ArticlePloS one2022
Predicting curve progression for adolescent idiopathic scoliosis using random forest model.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 3 of them syntheses that pooled it.
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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
22 citing papers in PubMed, 3 syntheses or guidelines 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
- A comprehensive narrative review of scoliosis in children and adolescents: epidemiology, screening strategies, therapeutic interventions, and biomechanical research.Frontiers in pediatrics · 2026Pooled it
- The application of machine learning methods for predicting the progression of adolescent idiopathic scoliosis: a systematic review.Biomedical engineering online · 2024Pooled it
- State-of-the-art review in AI and digital technologies for spine deformity.Spine deformity · 2026Review
- Principles Underlying Surgical Decision-Making in Lowest-Instrumented Vertebra Level Selection of Adolescent Idiopathic Scoliosis: From Traditional Landmarks to Emerging Predictive Models-A Narrative Review.Journal of clinical medicine · 2026Review
- Radiation-free Cobb angle estimation in adolescent scoliosis using surface topography and a linear regression model.Spine deformity · 2026Article
- The prognostic value of preoperative pulmonary function tests in adolescent idiopathic scoliosis surgery.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 · 2026Observational
- Prediction of Cobb angle progression in female patients with mild-to-moderate adolescent idiopathic scoliosis.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
- AI in Pediatric Spine Care: Clinical, Research, and Ethical Considerations.Journal of clinical medicine · 2025Article
- Increased differential rod contouring reduces the effectiveness of surgical correction in patients with adolescent idiopathic scoliosis.Spine deformity · 2025Article
- Machine learning algorithms for predicting future curve using first and second visit data in female adolescent idiopathic scoliosis patients.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 · 2025Article
- Idiopathic Scoliosis Progression: Presenting Rib and Segmental Rib Index as Predictors-A Literature Review.Medical sciences (Basel, Switzerland) · 2025Review
- Predicting pediatric patient rehabilitation outcomes after spinal deformity surgery with artificial intelligence.Communications medicine · 2025Article
- Predicting pulmonary function using thoracic deformity parameters in early onset scoliosis patients.PloS one · 2025Article
- Segmental deformity markers offer novel indicators of deformity progression risk in deformity-matched adolescent idiopathic scoliosis patients.Spine deformity · 2024Article
- Applications of artificial intelligence for adolescent idiopathic scoliosis: mapping the evidence.Spine deformity · 2024Review
- Prediction of adolescent idiopathic scoliosis with machine learning algorithms using brain volumetric measurements.JOR spine · 2024Article
- 3D Back Contour Metrics in Predicting Idiopathic Scoliosis Progression: Retrospective Cohort Analysis, Case Series Report and Proof of Concept.Children (Basel, Switzerland) · 2024Article
- Predicting radiographic outcomes of vertebral body tethering in adolescent idiopathic scoliosis patients using machine learning.PloS one · 2024Article
- Menarche in Scoliotic and Non-Scoliotic Balkan Girls and the Relationship between Menarche and the Laterality of Scoliotic Curves.Journal of clinical medicine · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAdolescent Idiopathic Scoliosis (AIS) is a three-dimensional (3D) spinal deformity characterized by coronal curvature and rotational deformity. Predicting curve progression is important for the selection and timing of treatment. Although there is a consensus in the literature regarding prognostic factors associated with curve progression, the order of importance, as well as the combination of factors that are most predictive of curve progression is unknown.
objectives(1) create an ordered list of prognostic factors that most contribute to curve progression, and (2) develop and validate a Machine Learning (ML) model to predict the final major Cobb angle in AIS patients.
methods193 AIS patients were selected for the current study. Preoperative PA, lateral and lateral bending radiographs were retrospectively obtained from the Shriners Hospitals for Children. Demographic and radiographic features, previously reported to be associated with curve progression, were collected. Sequential Backward Floating Selection (SBFS) was used to select a subset of the most predictive features. Based on the performance of several machine learning methods, a Random Forest (RF) regressor model was used to provide the importance rank of prognostic features and to predict the final major Cobb angle.
resultsThe seven most predictive prognostic features in the order of importance were initial major Cobb angle, flexibility, initial lumbar lordosis angle, initial thoracic kyphosis angle, age at last visit, number of levels involved, and Risser "+" stage at the first visit. The RF model predicted the final major Cobb angle with a Mean Absolute Error (MAE) of 4.64 degrees.
conclusionA RF model was developed and validated to identify the most important prognostic features for curve progression and predict the final major Cobb angle. It is possible to predict the final major Cobb angle value within 5 degrees error from 2D radiographic features. Such methods could be directly applied to guide intervention timing and optimization for AIS treatment.
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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.