SynthesisBiomedical engineering online2024
The application of machine learning methods for predicting the progression of adolescent idiopathic scoliosis: a systematic review.
Synthesis in Biomedical engineering online, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
12 citing papers in PubMed, 2 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 S1-LIV angle is essential for fusion selection in thoracic adolescent idiopathic scoliosis - a dual-center machine learning study with external validation.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
- 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
- Predicting postoperative coronal imbalance in Lenke 1/2 adolescent idiopathic scoliosis: A machine learning model with clinical interpretability.International orthopaedics · 2026Article
- Using Explainable Machine Learning to Identify Determinants of Spinal Deformities in Children: It's Not Only About What, but Also About How.Healthcare (Basel, Switzerland) · 2026Article
- Applications of Artificial Intelligence in Pediatric Orthopaedics.Indian journal of orthopaedics · 2026Review
- Machine learning-based prediction of pediatric scoliosis using foot pressure and body angles.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
- The role of artificial intelligence in advancing scoliosis care: a rapid review of current evidence and future opportunities.Frontiers in medicine · 2026Review
- The onset inflammatory parameters and the Cobb angle in adolescent idiopathic scoliosis: a case-control study.Croatian medical journal · 2025Article
- Development and Validation of Transformer- and Convolutional Neural Network-Based Deep Learning Models to Predict Curve Progression in Adolescent Idiopathic Scoliosis.Journal of clinical medicine · 2025Article
- Survey of evidence-based nursing practice readiness for scoliosis-specific rehabilitation exercise in orthopedic nurses.Frontiers in medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Predicting curve progression during the initial visit is pivotal in the disease management of patients with adolescent idiopathic scoliosis (AIS)-identifying patients at high risk of progression is essential for timely and proactive interventions. Both radiological and clinical factors have been investigated as predictors of curve progression. With the evolution of machine learning technologies, the integration of multidimensional information now enables precise predictions of curve progression. This review focuses on the application of machine learning methods to predict AIS curve progression, analyzing 15 selected studies that utilize various machine learning models and the risk factors employed for predictions. Key findings indicate that machine learning models can provide higher precision in predictions compared to traditional methods, and their implementation could lead to more personalized patient management. However, due to the model interpretability and data complexity, more comprehensive and multi-center studies are needed to transition from research to clinical practice.
Indexed as
Identifiers
What OpenQuestion holds
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.