ArticleOrthopaedic surgery2024
Validation of Artificial Intelligence in the Classification of Adolescent Idiopathic Scoliosis and the Compairment to Clinical Manual Handling.
Article in Orthopaedic surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Fully Automated Deep Learning-Based Lenke Classification for Adolescent Idiopathic Scoliosis Using Multi-View Full-Spine Radiographs: Development and Clinical Validation.Global spine journal · 2026Article
- State-of-the-art review in AI and digital technologies for spine deformity.Spine deformity · 2026Review
- [Artificial intelligence in spinal surgery: current applications and future perspectives].Orthopadie (Heidelberg, Germany) · 2026Review
- Radiation-free 3D assessment of back height differences via three-dimensional depth sensing in adolescent idiopathic scoliosis: prospective, single-center, observational study.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
- Reliability of Large Language Model-Based Artificial Intelligence in AIS Assessment: Lenke Classification and Fusion-Level Suggestion.Diagnostics (Basel, Switzerland) · 2025Article
- Article
- 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
- 2D and 3D Classification Systems for Adolescent Idiopathic Scoliosis: Clinical Implications and Technological Advances.Orthopaedic surgery · 2025Review
- Deep learning based decision-making and outcome prediction for adolescent idiopathic scoliosis patients with posterior surgery.Scientific reports · 2025Article
- A 20-year research trend analysis of the artificial intelligence on scoliosis using bibliometric methods.Frontiers in pediatrics · 2025Review
- Management of adolescent scoliosis: a comprehensive review of etiology and rehabilitation.Frontiers in pediatrics · 2025Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
objectiveThe accurate measurement of Cobb angles is crucial for the effective clinical management of patients with adolescent idiopathic scoliosis (AIS). The Lenke classification system plays a pivotal role in determining the appropriate fusion levels for treatment planning. However, the presence of interobserver variability and time-intensive procedures presents challenges for clinicians. The purpose of this study is to compare the measurement accuracy of our developed artificial intelligence measurement system for Cobb angles and Lenke classification in AIS patients with manual measurements to validate its feasibility.
methodsAn artificial intelligence (AI) system measured the Cobb angle of AIS patients using convolutional neural networks, which identified the vertebral boundaries and sequences, recognized the upper and lower end vertebras, and estimated the Cobb angles of the proximal thoracic, main thoracic, and thoracolumbar/lumbar curves sequentially. Accordingly, the Lenke classifications of scoliosis were divided by oscillogram and defined by the AI system. Furthermore, a man-machine comparison (n = 300) was conducted for senior spine surgeons (n = 2), junior spine surgeons (n = 2), and the AI system for the image measurements of proximal thoracic (PT), main thoracic (MT), thoracolumbar/lumbar (TL/L), thoracic sagittal profile T5-T12, bending views PT, bending views MT, bending views TL/L, the Lenke classification system, the lumbar modifier, and sagittal thoracic alignment.
resultsIn the AI system, the calculation time for each patient's data was 0.2 s, while the measurement time for each surgeon was 23.6 min. The AI system showed high accuracy in the recognition of the Lenke classification and had high reliability compared to senior doctors (ICC 0.962).
conclusionThe AI system has high reliability for the Lenke classification and is a potential auxiliary tool for spinal surgeons.
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