ArticleBMC musculoskeletal disorders2022
A deep convolutional neural network to predict the curve progression of adolescent idiopathic scoliosis: a pilot study.
Article in BMC musculoskeletal disorders, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 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.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it, 36 citations in OpenAlex.
- Prediction models for curve progression in adolescent idiopathic scoliosis: a systematic review with exploratory meta-analysis of discrimination.BMC musculoskeletal disorders · 2026Pooled it
- The application of machine learning methods for predicting the progression of adolescent idiopathic scoliosis: a systematic review.Biomedical engineering online · 2024Pooled it
- Deep learning based vertebra localization and Cobb angle estimation using x-ray images.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
- 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
- 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
- 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
- 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
- Progression risk of adolescent idiopathic scoliosis based on SHAP-Explained machine learning models: a multicenter retrospective study.BMC musculoskeletal disorders · 2025Article
- Wearable Devices in Scoliosis Treatment: A Scoping Review of Innovations and Challenges.Bioengineering (Basel, Switzerland) · 2025Review
- AI and machine learning in paediatric spine deformity surgery.Bone & joint open · 2025Article
- Machine learning identifies clusters of the normal adolescent spine based on sagittal balance.Spine deformity · 2025Article
- Automated Distal Radius and Ulna Skeletal Maturity Grading from Hand Radiographs with an Attention Multi-Task Learning Method.Tomography (Ann Arbor, Mich.) · 2024Article
- Applications of artificial intelligence for adolescent idiopathic scoliosis: mapping the evidence.Spine deformity · 2024Review
- Fully Automated Measurement of Cobb Angles in Coronal Plane Spine Radiographs.Journal of clinical medicine · 2024Article
- Explainable Deep-Learning-Based Gait Analysis of Hip-Knee Cyclogram for the Prediction of Adolescent Idiopathic Scoliosis Progression.Sensors (Basel, Switzerland) · 2024Article
- 3D Back Contour Metrics in Predicting Idiopathic Scoliosis Progression: Retrospective Cohort Analysis, Case Series Report and Proof of Concept.Children (Basel, Switzerland) · 2024Article
- Article
- Artificial Intelligence in Scoliosis: Current Applications and Future Directions.Journal of clinical medicine · 2023Review
- Use of artificial neural networks in the prognosis of musculoskeletal diseases-a scoping review.BMC musculoskeletal disorders · 2023Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundAdolescent idiopathic scoliosis (AIS) is a three-dimensional spinal deformity that predominantly occurs in girls. While skeletal growth and maturation influence the development of AIS, accurate prediction of curve progression remains difficult because the prognosis for deformity differs among individuals. The purpose of this study is to develop a new diagnostic platform using a deep convolutional neural network (DCNN) that can predict the risk of scoliosis progression in patients with AIS.
methodsFifty-eight patients with AIS (49 females and 9 males; mean age: 12.5 ± 1.4 years) and a Cobb angle between 10 and 25 degrees (mean angle: 18.7 ± 4.5) were divided into two groups: those whose Cobb angle increased by more than 10 degrees within two years (progression group, 28 patients) and those whose Cobb angle changed by less than 5 degrees (non-progression group, 30 patients). The X-ray images of three regions of interest (ROIs) (lung [ROI1], abdomen [ROI2], and total spine [ROI3]), were used as the source data for learning and prediction. Five spine surgeons also predicted the progression of scoliosis by reading the X-rays in a blinded manner.
resultsThe prediction performance of the DCNN for AIS curve progression showed an accuracy of 69% and an area under the receiver-operating characteristic curve of 0.70 using ROI3 images, whereas the diagnostic performance of the spine surgeons showed inferior at 47%. Transfer learning with a pretrained DCNN contributed to improved prediction accuracy.
conclusionOur developed method to predict the risk of scoliosis progression in AIS by using a DCNN could be a valuable tool in decision-making for therapeutic interventions for AIS.
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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.