Observational studySpine deformity2026
Artificial intelligence-driven 3D surface-topography app for screening and monitoring adolescent scoliosis: early results from a single institution.
Observational study in Spine deformity, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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Who cites it
2 citing papers in PubMed.
- Developmental Postural Modeling: A Historical and Conceptual Narrative Review of Posture Assessment.Journal of clinical medicine · 2026Review
- Comment on "Artificial intelligence-driven 3D surface-topography app for screening and monitoring adolescent scoliosis: early results from a single institution".Spine deformity · 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
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeRadiation-free tools, such as scoliometers, ultrasound, and Moiré topography, have been explored for monitoring Adolescent Idiopathic Scoliosis (AIS), but none have replaced the need for serial spinal radiographs. This study aimed to evaluate the accuracy and criterion validity of a new AI-powered digital health application using 3D surface topography to predict Cobb angles, with the goal of reducing radiation exposure and enabling home-based curve monitoring.
methodsIn a single-center observational study, 125 patients with confirmed or suspected scoliosis underwent smartphone-based 3D surface-topography scans in standing and forward-bending positions. Poor-quality scans (n = 20) were excluded. Radiographic Cobb angles were used as the gold standard. After random allocation, 79 scans formed the training set and 26 formed the validation set; external data (142 controls, 188 scoliosis patients) were added to strengthen training, and 25 controls were added to the test set. Accuracy was expressed as mean absolute error (MAE) and correlation with radiographs. Criterion validity was assessed by sensitivity, specificity, and AUC at clinically meaningful thresholds (10°, 25°, and 40°).
resultsAcross 51 test scans (AIS + controls), the algorithm showed a strong correlation with radiographs (r = 0.922, 95% CI 0.866-0.955) and an MAE of 5.9° (95% CI 4.5-7.3). In AIS-only curves of 10-50°, the MAE was 6.4° (95% CI 4.4-8.3). At 10°, sensitivity was 0.962, specificity was 0.960, and AUC was 0.978. At 25°, sensitivity was 0.706, specificity was 0.853, and AUC was 0.917. At 40°, sensitivity was 0.667, specificity was 1.000, and AUC was 1.000.
conclusionThis AI-driven 3D surface topography demonstrated high validity for non-radiographic Cobb angle prediction, particularly in mild-to-moderate AIS. It supports safer, more frequent, home-based monitoring, though refinements are needed for severe curves and patients with a higher BMI.
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