ArticleGlobal spine journal2024
Novel AI-Based Algorithm for the Automated Computation of Coronal Parameters in Adolescent Idiopathic Scoliosis Patients: A Validation Study on 100 Preoperative Full Spine X-Rays.
Article in Global spine journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 3 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- A comprehensive narrative review of scoliosis in children and adolescents: epidemiology, screening strategies, therapeutic interventions, and biomechanical research.Frontiers in pediatrics · 2026Pooled it
- Artificial intelligence automated measurements of spinopelvic parameters in adult spinal deformity-a systematic review.Spine deformity · 2025Pooled it
- Deep learning in Cobb angle automated measurement on X-rays: a systematic review and meta-analysis.Spine deformity · 2025Pooled it
- Can General-purpose Multimodal AI Reliably Measure Cobb Angles? A Head-to-head Diagnostic Agreement Study of GPT-5.1 and Gemini 3.1 Pro in Adolescent Idiopathic Scoliosis.Journal of imaging informatics in medicine · 2026Article
- State-of-the-art review in AI and digital technologies for spine deformity.Spine deformity · 2026Review
- From Algorithms to Clinics: Recent Progress in AI for Scoliosis Diagnosis and Management.Journal of clinical medicine · 2026Review
- Automated quantification of coronal balance in spinal deformity: a safety-aware clinical workflow.Scientific reports · 2026Article
- Automated Measurement of Occipito-Axial Angle on Cervical Radiographs Using a Deep Learning Object Detection Model: A Proof-of-Concept Study.Neurospine · 2026Article
- Article
- Deep learning-based estimation of sagittal spinal alignment from coronal radiographs.Frontiers in bioengineering and biotechnology · 2026Article
- A novel artificial Intelligence-Based model for automated Lenke classification in 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 · 2025Article
- Deep learning algorithm enables automated Cobb angle measurements with high accuracy.Skeletal radiology · 2025Article
- Intelligent measurement of adolescent idiopathic scoliosis x-ray coronal imaging parameters based on VB-Net neural network: a retrospective analysis of 2092 cases.Journal of orthopaedic surgery and research · 2025Article
- Current issues in the treatment of adolescent idiopathic scoliosis: a comprehensive narrative review.Asian spine journal · 2024Article
- Fully Automated Measurement of Cobb Angles in Coronal Plane Spine Radiographs.Journal of clinical medicine · 2024Article
- Does radiographic calibration affect linear radiographic measurements in a large pediatric spine registry?Spine deformity · 2024Article
- Unsupervised local center of mass based scoliosis spinal segmentation and Cobb angle measurement.PloS one · 2024Article
- Application of machine learning standardized integral area algorithm in measuring the scoliosis.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
STUDY
designRetrospective, mono-centric cohort research study.
objectivesThe purpose of this study is to validate a novel artificial intelligence (AI)-based algorithm against human-generated ground truth for radiographic parameters of adolescent idiopathic scoliosis (AIS).
methodsAn AI-algorithm was developed that is capable of detecting anatomical structures of interest (clavicles, cervical, thoracic, lumbar spine and sacrum) and calculate essential radiographic parameters in AP spine X-rays fully automatically. The evaluated parameters included T1-tilt, clavicle angle (CA), coronal balance (CB), lumbar modifier, and Cobb angles in the proximal thoracic (C-PT), thoracic, and thoracolumbar regions. Measurements from 2 experienced physicians on 100 preoperative AP full spine X-rays of AIS patients were used as ground truth and to evaluate inter-rater and intra-rater reliability. The agreement between human raters and AI was compared by means of single measure Intra-class Correlation Coefficients (ICC; absolute agreement; >.75 rated as excellent), mean error and additional statistical metrics.
resultsThe comparison between human raters resulted in excellent ICC values for intra- (range: .97-1) and inter-rater (.85-.99) reliability. The algorithm was able to determine all parameters in 100% of images with excellent ICC values (.78-.98). Consistently with the human raters, ICC values were typically smallest for C-PT (eg, rater 1A vs AI: .78, mean error: 4.7°) and largest for CB (.96, -.5 mm) as well as CA (.98, .2°).
conclusionsThe AI-algorithm shows excellent reliability and agreement with human raters for coronal parameters in preoperative full spine images. The reliability and speed offered by the AI-algorithm could contribute to the efficient analysis of large datasets (eg, registry studies) and measurements in clinical practice.
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