Surface Topography and Machine Learning: Strides Towards Radiation-Free Scoliosis Assessment: A Systematic Review
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Le résumé fourni par la source
BACKGROUND: Assessment of idiopathic scoliosis (IS) relies on radiographs and measurement of coronal curve magnitude, but repeated radiation exposure raises safety concerns in children. Noninvasive surface topography (ST) combined with machine learning (ML) has emerged as a potential radiation-free alternative for estimating curve severity, predicting coronal curve magnitude (Cobb angle), and monitoring longitudinally. This review aims to systematically review current evidence on the application of ML techniques to ST-based assessment of IS. METHODS: This systematic review was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. PubMed, Embase, Scopus, Cochrane Library, and Web of Science were searched from database inception through August 2025 for studies applying ML algorithms to noninvasive ST methods in IS. Extracted data included study design, patient characteristics, surface acquisition techniques, ML model architecture, validation strategies, and performance metrics. Methodological quality and risk of bias were assessed using the QUADAS-2 tool. RESULTS: Twelve studies met the inclusion criteria, encompassing 5015 patients and more than 6000 trunk surface scans. ST techniques included laser scanning, Moiré topography, raster stereography, depth sensors, and markerless 3D imaging. ML models demonstrated high accuracy in estimating curve severity and predicting coronal curve magnitude, with mean absolute errors of ∼3 to 6 degrees and sensitivities exceeding 0.9, approaching the variability of radiographic measurements. Curve-type classification showed lower and more heterogeneous performance, whereas binary classification models demonstrated strong potential for radiation-free screening and follow-up. CONCLUSIONS: ML applied to ST represents a promising noninvasive and radiation-free approach for IS assessment. Current evidence supports clinically meaningful performance in estimating curve severity and coronal curve magnitude, although limitations persist in classifying curve type. Larger prospective and multicenter studies are needed to validate these methods and define their role in clinical practice. LEVEL OF EVIDENCE: Level III.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Surface Topography and Machine Learning: Strides Towards Radiation-Free Scoliosis Assessment: A Systematic Review
- Date Crossref
- 01/09/2026
- Éditeur
- Ovid Technologies (Wolters Kluwer Health)
- 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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Università Campus Bio-Medico pays non établi dans la noticeUniversité ou école supérieure
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Università Campus Bio-Medico, Orthopaedic Center et Orthopaedic Surgery — Orthopaedic Research, avec 9 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.