Artificial intelligence-driven 3D surface-topography app for screening and monitoring adolescent scoliosis: early results from a single institution
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Le résumé fourni par la source
PURPOSE: Radiation-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. METHODS: In 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°). RESULTS: Across 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. CONCLUSION: This 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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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Artificial intelligence-driven 3D surface-topography app for screening and monitoring adolescent scoliosis: early results from a single institution
- Date Crossref
- 30/01/2026
- Éditeur
- Springer Science and Business Media LLC
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Centre Hospitalier Universitaire Sainte-Justine pays non établi dans la noticeÉtablissement de santé
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Université de Montréal pays non établi dans la noticeUniversité ou école supérieure
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Université de Sherbrooke Department of Community Health Sciences pays non établi dans la noticeUniversité ou école supérieure
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Centre de Recherche Azrieli du CHU Sainte-Justine Department of Surgery pays non établi dans la noticeStructure de recherche
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University of Montréal Department of Surgery pays non établi dans la noticeUniversité ou école supérieure
Centre Hospitalier Universitaire Sainte-Justine, Université de Montréal et Department of Community Health Sciences — Université de Sherbrooke, avec 2 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.