Pattern Moiré Fringe Topography Technique for Efficient Human Body Surface Analysis.Sensors (Basel, Switzerland) · 2026Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe purpose of this study was twofold: (1) to validate the predictive capabilities of the Scoliosis Assessment App using ST technology against X-ray "ground truth" in patients being evaluated for clinically significant scoliosis; and (2) to compare the diagnostic accuracy of the App versus the commonly used scoliometer tool.
methodsA multicenter, prospective validation study was conducted among patients with known or suspected scoliosis. The App determined an Asymmetry Index to predict the likelihood of clinically significant disease (MCM ≥ 20°) as determined by X-ray. Outcomes included the sensitivity, specificity, and area under the receiver operating characteristic curve (ROC AUC) associated with the Apps prediction of clinically significant disease.
resultsFifty-five patients were evaluated with a mean age of 13.6 ± 2.1 years. The App correctly classified 91% (50/55) of the patients compared to 69% (38/55) for the scoliometer. The sensitivity of the App was 96.4% (89.6-100% CI) versus 50% (28.1-71.9% CI) for the scoliometer (P < 0.05), while the specificity values were 85.2% (71.8-98.9% CI) and 88.9% (74.4-100% CI), respectively. ROC analysis indicated a statistically significant difference in accuracy (AUC) in favor of the App (95% versus 71%; P = 0.015).
conclusionThe Scoliosis Assessment App using ST technology offers an accurate, accessible, and non-ionizing method of detecting clinically significant scoliosis, suggesting that the App can be used for detection and monitoring as an alternative to radiography and as a replacement for scoliometer without diminishing the standard of care. Further studies are required to assess variations of sensitivity in a large cohort of patients and clinical utility as an alternative to radiographs.
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