ArticleEuropean spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society2025
Machine learning algorithms for predicting future curve using first and second visit data in female adolescent idiopathic scoliosis patients.
Article in 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, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prediction models for curve progression in adolescent idiopathic scoliosis: a systematic review with exploratory meta-analysis of discrimination.BMC musculoskeletal disorders · 2026Pooled it
- State-of-the-art review in AI and digital technologies for spine deformity.Spine deformity · 2026Review
- Development and Validation of a Multi-Modal Ensemble Model for Predicting Progression in Idiopathic Scoliosis.Global spine journal · 2026Article
- Management of adolescent scoliosis: a comprehensive review of etiology and rehabilitation.Frontiers in pediatrics · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
20 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThis study was designed to develop a machine learning (ML) model that predicts future Cobb angle in patients with adolescent idiopathic scoliosis (AIS) using minimal radiographs and simple questionnaires during the first and second visits.
methodsOur study focused on 887 female patients with AIS who were initially consulted at a specialized scoliosis center from July 2011 to February 2023. Patient data, including demographic and radiographic data based on anterior-posterior and lateral whole-spine radiographs, were collected at the first, second, and final visits. ML algorithms were employed to develop individual regression models for future Cobb angles of each curve type (proximal thoracic: PT, main thoracic: MT, and thoracolumbar/lumbar: TLL) using PyCaret in Python. Multiple models were explored and analyzed, with the selection of optimal models based on the coefficient of determination (R
resultsFor the future curve of PT, MT, and TLL, the top-performing models exhibit R
conclusionsThe ML-based model using items commonly evaluated at the first and second visits accurately predicted future Cobb angles in female patients with AIS.
Indexed as
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.