ArticleDiagnostics (Basel, Switzerland)2022
Automated Cobb Angle Measurement for Adolescent Idiopathic Scoliosis Using Convolutional Neural Network.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning in Cobb angle automated measurement on X-rays: a systematic review and meta-analysis.Spine deformity · 2025Pooled it
- Accuracy of general-purpose large language models (LLMS) in interpreting adolescent idiopathic scoliosis radiographs: a comparative study with experienced spine surgeons.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
- Large language models with image processing in automated Cobb angle.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
- Development and Validation of a Multi-Modal Ensemble Model for Predicting Progression in Idiopathic Scoliosis.Global spine journal · 2026Article
- A Fully Automated Multistage Deep Learning System for Lenke Classification: Enhanced Diagnostic Precision in Adolescent Idiopathic Scoliosis.The Journal of bone and joint surgery. American volume · 2026Article
- Performance of a deep learning-based algorithm for automated measurements of Cobb angles on preoperative spine radiographs in adolescent idiopathic scoliosis.Pediatric radiology · 2026Article
- An end-to-end pipeline for automated scoliosis diagnosis with standardized clinical reporting using SNOMED CT.Scientific reports · 2025Article
- Convolutional Neural Network-Based Approach for Cobb Angle Measurement Using Mask R-CNN.Diagnostics (Basel, Switzerland) · 2025Article
- A deep learning model for radiological measurement of adolescent idiopathic scoliosis using biplanar radiographs.Journal of orthopaedic surgery and research · 2025Article
- Training for Radiographic Assessment of Scoliosis: A Pilot Educational Study.Journal of clinical medicine · 2025Article
- Deep learning automates Cobb angle measurement compared with multi-expert observers.BJR artificial intelligence · 2025Article
- Radiographic Analysis of Scoliosis Using Convolutional Neural Network in Clinical Practice.Journal of the Korean Society of Radiology · 2024Article
- Artificial intelligence in musculoskeletal imaging: realistic clinical applications in the next decade.Skeletal radiology · 2024Review
- Comparison of three artificial intelligence algorithms for automatic cobb angle measurement using teaching data specific to three disease groups.Scientific reports · 2024Article
- Article
- Fully Automated Measurement of Cobb Angles in Coronal Plane Spine Radiographs.Journal of clinical medicine · 2024Article
- The effect of spinal range of motion on functional balance, quality of life, and perception of appearance in adolescent idiopathic scoliosis after posterior spinal fusion surgery.Irish journal of medical science · 2024Article
- Automatic Lenke classification of adolescent idiopathic scoliosis with deep learning.JOR spine · 2024Article
- Multi-pose-based convolutional neural network model for diagnosis of patients with central lumbar spinal stenosis.Scientific reports · 2024Article
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4 authors.
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Abstract
The Cobb angle measurement of the scoliotic spine is prone to inter- and intra-observer variations in the clinical setting. This paper proposes a deep learning architecture for detecting spine vertebrae from X-ray images to evaluate the Cobb angle automatically. The public AASCE MICCAI 2019 anterior-posterior X-ray image dataset and local images were used to train and test the proposed convolutional neural network architecture. Sixty-eight landmark features of the spine were detected from the input image to obtain seventeen vertebrae on the spine. The vertebrae locations obtained were processed to automatically measure the Cobb angle. The proposed method can measure the Cobb angle with accuracies up to 93.6% and has excellent reliability compared to clinicians' measurement (intraclass correlation coefficient > 0.95). The proposed deep learning architecture may be used as a tool to augment Cobb angle measurement in X-ray images of patients with adolescent idiopathic scoliosis in a real-world clinical setting.
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